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Block matching algorithm is solved the correspondence problem occurred in comparing stereo images (left and right sensors... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_75726415" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">This paper presents an obstacle detection and avoidance of mobile robot using stereo camera for indoor environment. Block matching algorithm is solved the correspondence problem occurred in comparing stereo images (left and right sensors of the camera). The algorithm uses Sum of Absolute Differences (SAD). Left image works as a reference block to the right image and the output is disparity mapping or depth maps with the left coordinate system. A pair of camera or stereo vision baseline is based on horizontal configuration. The block matching technique is briefly described with the performance of its output. The curve fitting tool would determine the range of each obstacle detected in disparity mapping. The programming activities are using Matlab software starting from capturing images until navigation of mobile robot.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/75726415" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="155540181" href="https://independent.academia.edu/SyedHamid16">Syed Hamid</a><script data-card-contents-for-user="155540181" type="text/json">{"id":155540181,"first_name":"Syed","last_name":"Hamid","domain_name":"independent","page_name":"SyedHamid16","display_name":"Syed Hamid","profile_url":"https://independent.academia.edu/SyedHamid16?f_ri=3413","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_75726415 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="75726415"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 75726415, container: ".js-paper-rank-work_75726415", }); 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Block matching algorithm is solved the correspondence problem occurred in comparing stereo images (left and right sensors of the camera). The algorithm uses Sum of Absolute Differences (SAD). Left image works as a reference block to the right image and the output is disparity mapping or depth maps with the left coordinate system. A pair of camera or stereo vision baseline is based on horizontal configuration. The block matching technique is briefly described with the performance of its output. The curve fitting tool would determine the range of each obstacle detected in disparity mapping. 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surgical instruments in robotized laparoscopic surgery using visual servoing</a></div></div><div class="u-pb4x u-mt3x"></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/33847879" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="95246b43e8f75ec4931537c6be606b8b" rel="nofollow" data-download="{"attachment_id":53828739,"asset_id":33847879,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" 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u-positionRelative">13</a>  </div><span class="InlineList-item-text u-textTruncate u-pl10x"><a class="InlineList-item-text" data-has-card-for-ri="60" href="https://www.academia.edu/Documents/in/Mechanical_Engineering">Mechanical Engineering</a>, <script data-card-contents-for-ri="60" type="text/json">{"id":60,"name":"Mechanical Engineering","url":"https://www.academia.edu/Documents/in/Mechanical_Engineering?f_ri=3413","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="77" href="https://www.academia.edu/Documents/in/Robotics">Robotics</a>, <script data-card-contents-for-ri="77" type="text/json">{"id":77,"name":"Robotics","url":"https://www.academia.edu/Documents/in/Robotics?f_ri=3413","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="3413" href="https://www.academia.edu/Documents/in/Robot_Vision">Robot Vision</a>, <script data-card-contents-for-ri="3413" type="text/json">{"id":3413,"name":"Robot 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Invasive Surgery","url":"https://www.academia.edu/Documents/in/Minimal_Invasive_Surgery?f_ri=3413"},{"id":2032079,"name":"Relative Position","url":"https://www.academia.edu/Documents/in/Relative_Position?f_ri=3413"},{"id":2177422,"name":"Light Emitting Diode","url":"https://www.academia.edu/Documents/in/Light_Emitting_Diode?f_ri=3413"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_8553666" data-work_id="8553666" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/8553666/Reconocimiento_de_Posturas_en_secuencias_de_video_usando_firma_Punto_Contorno">Reconocimiento de Posturas en secuencias de video usando firma Punto-Contorno</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">A research area in Computer Vision focuses on the identification of articulated objects, such as human actions and movements of the hand, which can be used in human-computer interaction, surveillance, and other tracking systems. Two... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_8553666" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">A research area in Computer Vision focuses on the identification of articulated objects, such as human actions and movements of the hand, which can be used in human-computer interaction, surveillance, and other tracking systems. Two problems arise: identify when two articulated objects in different stances are in the same class of objects, and differentiate the distinct positions of the same object. In both cases, it is necessary to know how correspond the different points or regions of such objects standing in different attitudes. This article presents the Contour-Point Signature; a point descriptor that allows to establish a method to achieve the better matching of points between two figures, and to thus obtain a transformation which relates them. With this descriptor, we can achieve more accurate shape features and implement more efficient retrieval under multi-resolution. In addition, CPS is robust to rigid translation, scaling, rotation and independent of the origin point. A measure of dissimilarity between two figures for classifying various human postures in a video sequence is also presented.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/8553666" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="9af4b65be618513cca8c7d1bdb69bd03" rel="nofollow" data-download="{"attachment_id":34927014,"asset_id":8553666,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/34927014/download_file?st=MTczMjQxMTY2OCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="2488929" href="https://una.academia.edu/wvenialbo">Waldemar Villamayor-Venialbo</a><script data-card-contents-for-user="2488929" type="text/json">{"id":2488929,"first_name":"Waldemar","last_name":"Villamayor-Venialbo","domain_name":"una","page_name":"wvenialbo","display_name":"Waldemar Villamayor-Venialbo","profile_url":"https://una.academia.edu/wvenialbo?f_ri=3413","photo":"https://0.academia-photos.com/2488929/3063966/135965852/s65_waldemar.villamayor-venialbo.jpg"}</script></span></span></li><li class="js-paper-rank-work_8553666 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="8553666"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 8553666, container: ".js-paper-rank-work_8553666", }); 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$(".js-view-count[data-work-id=8553666]").text(description); $(".js-view-count-work_8553666").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_8553666").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="8553666"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">39</a>  </div><span class="InlineList-item-text u-textTruncate u-pl10x"><a class="InlineList-item-text" data-has-card-for-ri="854" href="https://www.academia.edu/Documents/in/Computer_Vision">Computer Vision</a>, <script data-card-contents-for-ri="854" type="text/json">{"id":854,"name":"Computer Vision","url":"https://www.academia.edu/Documents/in/Computer_Vision?f_ri=3413","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="3413" href="https://www.academia.edu/Documents/in/Robot_Vision">Robot Vision</a>, <script data-card-contents-for-ri="3413" type="text/json">{"id":3413,"name":"Robot Vision","url":"https://www.academia.edu/Documents/in/Robot_Vision?f_ri=3413","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="7728" href="https://www.academia.edu/Documents/in/Object_Recognition_Computer_Vision_">Object Recognition (Computer Vision)</a>, <script data-card-contents-for-ri="7728" type="text/json">{"id":7728,"name":"Object Recognition (Computer Vision)","url":"https://www.academia.edu/Documents/in/Object_Recognition_Computer_Vision_?f_ri=3413","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="7937" href="https://www.academia.edu/Documents/in/Image_Recognition_Computer_Vision_">Image Recognition (Computer Vision)</a><script data-card-contents-for-ri="7937" type="text/json">{"id":7937,"name":"Image Recognition (Computer Vision)","url":"https://www.academia.edu/Documents/in/Image_Recognition_Computer_Vision_?f_ri=3413","nofollow":false}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=8553666]'), work: {"id":8553666,"title":"Reconocimiento de Posturas en secuencias de video usando firma Punto-Contorno","created_at":"2014-09-29T11:33:44.902-07:00","url":"https://www.academia.edu/8553666/Reconocimiento_de_Posturas_en_secuencias_de_video_usando_firma_Punto_Contorno?f_ri=3413","dom_id":"work_8553666","summary":"A research area in Computer Vision focuses on the identification of articulated objects, such as human actions and movements of the hand, which can be used in human-computer interaction, surveillance, and other tracking systems. 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href="https://www.academia.edu/Documents/in/Parallel_Mechanism">Parallel Mechanism</a>, <script data-card-contents-for-ri="223470" type="text/json">{"id":223470,"name":"Parallel Mechanism","url":"https://www.academia.edu/Documents/in/Parallel_Mechanism?f_ri=3413","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="297691" href="https://www.academia.edu/Documents/in/High_performance">High performance</a><script data-card-contents-for-ri="297691" type="text/json">{"id":297691,"name":"High performance","url":"https://www.academia.edu/Documents/in/High_performance?f_ri=3413","nofollow":false}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=28845879]'), work: {"id":28845879,"title":"A Novel Mechanism for Stereo Active 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Vision","url":"https://www.academia.edu/Documents/in/Robotic_Vision?f_ri=3413"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_10269937" data-work_id="10269937" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/10269937/Uncalibrated_Visual_Servo_control_with_multi_constraint_satisfaction">Uncalibrated Visual Servo control with multi-constraint satisfaction</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">This paper devises a new multicriteria image based controller for the control of six degrees of freedom (PUMA560) robotic arm, based upon Linear Matrix Inequality (LMI). The aim lies in developing such a method that neither involves... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_10269937" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">This paper devises a new multicriteria image based controller for the control of six degrees of freedom (PUMA560) robotic arm, based upon Linear Matrix Inequality (LMI). The aim lies in developing such a method that neither involves camera calibration parameters nor inverse kinematics. The approach adopted in this paper includes transpose Jacobian control; thus, inverse of the Jacobian matrix is no longer required. The proposed controller allows stabilizing the camera despite the unknown value of the target point depth. To make sure that the features remain in the camera field of view, and to restrict the controller's input using some bounds, visibility and kinematic constraints are introduced in the form of LMIs. By invoking the Lyapunov's direct method, closed loop stability of the system is ensured. Simulation results are shown for three different cases, which exhibit the system stability and convergence even in the presence of large errors, and present the comparative analysis of both i.e., systems with and without visibility and kinematic constraints.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/10269937" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="b83ee3dcd4f2430a94d27e80ea287271" rel="nofollow" data-download="{"attachment_id":47461292,"asset_id":10269937,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/47461292/download_file?st=MTczMjQxMTY2OCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="25148609" href="https://unversitycentralpunjablahore.academia.edu/AbrarAhmed">Abrar Ahmed</a><script data-card-contents-for-user="25148609" type="text/json">{"id":25148609,"first_name":"Abrar","last_name":"Ahmed","domain_name":"unversitycentralpunjablahore","page_name":"AbrarAhmed","display_name":"Abrar Ahmed","profile_url":"https://unversitycentralpunjablahore.academia.edu/AbrarAhmed?f_ri=3413","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_10269937 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="10269937"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 10269937, container: ".js-paper-rank-work_10269937", }); 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The aim lies in developing such a method that neither involves camera calibration parameters nor inverse kinematics. The approach adopted in this paper includes transpose Jacobian control; thus, inverse of the Jacobian matrix is no longer required. The proposed controller allows stabilizing the camera despite the unknown value of the target point depth. To make sure that the features remain in the camera field of view, and to restrict the controller's input using some bounds, visibility and kinematic constraints are introduced in the form of LMIs. By invoking the Lyapunov's direct method, closed loop stability of the system is ensured. Simulation results are shown for three different cases, which exhibit the system stability and convergence even in the presence of large errors, and present the comparative analysis of both i.e., systems with and without visibility and kinematic constraints.","downloadable_attachments":[{"id":47461292,"asset_id":10269937,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":25148609,"first_name":"Abrar","last_name":"Ahmed","domain_name":"unversitycentralpunjablahore","page_name":"AbrarAhmed","display_name":"Abrar Ahmed","profile_url":"https://unversitycentralpunjablahore.academia.edu/AbrarAhmed?f_ri=3413","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":3413,"name":"Robot Vision","url":"https://www.academia.edu/Documents/in/Robot_Vision?f_ri=3413","nofollow":false},{"id":819765,"name":"Constraint Satisfaction","url":"https://www.academia.edu/Documents/in/Constraint_Satisfaction?f_ri=3413","nofollow":false}]}, }) } 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kinematics","url":"https://www.academia.edu/Documents/in/Robot_kinematics?f_ri=3413"},{"id":95162,"name":"Grammar","url":"https://www.academia.edu/Documents/in/Grammar?f_ri=3413"},{"id":97585,"name":"User interfaces","url":"https://www.academia.edu/Documents/in/User_interfaces?f_ri=3413"},{"id":99861,"name":"ROBOT","url":"https://www.academia.edu/Documents/in/ROBOT?f_ri=3413"},{"id":188197,"name":"Industrial Robots","url":"https://www.academia.edu/Documents/in/Industrial_Robots?f_ri=3413"},{"id":550905,"name":"Robot programming","url":"https://www.academia.edu/Documents/in/Robot_programming?f_ri=3413"},{"id":757194,"name":"Programming Paradigm","url":"https://www.academia.edu/Documents/in/Programming_Paradigm?f_ri=3413"},{"id":1461519,"name":"Robotics Automation","url":"https://www.academia.edu/Documents/in/Robotics_Automation?f_ri=3413"},{"id":1751583,"name":"Product Line","url":"https://www.academia.edu/Documents/in/Product_Line?f_ri=3413"},{"id":4040883,"name":"service robot","url":"https://www.academia.edu/Documents/in/service_robot?f_ri=3413"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_48796383" data-work_id="48796383" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/48796383/Stereographic_projection_for_industrial_manipulator_tasks_Theory_and_experiments">Stereographic projection for industrial manipulator tasks: Theory and experiments</a></div></div><div class="u-pb4x u-mt3x"></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/48796383" data-share-source="work_strip" 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var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=48796383]").text(description); $(".js-view-count-work_48796383").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_48796383").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="48796383"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">8</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="3413" href="https://www.academia.edu/Documents/in/Robot_Vision">Robot Vision</a>, <script data-card-contents-for-ri="3413" type="text/json">{"id":3413,"name":"Robot Vision","url":"https://www.academia.edu/Documents/in/Robot_Vision?f_ri=3413","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="129253" href="https://www.academia.edu/Documents/in/Real_Time_Control">Real Time Control</a>, <script data-card-contents-for-ri="129253" type="text/json">{"id":129253,"name":"Real Time Control","url":"https://www.academia.edu/Documents/in/Real_Time_Control?f_ri=3413","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="168144" href="https://www.academia.edu/Documents/in/Redundancy">Redundancy</a>, <script data-card-contents-for-ri="168144" type="text/json">{"id":168144,"name":"Redundancy","url":"https://www.academia.edu/Documents/in/Redundancy?f_ri=3413","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="188197" href="https://www.academia.edu/Documents/in/Industrial_Robots">Industrial Robots</a><script data-card-contents-for-ri="188197" 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Pettersen","profile_url":"https://independent.academia.edu/KristinPettersen?f_ri=3413","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":3413,"name":"Robot Vision","url":"https://www.academia.edu/Documents/in/Robot_Vision?f_ri=3413","nofollow":false},{"id":129253,"name":"Real Time Control","url":"https://www.academia.edu/Documents/in/Real_Time_Control?f_ri=3413","nofollow":false},{"id":168144,"name":"Redundancy","url":"https://www.academia.edu/Documents/in/Redundancy?f_ri=3413","nofollow":false},{"id":188197,"name":"Industrial Robots","url":"https://www.academia.edu/Documents/in/Industrial_Robots?f_ri=3413","nofollow":false},{"id":308126,"name":"Stereographic Projection","url":"https://www.academia.edu/Documents/in/Stereographic_Projection?f_ri=3413"},{"id":778981,"name":"Experimental Validation","url":"https://www.academia.edu/Documents/in/Experimental_Validation?f_ri=3413"},{"id":983554,"name":"Robot Manipulator","url":"https://www.academia.edu/Documents/in/Robot_Manipulator?f_ri=3413"},{"id":1142923,"name":"End Effectors","url":"https://www.academia.edu/Documents/in/End_Effectors?f_ri=3413"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_29069797 coauthored" data-work_id="29069797" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/29069797/Next_Best_Stereo_Extending_Next_Best_View_Optimisation_For_Collaborative_Sensors">Next-Best Stereo: Extending Next-Best View Optimisation For Collaborative Sensors</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Most 3D reconstruction approaches passively optimise over all data, exhaustively matching pairs, rather than actively selecting data to process. This is costly both in terms of time and computer resources, and quickly becomes intractable... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_29069797" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Most 3D reconstruction approaches passively optimise over all data, exhaustively matching pairs, rather than actively selecting data to process. This is costly both in terms of time and computer resources, and quickly becomes intractable for large datasets. This work proposes an approach to intelligently filter large amounts of data for 3D reconstructions of unknown scenes using monocular cameras. Our contributions are twofold: First, we present a novel approach to efficiently optimise the Next-Best View (NBV) in terms of accuracy and coverage using partial scene geometry. Second, we extend this to intelligently selecting stereo pairs by jointly optimising the baseline and vergence to find the NBV's best stereo pair to perform reconstruction. Both contributions are extremely efficient, taking 0.8ms and 0.3ms per pose, respectively. Experimental evaluation shows that the proposed method allows efficient selection of stereo pairs for reconstruction, such that a dense model can be obtained with only a small number of images. Once a complete model has been obtained, the remaining computational budget is used to intelligently refine areas of uncertainty, achieving results comparable to state-of-the-art batch approaches on the Middlebury dataset, using as little as 3.8% of the views.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/29069797" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="af971d379f51b68078c4a7f1bd90945a" rel="nofollow" data-download="{"attachment_id":49522838,"asset_id":29069797,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/49522838/download_file?st=MTczMjQxMTY2OCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="54838585" href="https://surrey.academia.edu/OscarMendez">Oscar Mendez</a><script data-card-contents-for-user="54838585" type="text/json">{"id":54838585,"first_name":"Oscar","last_name":"Mendez","domain_name":"surrey","page_name":"OscarMendez","display_name":"Oscar Mendez","profile_url":"https://surrey.academia.edu/OscarMendez?f_ri=3413","photo":"https://0.academia-photos.com/54838585/14458820/19643424/s65_oscar.mendez.jpg"}</script></span></span><span class="u-displayInlineBlock InlineList-item-text"> and <span class="u-textDecorationUnderline u-clickable InlineList-item-text js-work-more-authors-29069797">+2</span><div class="hidden js-additional-users-29069797"><div><span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a href="https://surrey.academia.edu/SimonHadfield">Simon Hadfield</a></span></div><div><span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a href="https://surrey.academia.edu/RichardBowden">Richard Bowden</a></span></div></div></span><script>(function(){ var popoverSettings = { el: $('.js-work-more-authors-29069797'), placement: 'bottom', hide_delay: 200, html: true, content: function(){ return $('.js-additional-users-29069797').html(); 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container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_29069797 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="29069797"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 29069797; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=29069797]").text(description); $(".js-view-count-work_29069797").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_29069797").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="29069797"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">4</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="77" href="https://www.academia.edu/Documents/in/Robotics">Robotics</a>, <script data-card-contents-for-ri="77" type="text/json">{"id":77,"name":"Robotics","url":"https://www.academia.edu/Documents/in/Robotics?f_ri=3413","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="854" href="https://www.academia.edu/Documents/in/Computer_Vision">Computer Vision</a>, <script data-card-contents-for-ri="854" type="text/json">{"id":854,"name":"Computer Vision","url":"https://www.academia.edu/Documents/in/Computer_Vision?f_ri=3413","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="3413" href="https://www.academia.edu/Documents/in/Robot_Vision">Robot Vision</a>, <script data-card-contents-for-ri="3413" type="text/json">{"id":3413,"name":"Robot Vision","url":"https://www.academia.edu/Documents/in/Robot_Vision?f_ri=3413","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="15157" href="https://www.academia.edu/Documents/in/3D_Reconstruction">3D Reconstruction</a><script data-card-contents-for-ri="15157" type="text/json">{"id":15157,"name":"3D Reconstruction","url":"https://www.academia.edu/Documents/in/3D_Reconstruction?f_ri=3413","nofollow":false}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=29069797]'), work: {"id":29069797,"title":"Next-Best Stereo: Extending Next-Best View Optimisation For Collaborative Sensors","created_at":"2016-10-11T06:10:54.629-07:00","url":"https://www.academia.edu/29069797/Next_Best_Stereo_Extending_Next_Best_View_Optimisation_For_Collaborative_Sensors?f_ri=3413","dom_id":"work_29069797","summary":"Most 3D reconstruction approaches passively optimise over all data, exhaustively matching pairs, rather than actively selecting data to process. This is costly both in terms of time and computer resources, and quickly becomes intractable for large datasets. This work proposes an approach to intelligently filter large amounts of data for 3D reconstructions of unknown scenes using monocular cameras. Our contributions are twofold: First, we present a novel approach to efficiently optimise the Next-Best View (NBV) in terms of accuracy and coverage using partial scene geometry. Second, we extend this to intelligently selecting stereo pairs by jointly optimising the baseline and vergence to find the NBV's best stereo pair to perform reconstruction. Both contributions are extremely efficient, taking 0.8ms and 0.3ms per pose, respectively. Experimental evaluation shows that the proposed method allows efficient selection of stereo pairs for reconstruction, such that a dense model can be obtained with only a small number of images. Once a complete model has been obtained, the remaining computational budget is used to intelligently refine areas of uncertainty, achieving results comparable to state-of-the-art batch approaches on the Middlebury dataset, using as little as 3.8% of the views.","downloadable_attachments":[{"id":49522838,"asset_id":29069797,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":54838585,"first_name":"Oscar","last_name":"Mendez","domain_name":"surrey","page_name":"OscarMendez","display_name":"Oscar Mendez","profile_url":"https://surrey.academia.edu/OscarMendez?f_ri=3413","photo":"https://0.academia-photos.com/54838585/14458820/19643424/s65_oscar.mendez.jpg"},{"id":1185658,"first_name":"Simon","last_name":"Hadfield","domain_name":"surrey","page_name":"SimonHadfield","display_name":"Simon Hadfield","profile_url":"https://surrey.academia.edu/SimonHadfield?f_ri=3413","photo":"https://0.academia-photos.com/1185658/1588468/20106672/s65_simon.hadfield.jpg"},{"id":5692,"first_name":"Richard","last_name":"Bowden","domain_name":"surrey","page_name":"RichardBowden","display_name":"Richard Bowden","profile_url":"https://surrey.academia.edu/RichardBowden?f_ri=3413","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":77,"name":"Robotics","url":"https://www.academia.edu/Documents/in/Robotics?f_ri=3413","nofollow":false},{"id":854,"name":"Computer Vision","url":"https://www.academia.edu/Documents/in/Computer_Vision?f_ri=3413","nofollow":false},{"id":3413,"name":"Robot Vision","url":"https://www.academia.edu/Documents/in/Robot_Vision?f_ri=3413","nofollow":false},{"id":15157,"name":"3D Reconstruction","url":"https://www.academia.edu/Documents/in/3D_Reconstruction?f_ri=3413","nofollow":false}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_47463941" data-work_id="47463941" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/47463941/A_cooperative_agent_based_architecture_for_environmental_exploration_and_knowledge_sharing_by_vision">A cooperative agent based architecture for environmental exploration and knowledge sharing by vision</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">A comprehensive approach to the design and implementation of multi-robots cooperative systems is described. It focuses on a design process that uses the Unified Modeling Language and on a detailed ontology description with the goal of... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_47463941" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">A comprehensive approach to the design and implementation of multi-robots cooperative systems is described. It focuses on a design process that uses the Unified Modeling Language and on a detailed ontology description with the goal of sharing the knowledge on environments that robots can acquire through the use of their vision sub-system. We base the implementation of our robotics vision system on agents inserted in a generic multi-level architectures. 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The vision sensor is mainly used for obstacle detection and avoidance,... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_3347194" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Vision-based robot navigation systems allow a robot to explore and to navigate in its environment in a way that facilitates path planning and goal-oriented tasks. The vision sensor is mainly used for obstacle detection and avoidance, object detection and tracking, and interaction with users. Usually these systems do not depend solely on vision sensors but use other sensors like sonar and laser range finder. The paper considers an important issue for mobile robots navigation. This issue is the detection of obstacles in front of the robot within a corridor. We proposed and evaluated three algorithms for obstacle detection within a corridor environment using image processing techniques.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/3347194" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="7d4316ae95c1bdad56292d9b420215c5" rel="nofollow" data-download="{"attachment_id":31433333,"asset_id":3347194,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/31433333/download_file?st=MTczMjQxMTY2OCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="1461589" href="https://benha.academia.edu/BasemElHalawany">Basem M . 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The vision sensor is mainly used for obstacle detection and avoidance, object detection and tracking, and interaction with users. Usually these systems do not depend solely on vision sensors but use other sensors like sonar and laser range finder. The paper considers an important issue for mobile robots navigation. This issue is the detection of obstacles in front of the robot within a corridor. We proposed and evaluated three algorithms for obstacle detection within a corridor environment using image processing techniques.","downloadable_attachments":[{"id":31433333,"asset_id":3347194,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":1461589,"first_name":"Basem","last_name":"El-Halawany","domain_name":"benha","page_name":"BasemElHalawany","display_name":"Basem M . 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Since this is a textbook, a great deal of this chapter entails a survey on the topic under the paradigm of cyber-physical systems, what can be done... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_34879652" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">We would like to invite you to join this exciting new project as a chapter contributor. Since this is a textbook, a great deal of this chapter entails a survey on the topic under the paradigm of cyber-physical systems, what can be done onboard and remotely, the distributed nature of the system and some exercises on futurology (anticipating trends can shed some light on upcoming designs). IET will bring great visibility to your work. You are welcome to suggest another topic/chapter title if you feel it would be more suitable. Each chapter should be around 20-25 pages each and can be submitted as a Word or Latex File. 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This... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_5726400" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Currently, a major difficulty for the widespread use of robots in assembly and material handling comes from the necessity of feeding accurately positioned workpieces to robots. ``Bin picking'' techniques help reduce this constraint. This paper presents the application of matched filters for enabling robots with vision to acquire workpieces randomly stored in bins. This approach complements heuristic methods already reported. The concept of matched filter is an old one. Here, however, it is redefined to take into account robot end-effector features, in terms of geometry and mechanics. In particular, the proposed filters match local workpiece structures where the robot end-effector is likely to grasp successfully and hold workpieces. The local nature of the holdsites is very important as computation costs are shown to vary with the fifth power of structure size. In addition, the proposed filters tend to have a narrow angular bandwidth. An example, which features a parallel-jaw hand is developed in detail, using both statistical and Fourier models. Both approaches concur in requiring a very small number of filters (typically four), even if a good orientation accuracy is expected (two degrees). Success rates of about 90 percent in three or fewer attempts have been experimentally obtained on a system which includes a small minicomputer, a 128 Ã 128 pixel solidstate camera, a prototype Cartesian robot, and a ``universal'' parallel-jaw hand.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/5726400" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="2216716ef87b69e2d0918aff28bc8fdc" rel="nofollow" data-download="{"attachment_id":49166273,"asset_id":5726400,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/49166273/download_file?st=MTczMjQxMTY2OCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="8317076" href="https://independent.academia.edu/HenriqueMartins8">Henrique Martins</a><script data-card-contents-for-user="8317076" type="text/json">{"id":8317076,"first_name":"Henrique","last_name":"Martins","domain_name":"independent","page_name":"HenriqueMartins8","display_name":"Henrique Martins","profile_url":"https://independent.academia.edu/HenriqueMartins8?f_ri=3413","photo":"https://0.academia-photos.com/8317076/2902128/3390459/s65_henrique.martins.jpg"}</script></span></span></li><li class="js-paper-rank-work_5726400 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="5726400"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 5726400, container: ".js-paper-rank-work_5726400", }); 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This paper presents the application of matched filters for enabling robots with vision to acquire workpieces randomly stored in bins. This approach complements heuristic methods already reported. The concept of matched filter is an old one. Here, however, it is redefined to take into account robot end-effector features, in terms of geometry and mechanics. In particular, the proposed filters match local workpiece structures where the robot end-effector is likely to grasp successfully and hold workpieces. The local nature of the holdsites is very important as computation costs are shown to vary with the fifth power of structure size. In addition, the proposed filters tend to have a narrow angular bandwidth. An example, which features a parallel-jaw hand is developed in detail, using both statistical and Fourier models. Both approaches concur in requiring a very small number of filters (typically four), even if a good orientation accuracy is expected (two degrees). Success rates of about 90 percent in three or fewer attempts have been experimentally obtained on a system which includes a small minicomputer, a 128 Ã 128 pixel solidstate camera, a prototype Cartesian robot, and a ``universal'' parallel-jaw hand.","downloadable_attachments":[{"id":49166273,"asset_id":5726400,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":8317076,"first_name":"Henrique","last_name":"Martins","domain_name":"independent","page_name":"HenriqueMartins8","display_name":"Henrique Martins","profile_url":"https://independent.academia.edu/HenriqueMartins8?f_ri=3413","photo":"https://0.academia-photos.com/8317076/2902128/3390459/s65_henrique.martins.jpg"}],"research_interests":[{"id":37,"name":"Information Systems","url":"https://www.academia.edu/Documents/in/Information_Systems?f_ri=3413","nofollow":false},{"id":2424,"name":"Computational Geometry","url":"https://www.academia.edu/Documents/in/Computational_Geometry?f_ri=3413","nofollow":false},{"id":3413,"name":"Robot Vision","url":"https://www.academia.edu/Documents/in/Robot_Vision?f_ri=3413","nofollow":false},{"id":34420,"name":"Crime scene analysis","url":"https://www.academia.edu/Documents/in/Crime_scene_analysis?f_ri=3413","nofollow":false},{"id":101530,"name":"Artificial Intelligent","url":"https://www.academia.edu/Documents/in/Artificial_Intelligent?f_ri=3413"},{"id":189381,"name":"Robot Control","url":"https://www.academia.edu/Documents/in/Robot_Control?f_ri=3413"},{"id":223807,"name":"Matched Filter","url":"https://www.academia.edu/Documents/in/Matched_Filter?f_ri=3413"},{"id":247481,"name":"Microcomputers","url":"https://www.academia.edu/Documents/in/Microcomputers?f_ri=3413"},{"id":402431,"name":"Success Rate","url":"https://www.academia.edu/Documents/in/Success_Rate?f_ri=3413"},{"id":936410,"name":"Bandwidth","url":"https://www.academia.edu/Documents/in/Bandwidth?f_ri=3413"},{"id":1237788,"name":"Electrical And Electronic Engineering","url":"https://www.academia.edu/Documents/in/Electrical_And_Electronic_Engineering?f_ri=3413"},{"id":2500540,"name":"Pattern analysis","url":"https://www.academia.edu/Documents/in/Pattern_analysis?f_ri=3413"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_12786497" data-work_id="12786497" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/12786497/A_Kalman_Filter_Integrated_Optical_Flow_Method_for_Velocity_Sensing_of_Mobile_Robots">A Kalman Filter-Integrated Optical Flow Method for Velocity Sensing of Mobile Robots</a></div></div><div class="u-pb4x u-mt3x"></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/12786497" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="8a499a3c1c57330961bfa7d9eb5bbbc5" rel="nofollow" data-download="{"attachment_id":45934627,"asset_id":12786497,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/45934627/download_file?st=MTczMjQxMTY2OCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="31854287" href="https://independent.academia.edu/LakmalDSeneviratne">Lakmal D. 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We discuss the need for a new series of benchmarks in the robot vision field to provide a direct quantitative measure of progress... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_15264311" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Abstract—This paper aims to propose a roadmap for benchmarking vision based robot localization approaches. We discuss the need for a new series of benchmarks in the robot vision field to provide a direct quantitative measure of progress understandable to sponsors and evaluators of research as well as a guide to practitioners in the field. A first set of benchmarks in two categories is proposed: vision based topological and metric localization. In particular we present a novel way to measure performances with respect to the ...</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/15264311" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="9be03924261842774c8fdb3a22105d52" rel="nofollow" data-download="{"attachment_id":38606299,"asset_id":15264311,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/38606299/download_file?st=MTczMjQxMTY2OCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="34340433" href="https://univpm.academia.edu/PZingaretti">Primo Zingaretti</a><script data-card-contents-for-user="34340433" type="text/json">{"id":34340433,"first_name":"Primo","last_name":"Zingaretti","domain_name":"univpm","page_name":"PZingaretti","display_name":"Primo Zingaretti","profile_url":"https://univpm.academia.edu/PZingaretti?f_ri=3413","photo":"/images/s65_no_pic.png"}</script></span></span><span class="u-displayInlineBlock InlineList-item-text"> and <span class="u-textDecorationUnderline u-clickable InlineList-item-text js-work-more-authors-15264311">+1</span><div class="hidden js-additional-users-15264311"><div><span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a href="https://univpm.academia.edu/EmanueleFrontoni">Emanuele Frontoni</a></span></div></div></span><script>(function(){ var popoverSettings = { el: $('.js-work-more-authors-15264311'), placement: 'bottom', hide_delay: 200, html: true, content: function(){ return $('.js-additional-users-15264311').html(); 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We discuss the need for a new series of benchmarks in the robot vision field to provide a direct quantitative measure of progress understandable to sponsors and evaluators of research as well as a guide to practitioners in the field. A first set of benchmarks in two categories is proposed: vision based topological and metric localization. In particular we present a novel way to measure performances with respect to the ...","downloadable_attachments":[{"id":38606299,"asset_id":15264311,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":34340433,"first_name":"Primo","last_name":"Zingaretti","domain_name":"univpm","page_name":"PZingaretti","display_name":"Primo Zingaretti","profile_url":"https://univpm.academia.edu/PZingaretti?f_ri=3413","photo":"/images/s65_no_pic.png"},{"id":3304969,"first_name":"Emanuele","last_name":"Frontoni","domain_name":"univpm","page_name":"EmanueleFrontoni","display_name":"Emanuele Frontoni","profile_url":"https://univpm.academia.edu/EmanueleFrontoni?f_ri=3413","photo":"https://0.academia-photos.com/3304969/1100809/1372782/s65_emanuele.frontoni.jpg"}],"research_interests":[{"id":3413,"name":"Robot Vision","url":"https://www.academia.edu/Documents/in/Robot_Vision?f_ri=3413","nofollow":false},{"id":24777,"name":"Benchmarking","url":"https://www.academia.edu/Documents/in/Benchmarking?f_ri=3413","nofollow":false},{"id":55641,"name":"Performance Evaluation","url":"https://www.academia.edu/Documents/in/Performance_Evaluation?f_ri=3413","nofollow":false},{"id":160144,"name":"Feature Extraction","url":"https://www.academia.edu/Documents/in/Feature_Extraction?f_ri=3413","nofollow":false},{"id":1268716,"name":"Robot Localization","url":"https://www.academia.edu/Documents/in/Robot_Localization?f_ri=3413"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_63732877" data-work_id="63732877" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/63732877/Humanoid_robots_in_Waseda_university_Hadaly_2_and_WABIAN">Humanoid robots in Waseda university—Hadaly-2 and WABIAN</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">This paper describes two humanoid robots developed in the Humanoid Robotics Institute, Waseda University. Hadaly-2 is intended to realize information interaction with humans by integrating environmental recognition with vision,... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_63732877" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">This paper describes two humanoid robots developed in the Humanoid Robotics Institute, Waseda University. Hadaly-2 is intended to realize information interaction with humans by integrating environmental recognition with vision, conversation capability (voice recognition, voice synthesis), and gesture behaviors. It also possesses physical interaction functions for direct contact with humans and behaviors that are gentle and safe for humans. WABIAN is a robot with a complete human configuration that is capable of walking on two legs and ...</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/63732877" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="f380b5765031be5dffb9cb94dd0c97e5" rel="nofollow" data-download="{"attachment_id":76061175,"asset_id":63732877,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/76061175/download_file?st=MTczMjQxMTY2OCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="46680511" href="https://kagawa-u.academia.edu/httpwwwengkagawauacjpsawada">Hideyuki Sawada</a><script data-card-contents-for-user="46680511" type="text/json">{"id":46680511,"first_name":"Hideyuki","last_name":"Sawada","domain_name":"kagawa-u","page_name":"httpwwwengkagawauacjpsawada","display_name":"Hideyuki Sawada","profile_url":"https://kagawa-u.academia.edu/httpwwwengkagawauacjpsawada?f_ri=3413","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_63732877 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="63732877"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 63732877, container: ".js-paper-rank-work_63732877", }); 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Hadaly-2 is intended to realize information interaction with humans by integrating environmental recognition with vision, conversation capability (voice recognition, voice synthesis), and gesture behaviors. It also possesses physical interaction functions for direct contact with humans and behaviors that are gentle and safe for humans. WABIAN is a robot with a complete human configuration that is capable of walking on two legs and ...","downloadable_attachments":[{"id":76061175,"asset_id":63732877,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":46680511,"first_name":"Hideyuki","last_name":"Sawada","domain_name":"kagawa-u","page_name":"httpwwwengkagawauacjpsawada","display_name":"Hideyuki Sawada","profile_url":"https://kagawa-u.academia.edu/httpwwwengkagawauacjpsawada?f_ri=3413","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":60,"name":"Mechanical Engineering","url":"https://www.academia.edu/Documents/in/Mechanical_Engineering?f_ri=3413","nofollow":false},{"id":237,"name":"Cognitive Science","url":"https://www.academia.edu/Documents/in/Cognitive_Science?f_ri=3413","nofollow":false},{"id":3413,"name":"Robot Vision","url":"https://www.academia.edu/Documents/in/Robot_Vision?f_ri=3413","nofollow":false},{"id":49007,"name":"Communication model","url":"https://www.academia.edu/Documents/in/Communication_model?f_ri=3413","nofollow":false},{"id":66694,"name":"Autonomous Robots","url":"https://www.academia.edu/Documents/in/Autonomous_Robots?f_ri=3413"},{"id":150569,"name":"Voice Recognition","url":"https://www.academia.edu/Documents/in/Voice_Recognition?f_ri=3413"},{"id":243384,"name":"Physical Interaction","url":"https://www.academia.edu/Documents/in/Physical_Interaction?f_ri=3413"},{"id":428932,"name":"Humanoid robot","url":"https://www.academia.edu/Documents/in/Humanoid_robot?f_ri=3413"},{"id":547929,"name":"Walking robot","url":"https://www.academia.edu/Documents/in/Walking_robot?f_ri=3413"},{"id":3854666,"name":"Autonomous Robot","url":"https://www.academia.edu/Documents/in/Autonomous_Robot?f_ri=3413"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_2247318" data-work_id="2247318" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/2247318/Analysis_of_Inverse_Kinematics_by_Trigonometric_Method_for_a_Robot_Vision_System">Analysis of Inverse Kinematics by Trigonometric Method for a Robot Vision System </a></div></div><div class="u-pb4x u-mt3x"></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/2247318" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="ad4749f2bbb70571982477e3e9143bff" rel="nofollow" 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href="https://www.academia.edu/Documents/in/Mechanical_Engineering">Mechanical Engineering</a>, <script data-card-contents-for-ri="60" type="text/json">{"id":60,"name":"Mechanical Engineering","url":"https://www.academia.edu/Documents/in/Mechanical_Engineering?f_ri=3413","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="3413" href="https://www.academia.edu/Documents/in/Robot_Vision">Robot Vision</a>, <script data-card-contents-for-ri="3413" type="text/json">{"id":3413,"name":"Robot Vision","url":"https://www.academia.edu/Documents/in/Robot_Vision?f_ri=3413","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="8124" href="https://www.academia.edu/Documents/in/Information_Filtering">Information Filtering</a>, <script data-card-contents-for-ri="8124" type="text/json">{"id":8124,"name":"Information Filtering","url":"https://www.academia.edu/Documents/in/Information_Filtering?f_ri=3413","nofollow":false}</script><a 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Mapping","url":"https://www.academia.edu/Documents/in/Simultaneous_Localization_and_Mapping?f_ri=3413"},{"id":131949,"name":"Remotely Operated Vehicle","url":"https://www.academia.edu/Documents/in/Remotely_Operated_Vehicle?f_ri=3413"},{"id":134028,"name":"Information Space","url":"https://www.academia.edu/Documents/in/Information_Space?f_ri=3413"},{"id":179654,"name":"Mobile Robot","url":"https://www.academia.edu/Documents/in/Mobile_Robot?f_ri=3413"},{"id":548622,"name":"Underwater vehicles","url":"https://www.academia.edu/Documents/in/Underwater_vehicles?f_ri=3413"},{"id":822358,"name":"Ground Truth","url":"https://www.academia.edu/Documents/in/Ground_Truth?f_ri=3413"},{"id":1237788,"name":"Electrical And Electronic Engineering","url":"https://www.academia.edu/Documents/in/Electrical_And_Electronic_Engineering?f_ri=3413"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_4066210" data-work_id="4066210" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/4066210/Visual_Servoing_Considering_Sensing_Dynamics_and_Robot_Dynamics">Visual Servoing Considering Sensing Dynamics and Robot Dynamics</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">For many desirable applications of vision guided industrial robots, real-time visual servoing is necessary but also challenging. Difficulty comes from the limited sampling rate and response time of typical machine vision systems equipped... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_4066210" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">For many desirable applications of vision guided industrial robots, real-time visual servoing is necessary but also challenging. Difficulty comes from the limited sampling rate and response time of typical machine vision systems equipped on industrial robots. These factors are addressed as the dynamics of visual sensing. In addition, robot dynamics should also be fully considered when designing the control law. Considering these aspects, this paper presents a control scheme of visual servoing. A dual-rate adaptive tracking filter is presented to compensate the visual sensing dynamics. Based on the compensated vision feedback, the techniques of multi-surface sliding control and dynamic surface control are used to formulate a two-layer control law for target tracking. System kinematics and dynamics are decoupled and dealt with by the two layers of the control law respectively. The proposed method is validated through experiments on a SCARA robot.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/4066210" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="5b8dcfce81a24341bd0d24f6fb496c47" rel="nofollow" data-download="{"attachment_id":31596981,"asset_id":4066210,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/31596981/download_file?st=MTczMjQxMTY2OSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="4895190" href="https://njit.academia.edu/CongWang">Cong Wang</a><script data-card-contents-for-user="4895190" type="text/json">{"id":4895190,"first_name":"Cong","last_name":"Wang","domain_name":"njit","page_name":"CongWang","display_name":"Cong Wang","profile_url":"https://njit.academia.edu/CongWang?f_ri=3413","photo":"https://0.academia-photos.com/4895190/2107889/10796424/s65_cong.wang.jpg"}</script></span></span></li><li class="js-paper-rank-work_4066210 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="4066210"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 4066210, container: ".js-paper-rank-work_4066210", }); 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Difficulty comes from the limited sampling rate and response time of typical machine vision systems equipped on industrial robots. These factors are addressed as the dynamics of visual sensing. In addition, robot dynamics should also be fully considered when designing the control law. Considering these aspects, this paper presents a control scheme of visual servoing. A dual-rate adaptive tracking filter is presented to compensate the visual sensing dynamics. Based on the compensated vision feedback, the techniques of multi-surface sliding control and dynamic surface control are used to formulate a two-layer control law for target tracking. System kinematics and dynamics are decoupled and dealt with by the two layers of the control law respectively. 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By completing the questionnaire, you will be eligible to participate in an opportunity for one of... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_36541259" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">We kindly ask you to participate in this research devoted to the adoption of service robots by travel, tourism and hospitality companies. By completing the questionnaire, you will be eligible to participate in an opportunity for one of five electronic 100-dollar gift cards, redeemable as Amazon gift vouchers, among other options. Once you have completed the survey, a separate link will be sent to you allowing you to register for the opportunity. The researchers will not be able to identify your responses to the survey questions and the identifying information you will leave for the purposes of the opportunity for the gift vouchers. <br /><br />For questions about your rights as a research subject, please contact the Director, Office of Research Integrity, Ball State University, Muncie, IN 47306, (765) 285-5070 or at <a href="mailto:irb@bsu.edu" rel="nofollow">irb@bsu.edu</a>. IRB Protocol Number: 1194315-1<br /><br />Please complete the questionnaire if only you are at least 18 years of age.<br />Thank you in advance for your participation!<br /><br />Here is the link to the survey:<br /><br /><a href="https://bsu.qualtrics.com/jfe/form/SV_4PdOOi6ERWHw97v" rel="nofollow">https://bsu.qualtrics.com/jfe/form/SV_4PdOOi6ERWHw97v</a><br /><br />Sincerely,<br /><br />Dr. Craig Webster, Ball State University, USA, email: <a href="mailto:cwebster3@bsu.edu" rel="nofollow">cwebster3@bsu.edu</a><br />Prof. Stanislav Ivanov, Varna University Management, Bulgaria, email: <a href="mailto:stanislav.ivanov@vumk.eu" rel="nofollow">stanislav.ivanov@vumk.eu</a></div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/36541259" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="c1fec2fb9e5ade5f8555cf6e7d3b751e" rel="nofollow" data-download="{"attachment_id":56463693,"asset_id":36541259,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/56463693/download_file?st=MTczMjQxMTY2OSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="22133" href="https://vumk.academia.edu/StanislavIvanov">Stanislav Ivanov</a><script data-card-contents-for-user="22133" type="text/json">{"id":22133,"first_name":"Stanislav","last_name":"Ivanov","domain_name":"vumk","page_name":"StanislavIvanov","display_name":"Stanislav Ivanov","profile_url":"https://vumk.academia.edu/StanislavIvanov?f_ri=3413","photo":"https://0.academia-photos.com/22133/80607/38525499/s65_stanislav.ivanov.jpg"}</script></span></span></li><li class="js-paper-rank-work_36541259 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="36541259"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 36541259, container: ".js-paper-rank-work_36541259", }); 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IRB Protocol Number: 1194315-1\n\nPlease complete the questionnaire if only you are at least 18 years of age.\nThank you in advance for your participation!\n\nHere is the link to the survey:\n\nhttps://bsu.qualtrics.com/jfe/form/SV_4PdOOi6ERWHw97v\n\nSincerely,\n\nDr. Craig Webster, Ball State University, USA, email: cwebster3@bsu.edu\nProf. Stanislav Ivanov, Varna University Management, Bulgaria, email: stanislav.ivanov@vumk.eu","downloadable_attachments":[{"id":56463693,"asset_id":36541259,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":22133,"first_name":"Stanislav","last_name":"Ivanov","domain_name":"vumk","page_name":"StanislavIvanov","display_name":"Stanislav 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issues humans deal with on a regular basis is their own (at times, of their possessions) safety from various external agencies like car crashes, or homicide threats due to concealed weapons. To tackle this issue, a... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_24309125" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">One of the major issues humans deal with on a regular basis is their own (at times, of their possessions) safety from various external agencies like car crashes, or homicide threats due to concealed weapons. To tackle this issue, a robotic vision and image processing application could be developed and trained to recognize situations where human lives could be at hazard. For this proposal, a portable robot equipped with a set of cameras (infrared + normal vision as well as a LIDAR) is being considered, to be able to detect imminent crashes in moving vehicles or detect if there are concealed weapons on a personnel, all in real-time. The robot would survey human surroundings 24*7 and give appropriate warnings/ signals in case of danger. This robot could further be developed to perform preventive actions if a situation of alert is detected thus, acting as a trained bodyguard. With the present advances in technology, it is possible to develop such robotic applications that will make our lives much safer and easier.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/24309125" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="b844a62f9868ee7c8f0ef3e9ebe578b5" rel="nofollow" data-download="{"attachment_id":44645301,"asset_id":24309125,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/44645301/download_file?st=MTczMjQxMTY2OSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="46867075" href="https://independent.academia.edu/APapriwal">Anuja Papriwal</a><script data-card-contents-for-user="46867075" type="text/json">{"id":46867075,"first_name":"Anuja","last_name":"Papriwal","domain_name":"independent","page_name":"APapriwal","display_name":"Anuja Papriwal","profile_url":"https://independent.academia.edu/APapriwal?f_ri=3413","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_24309125 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="24309125"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 24309125, container: ".js-paper-rank-work_24309125", }); });</script></li><li class="js-percentile-work_24309125 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 24309125; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_24309125"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_24309125 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="24309125"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 24309125; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=24309125]").text(description); $(".js-view-count-work_24309125").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_24309125").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="24309125"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i></div><span class="InlineList-item-text u-textTruncate u-pl6x"><a class="InlineList-item-text" data-has-card-for-ri="3413" href="https://www.academia.edu/Documents/in/Robot_Vision">Robot Vision</a><script data-card-contents-for-ri="3413" type="text/json">{"id":3413,"name":"Robot Vision","url":"https://www.academia.edu/Documents/in/Robot_Vision?f_ri=3413","nofollow":false}</script></span></li><script>(function(){ if (false) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=24309125]'), work: {"id":24309125,"title":"Abstract and Conclusion for a Robot Vision Project","created_at":"2016-04-11T21:28:19.903-07:00","url":"https://www.academia.edu/24309125/Abstract_and_Conclusion_for_a_Robot_Vision_Project?f_ri=3413","dom_id":"work_24309125","summary":"One of the major issues humans deal with on a regular basis is their own (at times, of their possessions) safety from various external agencies like car crashes, or homicide threats due to concealed weapons. 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The areas of application of these two disciplines range widely, encompassing fields such as medicine, robotics, and security... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_35310787" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Deep learning and image processing are two areas of great interest to academics and industry professionals alike. The areas of application of these two disciplines range widely, encompassing fields such as medicine, robotics, and security and surveillance. The aim of this book, 'Deep Learning for Image Processing Applications', is to offer concepts from these two areas in the same platform, and the book brings together the shared ideas of professionals from academia and research about problems and solutions relating to the multifaceted aspects of the two disciplines. The first chapter provides an introduction to deep learning, and serves as the basis for much of what follows in the subsequent chapters, which cover subjects including: the application of deep neural networks for image classification; hand gesture recognition in robotics; deep learning techniques for image retrieval; disease detection using deep learning techniques; and the comparative analysis of deep data and big data. The book will be of interest to all those whose work involves the use of deep learning and image processing techniques</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/35310787" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="fbfaf8980224957a0b53929b144dea31" rel="nofollow" data-download="{"attachment_id":55171167,"asset_id":35310787,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/55171167/download_file?st=MTczMjQxMTY2OSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="1569445" href="https://uff.academia.edu/VaniaEstrela">Vania V Estrela</a><script data-card-contents-for-user="1569445" type="text/json">{"id":1569445,"first_name":"Vania","last_name":"Estrela","domain_name":"uff","page_name":"VaniaEstrela","display_name":"Vania V Estrela","profile_url":"https://uff.academia.edu/VaniaEstrela?f_ri=3413","photo":"https://0.academia-photos.com/1569445/584405/780232/s65_vania_v..estrela.jpg"}</script></span></span></li><li class="js-paper-rank-work_35310787 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="35310787"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 35310787, container: ".js-paper-rank-work_35310787", }); 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It is composed of a mobile robot, equipped with a... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_75205389" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">ABSTRACT This work presents a system for the automatic selective harvesting of asparagus in open field being developed in the framework of the Italian National Project on Robotics. It is composed of a mobile robot, equipped with a suitable manipulator, and driven by a stereo-vision module. 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InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="1937691"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 1937691; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=1937691]").text(description); $(".js-view-count-work_1937691").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_1937691").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="1937691"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">17</a>  </div><span class="InlineList-item-text u-textTruncate u-pl10x"><a class="InlineList-item-text" data-has-card-for-ri="188" href="https://www.academia.edu/Documents/in/Cultural_Studies">Cultural Studies</a>, <script data-card-contents-for-ri="188" type="text/json">{"id":188,"name":"Cultural Studies","url":"https://www.academia.edu/Documents/in/Cultural_Studies?f_ri=3413","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="915" href="https://www.academia.edu/Documents/in/Art_History">Art History</a>, <script data-card-contents-for-ri="915" type="text/json">{"id":915,"name":"Art History","url":"https://www.academia.edu/Documents/in/Art_History?f_ri=3413","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="925" href="https://www.academia.edu/Documents/in/Visual_Anthropology">Visual Anthropology</a>, <script data-card-contents-for-ri="925" type="text/json">{"id":925,"name":"Visual Anthropology","url":"https://www.academia.edu/Documents/in/Visual_Anthropology?f_ri=3413","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="934" href="https://www.academia.edu/Documents/in/Media_and_Cultural_Studies">Media and Cultural Studies</a><script data-card-contents-for-ri="934" type="text/json">{"id":934,"name":"Media and Cultural Studies","url":"https://www.academia.edu/Documents/in/Media_and_Cultural_Studies?f_ri=3413","nofollow":false}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=1937691]'), work: {"id":1937691,"title":"Can film show the invisible: the work of montage in ethnographic 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Anthropology","url":"https://www.academia.edu/Documents/in/Visual_Anthropology?f_ri=3413","nofollow":false},{"id":934,"name":"Media and Cultural Studies","url":"https://www.academia.edu/Documents/in/Media_and_Cultural_Studies?f_ri=3413","nofollow":false},{"id":957,"name":"Cultural Sociology","url":"https://www.academia.edu/Documents/in/Cultural_Sociology?f_ri=3413"},{"id":1555,"name":"Film Studies","url":"https://www.academia.edu/Documents/in/Film_Studies?f_ri=3413"},{"id":1589,"name":"Photography","url":"https://www.academia.edu/Documents/in/Photography?f_ri=3413"},{"id":2418,"name":"Literature","url":"https://www.academia.edu/Documents/in/Literature?f_ri=3413"},{"id":3413,"name":"Robot Vision","url":"https://www.academia.edu/Documents/in/Robot_Vision?f_ri=3413"},{"id":8232,"name":"Anthropology Of Art","url":"https://www.academia.edu/Documents/in/Anthropology_Of_Art?f_ri=3413"},{"id":20169,"name":"Montage","url":"https://www.academia.edu/Documents/in/Montage?f_ri=3413"},{"id":23214,"name":"Documentary Film","url":"https://www.academia.edu/Documents/in/Documentary_Film?f_ri=3413"},{"id":26558,"name":"Visual Arts","url":"https://www.academia.edu/Documents/in/Visual_Arts?f_ri=3413"},{"id":101374,"name":"Collage, Montage, \u0026 Assemblage","url":"https://www.academia.edu/Documents/in/Collage_Montage_and_Assemblage?f_ri=3413"},{"id":971467,"name":"Theories of Montage Between Cinema","url":"https://www.academia.edu/Documents/in/Theories_of_Montage_Between_Cinema?f_ri=3413"},{"id":971472,"name":"Cultural History and Criticism","url":"https://www.academia.edu/Documents/in/Cultural_History_and_Criticism?f_ri=3413"},{"id":1019146,"name":"Visual Anthropology and Sociology","url":"https://www.academia.edu/Documents/in/Visual_Anthropology_and_Sociology?f_ri=3413"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_78540857" data-work_id="78540857" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/78540857/Color_Sorting_Robotic_Arm_Using_Arduino">Color Sorting Robotic Arm Using Arduino</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">In the robotic community Robotic Arm is one of the popular ideas. Robotic arms are very usual in industries where they are mainly used in assembly lines in manufacturing plants. Building a Robotic Arm is a difficult process and involves... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_78540857" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">In the robotic community Robotic Arm is one of the popular ideas. Robotic arms are very usual in industries where they are mainly used in assembly lines in manufacturing plants. Building a Robotic Arm is a difficult process and involves complex programming for beginners. Once we put our full focus on the thing and even if our thoughts were the same in the at start , we found it rather easy as all it takes was some time and tinkering. The robotic arm we have built in this project is fully automated to do tasks. Apart from the Microcontroller we have used Arduino Nano and servo motors, all other components. This robotic arm can color sort and for that we have used sensors.The main motto of this project is to design an efficient system that pick up right color of objects and put it down at right place to the minimizing the cost of the products, optimizes productivity and decreasing human mistakes. This paper presents an application to differentiate colored objects with a robotic arm. This robotic arm can pick different colored objects and sorts them in specified cups. It contacts with color sensor TCS 3200 and various motor modules in real time to detect the correct color object and to control the movement of arm.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/78540857" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="b7997b14952717420e49b0a5153c0abe" rel="nofollow" data-download="{"attachment_id":85553262,"asset_id":78540857,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/85553262/download_file?st=MTczMjQxMTY2OSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="222736109" href="https://independent.academia.edu/priyankashetty50">priyanka shetty</a><script data-card-contents-for-user="222736109" type="text/json">{"id":222736109,"first_name":"priyanka","last_name":"shetty","domain_name":"independent","page_name":"priyankashetty50","display_name":"priyanka shetty","profile_url":"https://independent.academia.edu/priyankashetty50?f_ri=3413","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_78540857 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="78540857"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 78540857, container: ".js-paper-rank-work_78540857", }); });</script></li><li class="js-percentile-work_78540857 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 78540857; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_78540857"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_78540857 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="78540857"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 78540857; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=78540857]").text(description); $(".js-view-count-work_78540857").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_78540857").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="78540857"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">9</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="77" href="https://www.academia.edu/Documents/in/Robotics">Robotics</a>, <script data-card-contents-for-ri="77" type="text/json">{"id":77,"name":"Robotics","url":"https://www.academia.edu/Documents/in/Robotics?f_ri=3413","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="3413" href="https://www.academia.edu/Documents/in/Robot_Vision">Robot Vision</a>, <script data-card-contents-for-ri="3413" type="text/json">{"id":3413,"name":"Robot Vision","url":"https://www.academia.edu/Documents/in/Robot_Vision?f_ri=3413","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="4893" href="https://www.academia.edu/Documents/in/Humanoid_Robotics">Humanoid Robotics</a>, <script data-card-contents-for-ri="4893" type="text/json">{"id":4893,"name":"Humanoid Robotics","url":"https://www.academia.edu/Documents/in/Humanoid_Robotics?f_ri=3413","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="39595" href="https://www.academia.edu/Documents/in/Mechatronics_and_Robotics-2">Mechatronics and Robotics</a><script data-card-contents-for-ri="39595" type="text/json">{"id":39595,"name":"Mechatronics and Robotics","url":"https://www.academia.edu/Documents/in/Mechatronics_and_Robotics-2?f_ri=3413","nofollow":false}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=78540857]'), work: {"id":78540857,"title":"Color Sorting Robotic Arm Using Arduino","created_at":"2022-05-05T12:06:41.228-07:00","url":"https://www.academia.edu/78540857/Color_Sorting_Robotic_Arm_Using_Arduino?f_ri=3413","dom_id":"work_78540857","summary":"In the robotic community Robotic Arm is one of the popular ideas. Robotic arms are very usual in industries where they are mainly used in assembly lines in manufacturing plants. Building a Robotic Arm is a difficult process and involves complex programming for beginners. Once we put our full focus on the thing and even if our thoughts were the same in the at start , we found it rather easy as all it takes was some time and tinkering. The robotic arm we have built in this project is fully automated to do tasks. Apart from the Microcontroller we have used Arduino Nano and servo motors, all other components. This robotic arm can color sort and for that we have used sensors.The main motto of this project is to design an efficient system that pick up right color of objects and put it down at right place to the minimizing the cost of the products, optimizes productivity and decreasing human mistakes. This paper presents an application to differentiate colored objects with a robotic arm. This robotic arm can pick different colored objects and sorts them in specified cups. It contacts with color sensor TCS 3200 and various motor modules in real time to detect the correct color object and to control the movement of arm.","downloadable_attachments":[{"id":85553262,"asset_id":78540857,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":222736109,"first_name":"priyanka","last_name":"shetty","domain_name":"independent","page_name":"priyankashetty50","display_name":"priyanka shetty","profile_url":"https://independent.academia.edu/priyankashetty50?f_ri=3413","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":77,"name":"Robotics","url":"https://www.academia.edu/Documents/in/Robotics?f_ri=3413","nofollow":false},{"id":3413,"name":"Robot Vision","url":"https://www.academia.edu/Documents/in/Robot_Vision?f_ri=3413","nofollow":false},{"id":4893,"name":"Humanoid Robotics","url":"https://www.academia.edu/Documents/in/Humanoid_Robotics?f_ri=3413","nofollow":false},{"id":39595,"name":"Mechatronics and Robotics","url":"https://www.academia.edu/Documents/in/Mechatronics_and_Robotics-2?f_ri=3413","nofollow":false},{"id":77479,"name":"Human Augmentation","url":"https://www.academia.edu/Documents/in/Human_Augmentation?f_ri=3413"},{"id":247799,"name":"Internet of Things (IoT)","url":"https://www.academia.edu/Documents/in/Internet_of_Things_IoT_?f_ri=3413"},{"id":327654,"name":"Robotic Arm","url":"https://www.academia.edu/Documents/in/Robotic_Arm?f_ri=3413"},{"id":1253169,"name":"Arduino Project","url":"https://www.academia.edu/Documents/in/Arduino_Project?f_ri=3413"},{"id":1681036,"name":"Color Sorting","url":"https://www.academia.edu/Documents/in/Color_Sorting?f_ri=3413"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_32204259 coauthored" data-work_id="32204259" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/32204259/Artificial_Intelligence_and_the_Good_Society_the_US_EU_and_UK_approach">Artificial Intelligence and the 'Good Society': the US, EU, and UK approach</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">In October 2016, the White House, the European Parliament, and the UK House of Commons each issued a report outlining their visions on how to prepare society for the widespread use of artificial intelligence (AI). In this article, we... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_32204259" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">In October 2016, the White House, the European Parliament, and the UK House of Commons each issued a report outlining their visions on how to prepare society for the widespread use of artificial intelligence (AI). In this article, we provide a comparative assessment of these three reports in order to facilitate the design of policies favourable to the development of a 'good AI society'. To do so, we examine how each report addresses the following three topics: (a) the development of a 'good AI society'; (b) the role and responsibility of the government, the private sector, and the research community (including academia) in pursuing such a development; and (c) where the recommendations to support such a development may be in need of improvement. Our analysis concludes that the reports address adequately various ethical, social, and economic topics, but come short of providing an overarching political vision and long-term strategy for the development of a 'good AI society'. In order to contribute to fill this gap, in the conclusion we suggest a two-pronged approach.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/32204259" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="72eb30f93003086d3a48ccb6e3084e2b" rel="nofollow" data-download="{"attachment_id":52434610,"asset_id":32204259,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/52434610/download_file?st=MTczMjQxMTY2OSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="16582545" href="https://oxford.academia.edu/CorinneCath">Corinne Cath</a><script data-card-contents-for-user="16582545" type="text/json">{"id":16582545,"first_name":"Corinne","last_name":"Cath","domain_name":"oxford","page_name":"CorinneCath","display_name":"Corinne Cath","profile_url":"https://oxford.academia.edu/CorinneCath?f_ri=3413","photo":"https://0.academia-photos.com/16582545/11046051/18056580/s65_corinne.cath.jpg"}</script></span></span><span class="u-displayInlineBlock InlineList-item-text"> and <span class="u-textDecorationUnderline u-clickable InlineList-item-text js-work-more-authors-32204259">+3</span><div class="hidden js-additional-users-32204259"><div><span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a href="https://oxford.academia.edu/SandraWachter">Sandra Wachter</a></span></div><div><span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a href="https://oxford.academia.edu/BrentMittelstadt">Brent Mittelstadt</a></span></div><div><span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a href="https://yale.academia.edu/LucianoFloridi">Luciano Floridi</a></span></div></div></span><script>(function(){ var popoverSettings = { el: $('.js-work-more-authors-32204259'), placement: 'bottom', hide_delay: 200, html: true, content: function(){ return $('.js-additional-users-32204259').html(); 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In this article, we provide a comparative assessment of these three reports in order to facilitate the design of policies favourable to the development of a 'good AI society'. To do so, we examine how each report addresses the following three topics: (a) the development of a 'good AI society'; (b) the role and responsibility of the government, the private sector, and the research community (including academia) in pursuing such a development; and (c) where the recommendations to support such a development may be in need of improvement. Our analysis concludes that the reports address adequately various ethical, social, and economic topics, but come short of providing an overarching political vision and long-term strategy for the development of a 'good AI society'. In order to contribute to fill this gap, in the conclusion we suggest a two-pronged approach. ","downloadable_attachments":[{"id":52434610,"asset_id":32204259,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":16582545,"first_name":"Corinne","last_name":"Cath","domain_name":"oxford","page_name":"CorinneCath","display_name":"Corinne Cath","profile_url":"https://oxford.academia.edu/CorinneCath?f_ri=3413","photo":"https://0.academia-photos.com/16582545/11046051/18056580/s65_corinne.cath.jpg"},{"id":55640176,"first_name":"Sandra","last_name":"Wachter","domain_name":"oxford","page_name":"SandraWachter","display_name":"Sandra Wachter","profile_url":"https://oxford.academia.edu/SandraWachter?f_ri=3413","photo":"https://0.academia-photos.com/55640176/15009734/15747283/s65_sandra.wachter.jpg"},{"id":2050164,"first_name":"Brent","last_name":"Mittelstadt","domain_name":"oxford","page_name":"BrentMittelstadt","display_name":"Brent 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Soft Computing Methodologies","url":"https://www.academia.edu/Documents/in/Artificial_Intelligent_and_Soft_Computing_Methodologies?f_ri=3413"},{"id":1224548,"name":"Electrical and Computer Engineering (ECE)","url":"https://www.academia.edu/Documents/in/Electrical_and_Computer_Engineering_ECE_?f_ri=3413"},{"id":1331890,"name":"Robots and Ethics","url":"https://www.academia.edu/Documents/in/Robots_and_Ethics?f_ri=3413"},{"id":1712058,"name":"Big Data Ethics","url":"https://www.academia.edu/Documents/in/Big_Data_Ethics?f_ri=3413"},{"id":1744493,"name":"Science and Technology Studies","url":"https://www.academia.edu/Documents/in/Science_and_Technology_Studies?f_ri=3413"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_26460487 coauthored" data-work_id="26460487" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/26460487/Cooperative_and_Non_Cooperative_Sense_and_Avoid_in_the_CNS_A_Context_A_Unified_Methodology">Cooperative and Non-Cooperative Sense-and-Avoid in the CNS+A Context: A Unified Methodology</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">A unified approach to cooperative and non-cooperative Sense-and-Avoid (SAA) is presented that addresses the technical and regulatory challenges of Unmanned Aircraft Systems (UAS) integration into non-segregated airspace. In this paper,... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_26460487" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">A unified approach to cooperative and non-cooperative Sense-and-Avoid (SAA) is presented that addresses the technical and regulatory challenges of Unmanned Aircraft Systems (UAS) integration into non-segregated airspace. In this paper, state-of-the-art sensor/system technologies for cooperative and non-cooperative SAA are reviewed and a reference system architecture is presented. Automated selection of sensors/systems including passive and active Forward Looking Sensors (FLS), Traffic Collision Avoidance System (TCAS) and Automatic Dependent Surveillance – Broadcast (ADS-B) system is performed based on Boolean Decision Logics (BDL) to support trusted autonomous operations during all flight phases. The BDL adoption allows for a dynamic reconfiguration of the SAA architecture, based on the current error estimates of navigation and tracking sensors/systems. The significance of this approach is discussed in the Communication, Navigation and Surveillance/Air Traffic Management and Avionics (CNS+A) context, with a focus on avionics and ATM certification requirements. Additionally, the mathematical models employed in the SAA Unified Method (SUM) to compute the overall uncertainty volume in the airspace surrounding an intruder/obstacle are described. In the presented methodology, navigation and tracking errors affecting the host UAS platform and intruder sensor measurements are translated to unified range and bearing uncertainty descriptors. Simulation case studies are presented to evaluate the performance of the unified approach on a representative UAS host platform and a number of intruder platforms. The results confirm the validity of the proposed unified methodology providing a pathway for certification of SAA systems that typically employ a suite of non-cooperative sensors and/or cooperative systems.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/26460487" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="f87c4b89278b097b64a99c836353acd1" rel="nofollow" data-download="{"attachment_id":46755736,"asset_id":26460487,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/46755736/download_file?st=MTczMjQxMTY2OSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="9093041" href="https://khalifa.academia.edu/RobertoSabatini">Roberto Sabatini</a><script data-card-contents-for-user="9093041" type="text/json">{"id":9093041,"first_name":"Roberto","last_name":"Sabatini","domain_name":"khalifa","page_name":"RobertoSabatini","display_name":"Roberto Sabatini","profile_url":"https://khalifa.academia.edu/RobertoSabatini?f_ri=3413","photo":"https://0.academia-photos.com/9093041/4660850/17407129/s65_roberto.sabatini.jpg"}</script></span></span><span class="u-displayInlineBlock InlineList-item-text"> and <span class="u-textDecorationUnderline u-clickable InlineList-item-text js-work-more-authors-26460487">+2</span><div class="hidden js-additional-users-26460487"><div><span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a href="https://rmit.academia.edu/SubramanianRamasamy">Subramanian Ramasamy</a></span></div><div><span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a href="https://khalifa.academia.edu/AlessandroGardi">Alessandro Gardi</a></span></div></div></span><script>(function(){ var popoverSettings = { el: $('.js-work-more-authors-26460487'), placement: 'bottom', hide_delay: 200, html: true, content: function(){ return $('.js-additional-users-26460487').html(); 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In this paper, state-of-the-art sensor/system technologies for cooperative and non-cooperative SAA are reviewed and a reference system architecture is presented. Automated selection of sensors/systems including passive and active Forward Looking Sensors (FLS), Traffic Collision Avoidance System (TCAS) and Automatic Dependent Surveillance – Broadcast (ADS-B) system is performed based on Boolean Decision Logics (BDL) to support trusted autonomous operations during all flight phases. The BDL adoption allows for a dynamic reconfiguration of the SAA architecture, based on the current error estimates of navigation and tracking sensors/systems. The significance of this approach is discussed in the Communication, Navigation and Surveillance/Air Traffic Management and Avionics (CNS+A) context, with a focus on avionics and ATM certification requirements. Additionally, the mathematical models employed in the SAA Unified Method (SUM) to compute the overall uncertainty volume in the airspace surrounding an intruder/obstacle are described. In the presented methodology, navigation and tracking errors affecting the host UAS platform and intruder sensor measurements are translated to unified range and bearing uncertainty descriptors. Simulation case studies are presented to evaluate the performance of the unified approach on a representative UAS host platform and a number of intruder platforms. The results confirm the validity of the proposed unified methodology providing a pathway for certification of SAA systems that typically employ a suite of non-cooperative sensors and/or cooperative systems.","downloadable_attachments":[{"id":46755736,"asset_id":26460487,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":9093041,"first_name":"Roberto","last_name":"Sabatini","domain_name":"khalifa","page_name":"RobertoSabatini","display_name":"Roberto Sabatini","profile_url":"https://khalifa.academia.edu/RobertoSabatini?f_ri=3413","photo":"https://0.academia-photos.com/9093041/4660850/17407129/s65_roberto.sabatini.jpg"},{"id":25106050,"first_name":"Subramanian","last_name":"Ramasamy","domain_name":"rmit","page_name":"SubramanianRamasamy","display_name":"Subramanian Ramasamy","profile_url":"https://rmit.academia.edu/SubramanianRamasamy?f_ri=3413","photo":"https://0.academia-photos.com/25106050/9794378/10912904/s65_subramanian.ramasamy.jpg"},{"id":32392907,"first_name":"Alessandro","last_name":"Gardi","domain_name":"khalifa","page_name":"AlessandroGardi","display_name":"Alessandro Gardi","profile_url":"https://khalifa.academia.edu/AlessandroGardi?f_ri=3413","photo":"https://0.academia-photos.com/32392907/40923811/33345526/s65_alessandro.gardi.jpg"}],"research_interests":[{"id":77,"name":"Robotics","url":"https://www.academia.edu/Documents/in/Robotics?f_ri=3413","nofollow":false},{"id":88,"name":"Aerospace Engineering","url":"https://www.academia.edu/Documents/in/Aerospace_Engineering?f_ri=3413","nofollow":false},{"id":471,"name":"Robotics (Computer Science)","url":"https://www.academia.edu/Documents/in/Robotics_Computer_Science_?f_ri=3413","nofollow":false},{"id":1526,"name":"Sensors and 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})();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_28909475 coauthored" data-work_id="28909475" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/28909475/A_Low_Cost_and_High_Performance_Navigation_System_for_Small_RPAS_Applications">A Low-Cost and High Performance Navigation System for Small RPAS Applications</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Modern Remotely Piloted Aircraft Systems (RPAS) employ a variety of sensors and multi-sensor data fusion techniques to provide advanced functionalities and trusted autonomy in a wide range of mission-essential and safety-critical tasks.... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_28909475" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Modern Remotely Piloted Aircraft Systems (RPAS) employ a variety of sensors and multi-sensor data fusion techniques to provide advanced functionalities and trusted autonomy in a wide range of mission-essential and safety-critical tasks. In particular, Navigation and Guidance Systems (NGS) for small RPAS require a typical combination of lightweight, compact and inexpensive sensors to satisfy the Required Navigation Performance (RNP) in all flight phases. In this paper, the synergies attainable by the combination of Global Navigation Satellite System (GNSS), Micro-Electromechanical System based Inertial Measurement Unit (MEMS-IMU) and Vision-Based Navigation (VBN) sensors are explored. In case of VBN, an appearance-based navigation technique is adopted and feature extraction/optical flow methods are employed to estimate the navigation parameters during precision approach and landing phases. A key novelty of the proposed approach is the employment of Aircraft Dynamics Models (ADM) augmentation to compensate for the shortcomings of VBN and MEMS-IMU sensors in high-dynamics attitude determination tasks. To obtain the best estimates of Position, Velocity and Attitude (PVA), different sensor combinations are analysed and dynamic Boolean Decision Logics (BDL) are implemented for data selection before the centralised data fusion is accomplished. Various alternatives for data fusion are investigated including a traditional Extended Kalman Filter (EKF) and a more advanced Unscented Kalman Filter (UKF). A novel hybrid controller employing fuzzy logic and Proportional-Integral-Derivative (PID) techniques is implemented to provide effective stabilization and control of pitch and roll angles. After introducing the key mathematical models describing the three NGS architectures: EKF based VBN-IMU-GNSS (VIG) and VBN-IMU-GNSS-ADM (VIGA) and UKF based Enhanced VIGA (EVIGA), the system performances are compared in a small RPAS integration scheme (i.e., AEROSONDE RPAS platform) exploring a representative cross-section of the aircraft operational flight envelope. A dedicated ADM processor (i.e., a local pre-filter) is adopted in the EVIGA architecture to account for the RPAS maneuvering envelope in different flight phases (assisted by a maneuver identification algorithm), in order to extend the ADM validity time across all segments of the RPAS trajectory. Simulation results show that the VIG, VIGA and EVIGA systems are compliant with ICAO requirements for precision approach down to CAT-II. In all other flight phases, the VIGA system shows improvement in PVA data output with respect to the VIG system. The EVIGA system shows the best performance in terms of attitude data accuracy and a significant extension of the ADM validity time is achieved in this configuration.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/28909475" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="d248ff38c62bf2898a3d98c55e379dab" rel="nofollow" data-download="{"attachment_id":49343529,"asset_id":28909475,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/49343529/download_file?st=MTczMjQxMTY2OSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="9093041" href="https://khalifa.academia.edu/RobertoSabatini">Roberto Sabatini</a><script data-card-contents-for-user="9093041" type="text/json">{"id":9093041,"first_name":"Roberto","last_name":"Sabatini","domain_name":"khalifa","page_name":"RobertoSabatini","display_name":"Roberto Sabatini","profile_url":"https://khalifa.academia.edu/RobertoSabatini?f_ri=3413","photo":"https://0.academia-photos.com/9093041/4660850/17407129/s65_roberto.sabatini.jpg"}</script></span></span><span class="u-displayInlineBlock InlineList-item-text"> and <span class="u-textDecorationUnderline u-clickable InlineList-item-text js-work-more-authors-28909475">+1</span><div class="hidden js-additional-users-28909475"><div><span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a href="https://rmit.academia.edu/SubramanianRamasamy">Subramanian Ramasamy</a></span></div></div></span><script>(function(){ var popoverSettings = { el: $('.js-work-more-authors-28909475'), placement: 'bottom', hide_delay: 200, html: true, content: function(){ return $('.js-additional-users-28909475').html(); 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In particular, Navigation and Guidance Systems (NGS) for small RPAS require a typical combination of lightweight, compact and inexpensive sensors to satisfy the Required Navigation Performance (RNP) in all flight phases. In this paper, the synergies attainable by the combination of Global Navigation Satellite System (GNSS), Micro-Electromechanical System based Inertial Measurement Unit (MEMS-IMU) and Vision-Based Navigation (VBN) sensors are explored. In case of VBN, an appearance-based navigation technique is adopted and feature extraction/optical flow methods are employed to estimate the navigation parameters during precision approach and landing phases. A key novelty of the proposed approach is the employment of Aircraft Dynamics Models (ADM) augmentation to compensate for the shortcomings of VBN and MEMS-IMU sensors in high-dynamics attitude determination tasks. To obtain the best estimates of Position, Velocity and Attitude (PVA), different sensor combinations are analysed and dynamic Boolean Decision Logics (BDL) are implemented for data selection before the centralised data fusion is accomplished. Various alternatives for data fusion are investigated including a traditional Extended Kalman Filter (EKF) and a more advanced Unscented Kalman Filter (UKF). A novel hybrid controller employing fuzzy logic and Proportional-Integral-Derivative (PID) techniques is implemented to provide effective stabilization and control of pitch and roll angles. After introducing the key mathematical models describing the three NGS architectures: EKF based VBN-IMU-GNSS (VIG) and VBN-IMU-GNSS-ADM (VIGA) and UKF based Enhanced VIGA (EVIGA), the system performances are compared in a small RPAS integration scheme (i.e., AEROSONDE RPAS platform) exploring a representative cross-section of the aircraft operational flight envelope. A dedicated ADM processor (i.e., a local pre-filter) is adopted in the EVIGA architecture to account for the RPAS maneuvering envelope in different flight phases (assisted by a maneuver identification algorithm), in order to extend the ADM validity time across all segments of the RPAS trajectory. Simulation results show that the VIG, VIGA and EVIGA systems are compliant with ICAO requirements for precision approach down to CAT-II. In all other flight phases, the VIGA system shows improvement in PVA data output with respect to the VIG system. The EVIGA system shows the best performance in terms of attitude data accuracy and a significant extension of the ADM validity time is achieved in this configuration.","downloadable_attachments":[{"id":49343529,"asset_id":28909475,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":9093041,"first_name":"Roberto","last_name":"Sabatini","domain_name":"khalifa","page_name":"RobertoSabatini","display_name":"Roberto Sabatini","profile_url":"https://khalifa.academia.edu/RobertoSabatini?f_ri=3413","photo":"https://0.academia-photos.com/9093041/4660850/17407129/s65_roberto.sabatini.jpg"},{"id":25106050,"first_name":"Subramanian","last_name":"Ramasamy","domain_name":"rmit","page_name":"SubramanianRamasamy","display_name":"Subramanian Ramasamy","profile_url":"https://rmit.academia.edu/SubramanianRamasamy?f_ri=3413","photo":"https://0.academia-photos.com/25106050/9794378/10912904/s65_subramanian.ramasamy.jpg"}],"research_interests":[{"id":88,"name":"Aerospace 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The delivery robot is capable of navigating through a cluttered space environment from a home location to a destination point while avoiding obstacles... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_44386769" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">The autonomous delivery robot is meant to be a substitute for a goods delivery person. The delivery robot is capable of navigating through a cluttered space environment from a home location to a destination point while avoiding obstacles in the process. It uses an ultrasonic sensor to detect if anything has been placed inside the bin and only once when something is placed inside, the robot starts moving from a position to another. Bluetooth beacons are used to represent the start, end and mid-point which will be used by the robot to differentiate the specified locations. Bluetooth beacons will act as a terminal for the robot to detect. The autonomous robot will move on an undesignated path i.e. an unmarked route unlike the archaic robots only capable of moving inside a marked black lane. The robot will be trained and multiple simulations are run on it with the help of the DonkeyCar library so it can successfully avoid obstacles and relay the path with efficacy, making the delivery fast and efficient. A Pi camera is used which will help rectify the unmarked desired path over which the robot has to move.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/44386769" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="c9d9c3cbc6c2fcd96494d579e15f4080" rel="nofollow" data-download="{"attachment_id":64790892,"asset_id":44386769,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/64790892/download_file?st=MTczMjQxMTY2OSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="118747740" href="https://mpstme.academia.edu/DevangDave">Devang Dave</a><script data-card-contents-for-user="118747740" type="text/json">{"id":118747740,"first_name":"Devang","last_name":"Dave","domain_name":"mpstme","page_name":"DevangDave","display_name":"Devang Dave","profile_url":"https://mpstme.academia.edu/DevangDave?f_ri=3413","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_44386769 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="44386769"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 44386769, container: ".js-paper-rank-work_44386769", }); 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Civil and criminal liability, privacy and data protection, digital forensicsand cybersecurity, intellectual property. Editors: Claudio Artusio, Monica A. Senor.... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_19520711" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Legal issues of service robotics and drones from an Italian perspective. Civil and criminal liability, privacy and data protection, digital forensicsand cybersecurity, intellectual property.<br />Editors: Claudio Artusio, Monica A. Senor.<br />Authors: Mauro Alovisio, Carlo Blengino, Marco Ciurcina, Giovanni B. Gallus, Guido Noto La Diega, Ugo Pagallo,<br />Massimo Travostino, Giuseppe Vaciago, Paolo Zampella.<br />Thanks for their contribution to Gian Piero Fici, Alessandro Mantelero, Federico Morando.<br /><br />Ricognizione dell'assetto normativo rilevante nell'ambito della robotica di servizio: stato dell'arte e prime raccomandazioni di policy in una prospettiva multidisciplinare.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/19520711" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="676774336993785858018f9e2eea0ce0" rel="nofollow" data-download="{"attachment_id":40668290,"asset_id":19520711,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/40668290/download_file?st=MTczMjQxMTY2OSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="736533" href="https://strathclyde.academia.edu/GuidoNotoLaDiega">Guido Noto La Diega</a><script data-card-contents-for-user="736533" type="text/json">{"id":736533,"first_name":"Guido","last_name":"Noto La Diega","domain_name":"strathclyde","page_name":"GuidoNotoLaDiega","display_name":"Guido Noto La Diega","profile_url":"https://strathclyde.academia.edu/GuidoNotoLaDiega?f_ri=3413","photo":"https://0.academia-photos.com/736533/251443/111612223/s65_guido.noto_la_diega.jpg"}</script></span></span><span class="u-displayInlineBlock InlineList-item-text"> and <span class="u-textDecorationUnderline u-clickable InlineList-item-text js-work-more-authors-19520711">+3</span><div class="hidden js-additional-users-19520711"><div><span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a href="https://independent.academia.edu/MAlovisio">Mauro Alovisio</a></span></div><div><span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a href="https://independent.academia.edu/GiovanniBattistaGallus">Giovanni Battista Gallus</a></span></div><div><span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a href="https://independent.academia.edu/MassimoTravostino">Massimo Travostino</a></span></div></div></span><script>(function(){ var popoverSettings = { el: $('.js-work-more-authors-19520711'), placement: 'bottom', hide_delay: 200, html: true, content: function(){ return $('.js-additional-users-19520711').html(); 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Gallus, Guido Noto La Diega, Ugo Pagallo,\nMassimo Travostino, Giuseppe Vaciago, Paolo Zampella.\nThanks for their contribution to Gian Piero Fici, Alessandro Mantelero, Federico Morando.\n\nRicognizione dell'assetto normativo rilevante nell'ambito della robotica di servizio: stato dell'arte e prime raccomandazioni di policy in una prospettiva multidisciplinare.\n","downloadable_attachments":[{"id":40668290,"asset_id":19520711,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":736533,"first_name":"Guido","last_name":"Noto La Diega","domain_name":"strathclyde","page_name":"GuidoNotoLaDiega","display_name":"Guido Noto La Diega","profile_url":"https://strathclyde.academia.edu/GuidoNotoLaDiega?f_ri=3413","photo":"https://0.academia-photos.com/736533/251443/111612223/s65_guido.noto_la_diega.jpg"},{"id":31484436,"first_name":"Mauro","last_name":"Alovisio","domain_name":"independent","page_name":"MAlovisio","display_name":"Mauro 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(IoT)","url":"https://www.academia.edu/Documents/in/Internet_of_Things_IoT_?f_ri=3413"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_31096854 coauthored" data-work_id="31096854" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/31096854/Artificial_Intelligence_and_the_Good_Society_the_US_EU_and_UK_approach">Artificial Intelligence and the 'Good Society': the US, EU, and UK approach</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">In October 2016, the White House, the European Parliament, and the UK House of Commons each issued a report outlining their visions on how to prepare society for the widespread use of AI. In this article, we provide a comparative... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_31096854" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">In October 2016, the White House, the European Parliament, and the UK House of Commons each issued a report outlining their visions on how to prepare society for the widespread use of AI. In this article, we provide a comparative assessment of these three reports in order to facilitate the design of policies favourable to the development of a 'good AI society'. To do so, we examine how each report addresses the following three topics: (a) the development of a 'good AI society'; (b) the role and responsibility of the government, the private sector, and the research community (including academia) in pursuing such a development; and (c) where the recommendations to support such a development may be in need of improvement. Our analysis concludes that the reports address adequately various ethical, social, and economic topics, but come short of providing an overarching political vision and long-term strategy for the development of a 'good AI society'. In order to contribute to fill this gap, in the conclusion we suggest a two-pronged approach.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/31096854" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="da809726ec3888bcfdab9840096a74e4" rel="nofollow" data-download="{"attachment_id":51531766,"asset_id":31096854,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/51531766/download_file?st=MTczMjQxMTY2OSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="16582545" href="https://oxford.academia.edu/CorinneCath">Corinne Cath</a><script data-card-contents-for-user="16582545" type="text/json">{"id":16582545,"first_name":"Corinne","last_name":"Cath","domain_name":"oxford","page_name":"CorinneCath","display_name":"Corinne Cath","profile_url":"https://oxford.academia.edu/CorinneCath?f_ri=3413","photo":"https://0.academia-photos.com/16582545/11046051/18056580/s65_corinne.cath.jpg"}</script></span></span><span class="u-displayInlineBlock InlineList-item-text"> and <span class="u-textDecorationUnderline u-clickable InlineList-item-text js-work-more-authors-31096854">+4</span><div class="hidden js-additional-users-31096854"><div><span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a href="https://oxford.academia.edu/SandraWachter">Sandra Wachter</a></span></div><div><span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a href="https://oxford.academia.edu/BrentMittelstadt">Brent Mittelstadt</a></span></div><div><span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a href="https://oxford.academia.edu/MariaTadeo">Mariarosaria Taddeo</a></span></div><div><span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a href="https://yale.academia.edu/LucianoFloridi">Luciano Floridi</a></span></div></div></span><script>(function(){ var popoverSettings = { el: $('.js-work-more-authors-31096854'), placement: 'bottom', hide_delay: 200, html: true, content: function(){ return $('.js-additional-users-31096854').html(); 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u-pv7x u-mb0x js-work-card work_25063138 coauthored" data-work_id="25063138" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/25063138/Should_we_campaign_against_sex_robots">Should we campaign against sex robots?</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">In September 2015 a well-publicised Campaign Against Sex Robots (CASR) was launched. Modelled on the longer-standing Campaign to Stop Killer Robots, the CASR opposes the development of sex robots on the grounds that the technology is... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_25063138" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">In September 2015 a well-publicised Campaign Against Sex Robots  (CASR) was launched. Modelled on the longer-standing Campaign to Stop Killer Robots, the CASR opposes the development of sex robots on the grounds that the technology is being developed with a particular model of female-male relations (the prostitute-john model) in mind, and that this will prove harmful in various ways. In this chapter, we consider carefully the merits of campaigning against such a technology. We make three main arguments. First, we argue that the particular claims advanced by the CASR are unpersuasive, partly due to a lack of clarity about the campaign’s aims and partly due to substantive defects in the main ethical objections put forward by campaign’s founder(s). Second, broadening our inquiry beyond the arguments proferred by the campaign itself, we argue that it would be very difficult to endorse a general campaign against sex robots unless one embraced a highly conservative attitude towards the ethics of sex, which is likely to be unpalatable to those who are active in the campaign. In making this argument we draw upon lessons from the campaign against killer robots. Finally, we conclude by suggesting that although a generalised campaign against sex robots is unwarranted, there are legitimate concerns that one can raise about the development of sex robots.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/25063138" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="91e1bb2e26af67b88016e5c73b816825" rel="nofollow" data-download="{"attachment_id":50127659,"asset_id":25063138,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/50127659/download_file?st=MTczMjQxMTY2OSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="711609" href="https://universityofgalway.academia.edu/JohnDanaher">John Danaher</a><script data-card-contents-for-user="711609" type="text/json">{"id":711609,"first_name":"John","last_name":"Danaher","domain_name":"universityofgalway","page_name":"JohnDanaher","display_name":"John Danaher","profile_url":"https://universityofgalway.academia.edu/JohnDanaher?f_ri=3413","photo":"https://0.academia-photos.com/711609/241092/17297863/s65_john.danaher.jpg"}</script></span></span><span class="u-displayInlineBlock InlineList-item-text"> and <span class="u-textDecorationUnderline u-clickable InlineList-item-text js-work-more-authors-25063138">+1</span><div class="hidden js-additional-users-25063138"><div><span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a href="https://oxford.academia.edu/BrianDEarp">Brian D . 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type="text/json">{"id":3413,"name":"Robot Vision","url":"https://www.academia.edu/Documents/in/Robot_Vision?f_ri=3413","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="21593" href="https://www.academia.edu/Documents/in/Artificial_Inteligence">Artificial Inteligence</a>, <script data-card-contents-for-ri="21593" type="text/json">{"id":21593,"name":"Artificial Inteligence","url":"https://www.academia.edu/Documents/in/Artificial_Inteligence?f_ri=3413","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="26138" href="https://www.academia.edu/Documents/in/Utopianism">Utopianism</a><script data-card-contents-for-ri="26138" type="text/json">{"id":26138,"name":"Utopianism","url":"https://www.academia.edu/Documents/in/Utopianism?f_ri=3413","nofollow":false}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=37506455]'), work: {"id":37506455,"title":"Robert Owen 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Zeka","url":"https://www.academia.edu/Documents/in/Yapay_Zeka?f_ri=3413"},{"id":250353,"name":"Komunisme","url":"https://www.academia.edu/Documents/in/Komunisme?f_ri=3413"},{"id":281051,"name":"Türkiye'de Teknoloji ve İnovasyon","url":"https://www.academia.edu/Documents/in/Turkiyede_Teknoloji_ve_Inovasyon?f_ri=3413"},{"id":358251,"name":"Komünizmin teorik altyapısı","url":"https://www.academia.edu/Documents/in/Komunizmin_teorik_altyapisi?f_ri=3413"},{"id":377749,"name":"Sosyalizm","url":"https://www.academia.edu/Documents/in/Sosyalizm?f_ri=3413"},{"id":398267,"name":"İnovasyon","url":"https://www.academia.edu/Documents/in/Inovasyon?f_ri=3413"},{"id":408303,"name":"Makine Kırıcılığı","url":"https://www.academia.edu/Documents/in/Makine_Kiriciligi?f_ri=3413"},{"id":476168,"name":"Robert Owen","url":"https://www.academia.edu/Documents/in/Robert_Owen?f_ri=3413"},{"id":751155,"name":"Inovation and Opportunity Research","url":"https://www.academia.edu/Documents/in/Inovation_and_Opportunity_Research?f_ri=3413"},{"id":775235,"name":"ütopya","url":"https://www.academia.edu/Documents/in/utopya?f_ri=3413"},{"id":1296130,"name":"Endüstriyel Robotlar","url":"https://www.academia.edu/Documents/in/Endustriyel_Robotlar?f_ri=3413"},{"id":1335783,"name":"Inovasyon Yönetimi","url":"https://www.academia.edu/Documents/in/Inovasyon_Yonetimi?f_ri=3413"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_1232567" data-work_id="1232567" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/1232567/A_versatile_camera_calibration_technique_for_high_accuracy_3D_machine_vision_metrology_using_off_the_shelf_TV_cameras_and_lenses">A versatile camera calibration technique for high-accuracy 3D machine vision metrology using off-the-shelf TV cameras and lenses</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">A new technique for three-dimensional (3D) camera calibration for machine vision metrology using off-the-shelf TV cameras and lenses is described. The two-stage technique is aimed at efficient computation of camera external position and... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_1232567" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">A new technique for three-dimensional (3D) camera calibration for machine vision metrology using off-the-shelf TV cameras and lenses is described. The two-stage technique is aimed at efficient computation of camera external position and orientation relative to object reference coordinate system as well as the effective focal length, radial lens distortion, and image scanning parameters. The two-stage technique has advantage in terms of accuracy, speed, and versatility over existing state of the art. A critical review of the state of the art is given in the beginning. A theoretical framework is established, supported by comprehensive proof in five appendixes, and may pave the way for future research on 3D robotics vision. Test results using real data are described. Both accuracy and speed are reported. The experimental results are analyzed and compared with theoretical prediction. Recent effort indicates that with slight modification, the two-stage calibration can be done in real time.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/1232567" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="f2e64a34e7d3a157d3c86fc5a9a46b34" rel="nofollow" data-download="{"attachment_id":19112410,"asset_id":1232567,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/19112410/download_file?st=MTczMjQxMTY2OSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="1965529" href="https://independent.academia.edu/ErshadKarimi">Ershad Karimi</a><script data-card-contents-for-user="1965529" type="text/json">{"id":1965529,"first_name":"Ershad","last_name":"Karimi","domain_name":"independent","page_name":"ErshadKarimi","display_name":"Ershad Karimi","profile_url":"https://independent.academia.edu/ErshadKarimi?f_ri=3413","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_1232567 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="1232567"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 1232567, container: ".js-paper-rank-work_1232567", }); 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$(".js-view-count[data-work-id=1232567]").text(description); $(".js-view-count-work_1232567").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_1232567").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="1232567"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">12</a>  </div><span class="InlineList-item-text u-textTruncate u-pl10x"><a class="InlineList-item-text" data-has-card-for-ri="3413" href="https://www.academia.edu/Documents/in/Robot_Vision">Robot Vision</a>, <script data-card-contents-for-ri="3413" type="text/json">{"id":3413,"name":"Robot Vision","url":"https://www.academia.edu/Documents/in/Robot_Vision?f_ri=3413","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="12901" href="https://www.academia.edu/Documents/in/TV">TV</a>, <script data-card-contents-for-ri="12901" type="text/json">{"id":12901,"name":"TV","url":"https://www.academia.edu/Documents/in/TV?f_ri=3413","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="14417" href="https://www.academia.edu/Documents/in/Machine_Vision">Machine Vision</a>, <script data-card-contents-for-ri="14417" type="text/json">{"id":14417,"name":"Machine Vision","url":"https://www.academia.edu/Documents/in/Machine_Vision?f_ri=3413","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="52545" href="https://www.academia.edu/Documents/in/Metrology">Metrology</a><script data-card-contents-for-ri="52545" type="text/json">{"id":52545,"name":"Metrology","url":"https://www.academia.edu/Documents/in/Metrology?f_ri=3413","nofollow":false}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=1232567]'), work: {"id":1232567,"title":"A versatile camera calibration technique for high-accuracy 3D machine vision metrology using off-the-shelf TV cameras and lenses","created_at":"2012-06-17T22:34:05.630-07:00","url":"https://www.academia.edu/1232567/A_versatile_camera_calibration_technique_for_high_accuracy_3D_machine_vision_metrology_using_off_the_shelf_TV_cameras_and_lenses?f_ri=3413","dom_id":"work_1232567","summary":"A new technique for three-dimensional (3D) camera calibration for machine vision metrology using off-the-shelf TV cameras and lenses is described. The two-stage technique is aimed at efficient computation of camera external position and orientation relative to object reference coordinate system as well as the effective focal length, radial lens distortion, and image scanning parameters. The two-stage technique has advantage in terms of accuracy, speed, and versatility over existing state of the art. A critical review of the state of the art is given in the beginning. A theoretical framework is established, supported by comprehensive proof in five appendixes, and may pave the way for future research on 3D robotics vision. Test results using real data are described. Both accuracy and speed are reported. The experimental results are analyzed and compared with theoretical prediction. 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Volume 1- CONTROL AND PERFORMANCE</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">IET indexes its books and journal in SCOPUS and IEEE Xplore. Computer Vision (CV) and Sensors play a decisive role in the operation of Unmanned Aerial Vehicle (UAV), but there exists a void when it comes to analysing the extent of their... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_34205003" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">IET indexes its books and journal in SCOPUS and IEEE Xplore. <br />Computer Vision (CV) and Sensors play a decisive role in the operation of Unmanned Aerial Vehicle (UAV), but there exists a void when it comes to analysing the extent of their impact on the entire UAV system. In general, the fact that a UAV is a Cyber-Physical System (CPS) is not taken into account. In this proposal, we propose to expand on earlier books covering the use of CV and sensing in UAVs. Among other things, an entirely autonomous UAV can help to (i) obtain information about the environment, (ii) work for an extended period of time without human interference, (iii) move either all or part of itself all over its operating location devoid of human help and (iv) stay away from dangerous situations for people and their possessions. A Vision System (VS) entails the way CV data will be utilized, the appropriate architecture for total avionics integration, the control interfaces, and the UAV operation. Since the VS core is its sensors and cameras, multi-sensor fusion, navigation, hazard detection, and ground correlation in real time are important operational aspects that can benefit from CV knowledge and technology. This book will aim to collect and shed some light on the existing information on CV software and hardware for UAVs as well as pinpoint aspects that need additional thinking. It will list standards and a set of prerequisites (or lack of them thereof) when it comes to CV deployment in UAVs. The issue of data fusion takes a centre place when the book explores ways to deal with sensor data and images as well as their integration and display. The best practices to fuse image and sensor information to enhance UAV performance by means of CV can greatly improve all aspects of the corresponding CPS. The CPS viewpoint can improve the way UAVs interact with the Internet of Things (IoT), use cloud computing, meet communications requirements, implement hardware/software paradigms necessary to handle video streaming, incorporate satellite data, and combine CV with Virtual/Augmented Realities. <br /> <br />VOLUME 1-CONTROL AND PERFORMANCE: This tome explores how sensors and computer vision technologies are used in unmanned aerial vehicles for the navigation, control, stability, reliability, guidance, fault detection, self-maintenance, strategic replanning and reconfiguration of the entire system. It helps analyse the manner UAVs interact with the Internet of Things (IoT), use cloud computing, meet communications requirements, implement hardware/software paradigms necessary to handle still imagery, video streaming, incorporate satellite data, and combine computer vision with virtual/augmented realities (VR/AR).NB: This is planned to be the companion volume of Estrela, Hemanth, Saotome (Eds) / Imaging and Sensing for Unmanned Aerial Vehicles: Volume 2-Deployment and Applications</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/34205003" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="64757e837be3f075369efafaf09d61a7" rel="nofollow" data-download="{"attachment_id":54121803,"asset_id":34205003,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/54121803/download_file?st=MTczMjQxMTY2OSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="1569445" href="https://uff.academia.edu/VaniaEstrela">Vania V Estrela</a><script data-card-contents-for-user="1569445" type="text/json">{"id":1569445,"first_name":"Vania","last_name":"Estrela","domain_name":"uff","page_name":"VaniaEstrela","display_name":"Vania V Estrela","profile_url":"https://uff.academia.edu/VaniaEstrela?f_ri=3413","photo":"https://0.academia-photos.com/1569445/584405/780232/s65_vania_v..estrela.jpg"}</script></span></span></li><li class="js-paper-rank-work_34205003 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="34205003"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 34205003, container: ".js-paper-rank-work_34205003", }); 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Volume 1- CONTROL AND PERFORMANCE","created_at":"2017-08-11T15:44:32.275-07:00","url":"https://www.academia.edu/34205003/1st_IET_Call_for_Book_Chapters_IMAGE_AND_SENSING_FOR_UNMANNED_AERIAL_VEHICLES_Volume_1_CONTROL_AND_PERFORMANCE?f_ri=3413","dom_id":"work_34205003","summary":"IET indexes its books and journal in SCOPUS and IEEE Xplore.\r\nComputer Vision (CV) and Sensors play a decisive role in the operation of Unmanned Aerial Vehicle (UAV), but there exists a void when it comes to analysing the extent of their impact on the entire UAV system. In general, the fact that a UAV is a Cyber-Physical System (CPS) is not taken into account. In this proposal, we propose to expand on earlier books covering the use of CV and sensing in UAVs. Among other things, an entirely autonomous UAV can help to (i) obtain information about the environment, (ii) work for an extended period of time without human interference, (iii) move either all or part of itself all over its operating location devoid of human help and (iv) stay away from dangerous situations for people and their possessions. A Vision System (VS) entails the way CV data will be utilized, the appropriate architecture for total avionics integration, the control interfaces, and the UAV operation. Since the VS core is its sensors and cameras, multi-sensor fusion, navigation, hazard detection, and ground correlation in real time are important operational aspects that can benefit from CV knowledge and technology. This book will aim to collect and shed some light on the existing information on CV software and hardware for UAVs as well as pinpoint aspects that need additional thinking. It will list standards and a set of prerequisites (or lack of them thereof) when it comes to CV deployment in UAVs. The issue of data fusion takes a centre place when the book explores ways to deal with sensor data and images as well as their integration and display. The best practices to fuse image and sensor information to enhance UAV performance by means of CV can greatly improve all aspects of the corresponding CPS. The CPS viewpoint can improve the way UAVs interact with the Internet of Things (IoT), use cloud computing, meet communications requirements, implement hardware/software paradigms necessary to handle video streaming, incorporate satellite data, and combine CV with Virtual/Augmented Realities. \r\n\r\nVOLUME 1-CONTROL AND PERFORMANCE: This tome explores how sensors and computer vision technologies are used in unmanned aerial vehicles for the navigation, control, stability, reliability, guidance, fault detection, self-maintenance, strategic replanning and reconfiguration of the entire system. It helps analyse the manner UAVs interact with the Internet of Things (IoT), use cloud computing, meet communications requirements, implement hardware/software paradigms necessary to handle still imagery, video streaming, incorporate satellite data, and combine computer vision with virtual/augmented realities (VR/AR).NB: This is planned to be the companion volume of Estrela, Hemanth, Saotome (Eds) / Imaging and Sensing for Unmanned Aerial Vehicles: Volume 2-Deployment and Applications","downloadable_attachments":[{"id":54121803,"asset_id":34205003,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":1569445,"first_name":"Vania","last_name":"Estrela","domain_name":"uff","page_name":"VaniaEstrela","display_name":"Vania V Estrela","profile_url":"https://uff.academia.edu/VaniaEstrela?f_ri=3413","photo":"https://0.academia-photos.com/1569445/584405/780232/s65_vania_v..estrela.jpg"}],"research_interests":[{"id":50,"name":"Electronic 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})();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_36656466" data-work_id="36656466" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/36656466/Translating_Videos_to_Commands_for_Robotic_Manipulation_with_Deep_Recurrent_Neural_Networks">Translating Videos to Commands for Robotic Manipulation with Deep Recurrent Neural Networks</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">We present a new method to translate videos to commands for robotic manipulation using Deep Recurrent Neural Networks (RNN). Our framework first extracts deep features from the input video frames with a deep Convolutional Neural Networks... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_36656466" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">We present a new method to translate videos to commands for robotic manipulation using Deep Recurrent Neural Networks (RNN). Our framework first extracts deep features from the input video frames with a deep Convolutional Neural Networks (CNN). Two RNN layers with an encoder-decoder architecture are then used to encode the visual features and sequentially generate the output words as the command. We demonstrate that the translation accuracy can be improved by allowing a smooth transaction between two RNN layers and using the state-of-the-art feature extractor. The experimental results on our new challenging dataset show that our approach outperforms recent methods by a fair margin. Furthermore, we combine the proposed translation module with the vision and planning system to let a robot perform various manipulation tasks. Finally, we demonstrate the effectiveness of our framework on a full-size humanoid robot WALK-MAN.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/36656466" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="862d74aac5343f5e6241a483e1235907" rel="nofollow" data-download="{"attachment_id":56589751,"asset_id":36656466,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/56589751/download_file?st=MTczMjQxMTY2OSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="2613253" href="https://ucl.academia.edu/DimitriosKanoulas">Dimitrios Kanoulas</a><script data-card-contents-for-user="2613253" type="text/json">{"id":2613253,"first_name":"Dimitrios","last_name":"Kanoulas","domain_name":"ucl","page_name":"DimitriosKanoulas","display_name":"Dimitrios Kanoulas","profile_url":"https://ucl.academia.edu/DimitriosKanoulas?f_ri=3413","photo":"https://0.academia-photos.com/2613253/826405/37226025/s65_dimitrios.kanoulas.jpg"}</script></span></span></li><li class="js-paper-rank-work_36656466 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="36656466"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 36656466, container: ".js-paper-rank-work_36656466", }); 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Our framework first extracts deep features from the input video frames with a deep Convolutional Neural Networks (CNN). Two RNN layers with an encoder-decoder architecture are then used to encode the visual features and sequentially generate the output words as the command. We demonstrate that the translation accuracy can be improved by allowing a smooth transaction between two RNN layers and using the state-of-the-art feature extractor. The experimental results on our new challenging dataset show that our approach outperforms recent methods by a fair margin. Furthermore, we combine the proposed translation module with the vision and planning system to let a robot perform various manipulation tasks. 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Confirmation Theory","url":"https://www.academia.edu/Documents/in/Bayesian_Confirmation_Theory?f_ri=3413"},{"id":2699021,"name":"Bayesian theory","url":"https://www.academia.edu/Documents/in/Bayesian_theory?f_ri=3413"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_6984608" data-work_id="6984608" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/6984608/Design_of_Kinematic_Controller_for_Real_time_Vision_Guided_Robot_Manipulators">Design of Kinematic Controller for Real-time Vision Guided Robot Manipulators</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">This paper discusses the control strategies for robot manipulators to track moving targets based on realtime vision guidance. The work is motivated by some new demands of vision guided robot manipulators in which the workpieces being... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_6984608" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">This paper discusses the control strategies for robot manipulators to track moving targets based on realtime vision guidance. The work is motivated by some new demands of vision guided robot manipulators in which the workpieces being manipulated are in complex motion and the widely adopted look-then-move control strategy cannot give satisfactory performance. In order to serve industrial applications, the limited sensing and actuation capabilities have to be considered properly. In our work, a cascade control structure is introduced. The sensor and actuator limits are dealt with by consecutive modules in the controller respectively. In particular, the kinematic visual servoing (KVS) module is discussed in detail. It is a kinematic controller that generates reference trajectory in real-time. Sliding control is used to give a basic Jacobian-based design. Constrained optimal control is applied to address the actuator limits. validation is conducted through simulation and experiment.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/6984608" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="331b7dd1e36fd6ba9d07e2b2cb3e24bd" rel="nofollow" data-download="{"attachment_id":33650246,"asset_id":6984608,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/33650246/download_file?st=MTczMjQxMTY2OSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="4895190" href="https://njit.academia.edu/CongWang">Cong Wang</a><script data-card-contents-for-user="4895190" type="text/json">{"id":4895190,"first_name":"Cong","last_name":"Wang","domain_name":"njit","page_name":"CongWang","display_name":"Cong Wang","profile_url":"https://njit.academia.edu/CongWang?f_ri=3413","photo":"https://0.academia-photos.com/4895190/2107889/10796424/s65_cong.wang.jpg"}</script></span></span></li><li class="js-paper-rank-work_6984608 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="6984608"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 6984608, container: ".js-paper-rank-work_6984608", }); 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The work is motivated by some new demands of vision guided robot manipulators in which the workpieces being manipulated are in complex motion and the widely adopted look-then-move control strategy cannot give satisfactory performance. In order to serve industrial applications, the limited sensing and actuation capabilities have to be considered properly. In our work, a cascade control structure is introduced. The sensor and actuator limits are dealt with by consecutive modules in the controller respectively. In particular, the kinematic visual servoing (KVS) module is discussed in detail. It is a kinematic controller that generates reference trajectory in real-time. Sliding control is used to give a basic Jacobian-based design. 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