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Explore the widespread application of intelligence and robots in various fields, such as intelligent control, biomedical artificial intelligence, industrial artificial intelligence, smart agriculture, intelligent transportation, intelligent communication, and smart cities. Promote research and application innovation in the fields of intelligence and robotics."><meta data-n-head="ssr" name="keywords" content=""><meta data-n-head="ssr" name="twitter:title" content="Intelligence & Robotics - OAE Publishing Inc."><meta data-n-head="ssr" name="twitter:type" content="OAE Publishing Inc."><meta data-n-head="ssr" name="twitter:description" content="Intelligence & Robotics is a peer-reviewed journal led by Editor-in-Chief Simon X. Yang, covering intelligent algorithms such as neural networks, swarm intelligence, evolutionary algorithms, etc. Focus on mobile, airborne, and underwater robot systems, including multi-sensor fusion, design modeling, data analysis, positioning, planning, and control, as well as robot collaboration, remote operation, and human-machine interaction. Explore the widespread application of intelligence and robots in various fields, such as intelligent control, biomedical artificial intelligence, industrial artificial intelligence, smart agriculture, intelligent transportation, intelligent communication, and smart cities. 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Robotics</h1> <div class="wrapper" data-v-9d35a14e><div aria-label="Breadcrumb" role="navigation" class="el-breadcrumb" data-v-9d35a14e><span class="el-breadcrumb__item" data-v-9d35a14e><span role="link" class="el-breadcrumb__inner is-link">Journals</span><span role="presentation" class="el-breadcrumb__separator"></span></span> <span class="el-breadcrumb__item" style="text-transform:uppercase;" data-v-9d35a14e><span role="link" class="el-breadcrumb__inner">ir</span><span role="presentation" class="el-breadcrumb__separator"></span></span></div> <div class="el-row" style="margin-left:-10px;margin-right:-10px;" data-v-9d35a14e><div class="el-col el-col-24 el-col-xs-24 el-col-sm-16 el-col-md-18" style="padding-left:10px;padding-right:10px;" data-v-9d35a14e><div data-v-c1f41ab4 data-v-9d35a14e><div class="banner_box" data-v-c1f41ab4><div class="grid-content banner_cont pad_r_10" data-v-c1f41ab4><div class="banner_box_b" data-v-c1f41ab4> <div class="swiper-container" data-v-5c8e3c6d data-v-c1f41ab4><div class="swiper-wrapper" data-v-5c8e3c6d><div class="swiper-slide" data-v-5c8e3c6d><a href="https://www.raiic.org/" data-v-5c8e3c6d><div class="tit" data-v-5c8e3c6d><div class="title" data-v-5c8e3c6d><a href="https://www.raiic.org/" data-v-5c8e3c6d><span data-v-5c8e3c6d><em>IR</em> & RAIIC 2025</span></a></div> <div class="title_sm" data-v-5c8e3c6d><span data-v-5c8e3c6d>July 4-6, 2025 | Chengdu, China</span></div></div> <img src="https://i.oaes.cc/uploads/20250213/2fac968b67a746ab80bbb38f66a712a3.jpg" alt data-v-5c8e3c6d></a></div><div class="swiper-slide" data-v-5c8e3c6d><a href="https://www.oaepublish.com/news/ir.1026" data-v-5c8e3c6d><div class="tit" data-v-5c8e3c6d><div class="title" data-v-5c8e3c6d><a href="https://www.oaepublish.com/news/ir.1026" data-v-5c8e3c6d><span data-v-5c8e3c6d>Congratulations!</span></a></div> <div class="title_sm" data-v-5c8e3c6d><span data-v-5c8e3c6d><em>Intelligence & Robotics</em> (<em>IR</em>) is delighted to announce the recipients of the Best Editorial Board Member Award and Best Junior Editorial Board Member Award in 2024.</span></div></div> <img src="https://i.oaes.cc/uploads/20250125/4a02b83eade7425487e1689d017b38bd.jpg" alt data-v-5c8e3c6d></a></div><div class="swiper-slide" data-v-5c8e3c6d><a href="https://www.oaepublish.com/news/ir.976" data-v-5c8e3c6d><div class="tit" data-v-5c8e3c6d><div class="title" data-v-5c8e3c6d><a href="https://www.oaepublish.com/news/ir.976" data-v-5c8e3c6d><span data-v-5c8e3c6d>Top 10 Breakthrough Articles in <em>Intelligence & Robotics</em> from 2024 </span></a></div> <div class="title_sm" data-v-5c8e3c6d><span data-v-5c8e3c6d>These articles span innovations in artificial intelligence (AI), robotics control, medical imaging, and collaborative systems, showcasing the journal’s commitment to advancing knowledge and fostering transformative applications. </span></div></div> <img src="https://i.oaes.cc/uploads/20250103/bd9e44db15a9464887ba512ceb539b47.jpg" alt 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Reviewer Award</span></a></div> <div class="title_sm" data-v-5c8e3c6d><span data-v-5c8e3c6d>Best Paper Award: First prize $400, Second prize $300<br>Best Reviewer Award: $300</span></div></div> <img src="https://i.oaes.cc/uploads/20240321/94592826ff73445281be8681602e3e8d.jpg" alt data-v-5c8e3c6d></a></div></div> <div class="swiper-pagination" style="display:;" data-v-5c8e3c6d></div> <div class="swiper-button-prev" style="display:;" data-v-5c8e3c6d><img src="https://g.oaes.cc/oae/nuxt/img/slide-left30x40_@3x.5042b29.png" alt class="btn_swiper" data-v-5c8e3c6d></div> <div class="swiper-button-next" style="display:;" data-v-5c8e3c6d><img src="https://g.oaes.cc/oae/nuxt/img/slide-right30x40_@3x.21cefa8.png" alt class="btn_swiper" data-v-5c8e3c6d></div></div></div></div></div></div> <div class="grid-content index_left pad_r_10" data-v-9d35a14e> <div class="hot_art" data-v-9d35a14e><div data-v-ab8748fc data-v-9d35a14e><div class="PcTitle" data-v-ab8748fc><div class="top_line" 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Simon X. Yang<a href='https://orcid.org/0000-0002-6888-7993' target='_blank'><img src='https://i.oaes.cc/images/orcid.png' class='author_id' alt='Simon X. 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Xianyi Yang</div> <div class="article-block" data-v-09a1383d><span class="block-f17452" data-v-09a1383d>Open Access</span> <span class="btn_line" data-v-09a1383d></span> <span class="block-999" data-v-09a1383d>Research Article</span> <span class="btn_line" data-v-09a1383d></span> <span class="doi_box" data-v-09a1383d> DOI: <a href="/articles/ir.2025.07" data-v-09a1383d>10.20517/ir.2025.07</a></span> <span class="btn_line" data-v-09a1383d></span> <span data-v-09a1383d>4 Feb 2025</span></div> <div class="article_link2" data-v-09a1383d> Views: <i class="el-icon-loading" data-v-09a1383d></i><i class="num_n" data-v-09a1383d></i> <span class="btn_line" data-v-09a1383d></span> Downloads: <i class="el-icon-loading" data-v-09a1383d></i><i class="num_n" data-v-09a1383d></i></div></div>   <div class="article_link" data-v-09a1383d><span data-v-09a1383d><a data-v-09a1383d><b data-v-09a1383d><i class="icon-download icon_right4" data-v-09a1383d></i> Download PDF</b></a></span> <span data-v-09a1383d><a href="/articles/ir.2025.07?to=comment" data-v-09a1383d><i class="comment-l icon-commentl iconfont icon_right4" data-v-09a1383d></i> <i class="el-icon-loading" data-v-09a1383d></i><b data-v-09a1383d></b></a></span> <span data-v-09a1383d><span data-v-09a1383d><div role="tooltip" id="el-popover-3046" aria-hidden="true" class="el-popover el-popper" style="width:170px;display:none;"><div class="icon_share" style="text-align:right;margin:0;" data-v-09a1383d><a href="http://pinterest.com/pin/create/button/?url=&media=&description=https://www.oaepublish.com/articles/ir.2025.07" target="_blank" class="pinterest-sign" data-v-09a1383d><i class="iconfont icon-pinterest" data-v-09a1383d></i></a> <a href="https://www.facebook.com/sharer/sharer.php?u=https://www.oaepublish.com/articles/ir.2025.07" target="_blank" class="facebook-sign" data-v-09a1383d><i aria-hidden="true" class="iconfont icon-facebook" data-v-09a1383d></i></a> <a href="https://twitter.com/intent/tweet?url=https://www.oaepublish.com/articles/ir.2025.07" target="_blank" class="twitter-sign" data-v-09a1383d><i class="iconfont icon-tuite1" data-v-09a1383d></i></a> <a href="https://www.linkedin.com/shareArticle?url=https://www.oaepublish.com/articles/ir.2025.07" target="_blank" class="linkedin-sign" data-v-09a1383d><i class="iconfont icon-linkedin" data-v-09a1383d></i></a></div> </div><span class="el-popover__reference-wrapper"><button type="button" class="el-button colorddd el-button--text el-button--mini" data-v-09a1383d><span><i class="icon-fenxiang iconfont icon_right4" data-v-09a1383d></i> <i class="el-icon-loading" data-v-09a1383d></i><b data-v-09a1383d></b></span></button></span></span></span> <span data-v-09a1383d><span class="no_zan" data-v-09a1383d><i class="icon-like-line icon_right4" data-v-09a1383d></i> <i class="el-icon-loading" data-v-09a1383d></i><i class="num_n" data-v-09a1383d><b data-v-09a1383d></b></i></span></span></div></div> <div class="article_right" data-v-09a1383d><img alt src="https://i.oaes.cc/uploads/20250205/7c46da16b753459a90f72419852df9dc.png" data-itemid="7712" data-itemhref="https://v.oaes.cc/uploads/20250205/8f7e8f9e9a82401c8f54cc376d8c36d6.mp4" data-itemimg="https://i.oaes.cc/uploads/20250205/7c46da16b753459a90f72419852df9dc.png" class="video_img" data-v-09a1383d> <i data-itemid="7712" data-itemhref="https://v.oaes.cc/uploads/20250205/8f7e8f9e9a82401c8f54cc376d8c36d6.mp4" data-itemimg="https://i.oaes.cc/uploads/20250205/7c46da16b753459a90f72419852df9dc.png" class="bo_icon" data-v-09a1383d></i></div> <div alt="" data-v-98672444 data-v-09a1383d><div class="imgDolg" style="display:none;" data-v-98672444><div class="img_btn_box" data-v-98672444><i class="el-icon-error img_btn" data-v-98672444></i> <i class="el-icon-circle-plus img_btn" data-v-98672444></i> <i class="el-icon-remove img_btn" data-v-98672444></i></div> <div class="img" data-v-98672444><img alt="" style="transform:scale(1);" data-v-98672444> <div class="mo_btn" data-v-98672444><i class="el-icon-error img_btn" data-v-98672444></i></div></div></div></div></div><div class="article_item" data-v-09a1383d data-v-035d42c0><div class="article_left" data-v-09a1383d><div class="article_header" data-v-09a1383d><a href="/articles/ir.2025.06" data-v-09a1383d><h4 class="art_head" data-v-09a1383d>Stackelberg game-based anti-disturbance control for unmanned surface vessels via integrative reinforcement learning</h4></a></div>  <div class="text_cont" data-v-09a1383d><div class="article_author font-999" data-v-09a1383d>Yizhen Meng, ... 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<div class="article_right" data-v-09a1383d><img alt src="https://i.oaes.cc/uploads/20240205/c594ecf74c2a433e90b38a7211bdd0e7.jpg" data-itemid="5329" data-itemhref="https://v1.oaepublish.com/files/talkvideo/5329.mp4" data-itemimg="https://i.oaes.cc/uploads/20240205/c594ecf74c2a433e90b38a7211bdd0e7.jpg" class="video_img" data-v-09a1383d> <i data-itemid="5329" data-itemhref="https://v1.oaepublish.com/files/talkvideo/5329.mp4" data-itemimg="https://i.oaes.cc/uploads/20240205/c594ecf74c2a433e90b38a7211bdd0e7.jpg" class="bo_icon" data-v-09a1383d></i></div> <div alt="" data-v-98672444 data-v-09a1383d><div class="imgDolg" style="display:none;" data-v-98672444><div class="img_btn_box" data-v-98672444><i class="el-icon-error img_btn" data-v-98672444></i> <i class="el-icon-circle-plus img_btn" data-v-98672444></i> <i class="el-icon-remove img_btn" data-v-98672444></i></div> <div class="img" data-v-98672444><img alt="" style="transform:scale(1);" data-v-98672444> <div class="mo_btn" 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class="title_sm" data-v-5c8e3c6d><span data-v-5c8e3c6d>These articles span innovations in artificial intelligence (AI), robotics control, medical imaging, and collaborative systems, showcasing the journal’s commitment to advancing knowledge and fostering transformative applications. </span></div></div> <img src="https://i.oaes.cc/uploads/20250103/bd9e44db15a9464887ba512ceb539b47.jpg" alt data-v-5c8e3c6d></a></div><div class="swiper-slide" data-v-5c8e3c6d><a href="https://www.oaepublish.com/news/ir.993" data-v-5c8e3c6d><div class="tit" data-v-5c8e3c6d><div class="title" data-v-5c8e3c6d><a href="https://www.oaepublish.com/news/ir.993" data-v-5c8e3c6d><span data-v-5c8e3c6d>Honoring the Contributions of Peer Reviewers </span></a></div> <div class="title_sm" data-v-5c8e3c6d><span data-v-5c8e3c6d>We are delighted to announce the 2024 Annual List of Distinguished Reviewers for <em>Intelligence & Robotics (IR)</em>. </span></div></div> <img 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2024.",href:"https:\u002F\u002Fwww.oaepublish.com\u002Fnews\u002Fir.1026",target:c,img:"https:\u002F\u002Fi.oaes.cc\u002Fuploads\u002F20250125\u002F4a02b83eade7425487e1689d017b38bd.jpg",mobile_img:a},{id:1136,type:p,title:"Top 10 Breakthrough Articles in \u003Cem\u003EIntelligence & Robotics\u003C\u002Fem\u003E from 2024\r\n",sub_title:"These articles span innovations in artificial intelligence (AI), robotics control, medical imaging, and collaborative systems, showcasing the journal’s commitment to advancing knowledge and fostering transformative applications.\r\n",href:"https:\u002F\u002Fwww.oaepublish.com\u002Fnews\u002Fir.976",target:b,img:"https:\u002F\u002Fi.oaes.cc\u002Fuploads\u002F20250103\u002Fbd9e44db15a9464887ba512ceb539b47.jpg",mobile_img:a},{id:1150,type:p,title:"Honoring the Contributions of Peer Reviewers\r\n",sub_title:"We are delighted to announce the 2024 Annual List of Distinguished Reviewers for \u003Cem\u003EIntelligence & Robotics (IR)\u003C\u002Fem\u003E.\r\n",href:"https:\u002F\u002Fwww.oaepublish.com\u002Fnews\u002Fir.993",target:b,img:"https:\u002F\u002Fi.oaes.cc\u002Fuploads\u002F20250114\u002F10441e7b91fa43978e1188c4e407d3e1.jpg",mobile_img:a},{id:786,type:p,title:"Announcement: \u003Ci\u003EIntelligence & Robotics\u003C\u002Fi\u003E Best Paper Award and Best Reviewer Award",sub_title:"Best Paper Award: First prize $400, Second prize $300\u003Cbr\u003EBest Reviewer Award: $300",href:"https:\u002F\u002Fwww.oaepublish.com\u002Fir\u002Fawards",target:c,img:"https:\u002F\u002Fi.oaes.cc\u002Fuploads\u002F20240321\u002F94592826ff73445281be8681602e3e8d.jpg",mobile_img:a}],articles:[{url_doi:"ir.2023.08",title:"ChatGPT in connected and autonomous vehicles: benefits and challenges",id:218},{url_doi:B,title:C,id:167},{url_doi:"ir.2021.16",title:"Facial expression recognition using adapted residual based deep neural network",id:166}],video:{id:386,title:"Special Interview with Prof. Marcelo H. Ang",video_url:"https:\u002F\u002Fv.oaes.cc\u002Fuploads\u002F20240724\u002Fbad86d7bf85849bf94d44614ff4c6268.mp4",img_url:"https:\u002F\u002Fi.oaes.cc\u002Fuploads\u002F20240724\u002Fbc840a5f2a274ae8b5c25f2dfee136f6.jpg",link:"https:\u002F\u002Fwww.oaepublish.com\u002Finterviews\u002Fir.267",journal_id:m,type:"academic",videos_home_url:"\u002Facademic_talks"}},indexMid:{articles:{recent:[{article_id:"7691",title:"Task cognition and planning for service robots",image:O,tag:"119-42",doi:"10.20517\u002Fir.2025.08",date_published:"1738857600",section:i,authors:"Yongcheng Cui, ... Simon X. Yang\u003Ca href='https:\u002F\u002Forcid.org\u002F0000-0002-6888-7993' target='_blank'\u003E\u003Cimg src='https:\u002F\u002Fi.oaes.cc\u002Fimages\u002Forcid.png' class='author_id' alt='Simon X. Yang'\u003E\u003C\u002Fa\u003E",journal_id:g,volume:h,year:q,section_id:b,special_name:a,url_doi:"ir.2025.08",pdf:"https:\u002F\u002Ff.oaes.cc\u002Fxmlpdf\u002F8725948d-9e72-44b9-b975-58656279e74b\u002Fir5008.pdf",video_url:"https:\u002F\u002Fv.oaes.cc\u002Fuploads\u002F20250207\u002F63f930aa71ab4ff78cbb1fac22a561be.mp4",allauthors:"\u003Cimg src='https:\u002F\u002Fi.oaes.cc\u002Fimages\u002Fdefault_photo.png' class='author_photo' alt='Yongcheng Cui'\u003E Yongcheng Cui, \u003Cimg src='https:\u002F\u002Fi.oaes.cc\u002Fimages\u002Fdefault_photo.png' class='author_photo' alt='Ying Zhang'\u003E Ying Zhang\u003Ca href='https:\u002F\u002Forcid.org\u002F0000-0001-8982-8223' target='_blank'\u003E\u003Cimg src='https:\u002F\u002Fi.oaes.cc\u002Fimages\u002Forcid.png' class='author_id' alt='Ying Zhang'\u003E\u003C\u002Fa\u003E, \u003Cimg src='https:\u002F\u002Fi.oaes.cc\u002Fimages\u002Fdefault_photo.png' class='author_photo' alt='Cui-Hua Zhang'\u003E Cui-Hua Zhang, \u003Cimg src='https:\u002F\u002Fi.oaes.cc\u002Fimages\u002Fdefault_photo.png' class='author_photo' alt='Simon X. Yang'\u003E Simon X. Yang\u003Ca href='https:\u002F\u002Forcid.org\u002F0000-0002-6888-7993' target='_blank'\u003E\u003Cimg src='https:\u002F\u002Fi.oaes.cc\u002Fimages\u002Forcid.png' class='author_id' alt='Simon X. Yang'\u003E\u003C\u002Fa\u003E",journal_path:d,video_img:"https:\u002F\u002Fi.oaes.cc\u002Fuploads\u002F20250207\u002F298da598f3d34f8c8b53bea9d9a09ef5.png",image_list:O,abstract:"\u003Cp\u003EWith the rapid development of artificial intelligence and robotics, service robots are increasingly becoming a part of our daily lives to provide domestic services. For robots to complete such services intelligently and with high quality, the prerequisite is that they can recognize and plan tasks to discover task requirements and generate executable action sequences. In this context, this paper systematically reviews the latest research progress in task cognition and planning for domestic service robots, covering key technologies such as command text parsing, active task cognition (ATC), multimodal perception, and action sequence generation. Initially, the challenges traditional rule-based command parsing methods face are analyzed, and the enhancement of robots’ understanding of complex instructions through deep learning methods is explored. Subsequently, the research trends in ATC are introduced, discussing the ability of robots to autonomously discover tasks by perceiving the surrounding environment through visual and semantic features. The discussion then moves to the current typical methods in task planning, comparing and analyzing four common approaches to highlight their advantages and disadvantages in this field. Finally, the paper summarizes the challenges of existing research and the future directions for development, providing references for further enhancing the task execution capabilities of domestic service robots in complex home environments.\u003C\u002Fp\u003E"},{article_id:"7712",title:"SKPNet: snake KAN perceive bridge cracks through semantic segmentation",image:b,tag:"105-18",doi:"10.20517\u002Fir.2025.07",date_published:"1738684800",section:t,authors:"Yudi Ruan, ... Xianyi Yang",journal_id:g,volume:h,year:q,section_id:b,special_name:a,url_doi:"ir.2025.07",pdf:"https:\u002F\u002Ff.oaes.cc\u002Fxmlpdf\u002Fba9dde04-8e35-4852-b204-afac598834ad\u002Fir5007.pdf",video_url:"https:\u002F\u002Fv.oaes.cc\u002Fuploads\u002F20250205\u002F8f7e8f9e9a82401c8f54cc376d8c36d6.mp4",allauthors:"\u003Cimg src='https:\u002F\u002Fi.oaes.cc\u002Fimages\u002Fdefault_photo.png' class='author_photo' alt='Yudi Ruan'\u003E Yudi Ruan, \u003Cimg src='https:\u002F\u002Fi.oaes.cc\u002Fimages\u002Fdefault_photo.png' class='author_photo' alt='Di Wang'\u003E Di Wang\u003Ca href='http:\u002F\u002Forcid.org\u002F0000-0001-9679-7592' target='_blank'\u003E\u003Cimg src='https:\u002F\u002Fi.oaes.cc\u002Fimages\u002Forcid.png' class='author_id' alt='Di Wang'\u003E\u003C\u002Fa\u003E, \u003Cimg src='https:\u002F\u002Fi.oaes.cc\u002Fimages\u002Fdefault_photo.png' class='author_photo' alt='Yijing Yuan'\u003E Yijing Yuan, \u003Cimg src='https:\u002F\u002Fi.oaes.cc\u002Fimages\u002Fdefault_photo.png' class='author_photo' alt='Shixin Jiang'\u003E Shixin Jiang, \u003Cimg src='https:\u002F\u002Fi.oaes.cc\u002Fimages\u002Fdefault_photo.png' class='author_photo' alt='Xianyi Yang'\u003E Xianyi Yang",journal_path:d,video_img:"https:\u002F\u002Fi.oaes.cc\u002Fuploads\u002F20250205\u002F7c46da16b753459a90f72419852df9dc.png",image_list:b,abstract:"\u003Cp\u003EAs the demands for ensuring bridge safety continue to rise, crack detection technology has become more crucial than ever. In this context, deep learning methods have been widely applied in the field of intelligent crack detection for bridges. However, existing methods are often constrained by complex backgrounds and computational limitations, struggling with issues such as weak crack continuity and insufficient detail representation. Inspired by biological mechanisms, a dynamic snake convolution (DSC) with tubular offsets is incorporated to tackle these challenges effectively. Additionally, a channel-wise self-attention (CWSA) mechanism is introduced to efficiently fuse multi-scale features in U-Net, significantly enhancing the ability of the model to capture fine details. In the classification head, the traditional linear layer is replaced with a Kolmogorov-Arnold network (KAN) structure, which strengthens the robustness and generalization capacity of the model. Experimental results demonstrate that the proposed model improves detection accuracy, achieving a mean intersection over union (mIoU) of 0.877, while maintaining almost the same number of parameters, showcasing exceptional performance and practical applicability. Our project is released at \u003Ca target=\"_blank\" href=\"https:\u002F\u002Fgithub.com\u002Fruanyudi\u002FKanSeg-Bi\" xmlns:xlink=\"http:\u002F\u002Fwww.w3.org\u002F1999\u002Fxlink\"\u003Ehttps:\u002F\u002Fgithub.com\u002Fruanyudi\u002FKanSeg-Bi\u003C\u002Fa\u003E.\u003C\u002Fp\u003E"},{article_id:"7680",title:"Stackelberg game-based anti-disturbance control for unmanned surface vessels via integrative reinforcement learning",image:P,tag:"88-104",doi:"10.20517\u002Fir.2025.06",date_published:Q,section:t,authors:"Yizhen Meng, ... Guanbo Jing",journal_id:g,volume:h,year:q,section_id:b,special_name:a,url_doi:"ir.2025.06",pdf:"https:\u002F\u002Ff.oaes.cc\u002Fxmlpdf\u002Fc3a967a3-ac3d-471f-85b5-e34b251968a3\u002Fir5006.pdf",video_url:a,allauthors:"\u003Cimg src='https:\u002F\u002Fi.oaes.cc\u002Fimages\u002Fdefault_photo.png' class='author_photo' alt='Yizhen Meng'\u003E Yizhen Meng, \u003Cimg src='https:\u002F\u002Fi.oaes.cc\u002Fimages\u002Fdefault_photo.png' class='author_photo' alt='Chun Liu'\u003E Chun Liu\u003Ca href='http:\u002F\u002Forcid.org\u002F0000-0002-9294-6519' target='_blank'\u003E\u003Cimg src='https:\u002F\u002Fi.oaes.cc\u002Fimages\u002Forcid.png' class='author_id' alt='Chun Liu'\u003E\u003C\u002Fa\u003E, \u003Cimg src='https:\u002F\u002Fi.oaes.cc\u002Fimages\u002Fdefault_photo.png' class='author_photo' alt='Jing Zhao'\u003E Jing Zhao, \u003Cimg src='https:\u002F\u002Fi.oaes.cc\u002Fimages\u002Fdefault_photo.png' class='author_photo' alt='Jing Huang'\u003E Jing Huang, \u003Cimg src='https:\u002F\u002Fi.oaes.cc\u002Fimages\u002Fdefault_photo.png' class='author_photo' alt='Guanbo Jing'\u003E Guanbo Jing",journal_path:d,video_img:a,image_list:P,abstract:"\u003Cp\u003EIn the navigation of unmanned surface vessels (USVs), external disturbances, particularly ocean waves, frequently induce deviations from the desired trajectory. To mitigate these challenges, we propose a novel disturbance rejection control strategy based on Stackelberg game theory, designed to address unmodeled system dynamics, complex environmental conditions, and other external perturbations. This approach incorporates several key innovations. First, we introduce a velocity error dynamic system coupled with a non-cooperative Stackelberg game model, where the USV's control inputs (as the leader) and external disturbances (as the follower) interact within an alternating update framework. This leader-follower interaction facilitates the joint optimization of both the disturbance rejection and performance-optimal control strategies, enhancing the USV's tracking accuracy while maximizing its disturbance rejection capacity. Second, we rigorously verify the existence of a cooperative optimal solution through an analysis of the Nash equilibrium under sequential decision-making between the leader and follower. Building on this, integral reinforcement learning and neural networks are employed to approximate the optimal Stackelberg solution. The boundedness and convergence of the proposed approach are validated using Lyapunov functions, ensuring stability and optimal performance under dynamic operating conditions. Finally, simulation results confirm the efficacy of the proposed strategy, demonstrating its ability to concurrently optimize control robustness and performance - such as minimizing tracking error and energy consumption - when confronted with unmodeled dynamics and external disturbances.\u003C\u002Fp\u003E"},{article_id:"7679",title:"A novel real-time intelligent detector for monitoring UAVs in live-line operation on 10 kV distribution networks",image:b,tag:"70-87",doi:"10.20517\u002Fir.2025.05",date_published:Q,section:t,authors:"Haibo Duan, ... Qiushi Cui",journal_id:g,volume:h,year:q,section_id:b,special_name:a,url_doi:"ir.2025.05",pdf:"https:\u002F\u002Ff.oaes.cc\u002Fxmlpdf\u002F70d593d9-d1fe-4b5c-8b57-5a56eef2bc19\u002Fir5005.pdf",video_url:a,allauthors:"\u003Cimg src='https:\u002F\u002Fi.oaes.cc\u002Fimages\u002Fdefault_photo.png' class='author_photo' alt='Haibo Duan'\u003E Haibo Duan, \u003Cimg src='https:\u002F\u002Fi.oaes.cc\u002Fimages\u002Fdefault_photo.png' class='author_photo' alt='Fanrong Shi'\u003E Fanrong Shi, \u003Cimg src='https:\u002F\u002Fi.oaes.cc\u002Fimages\u002Fdefault_photo.png' class='author_photo' alt='Bo Gao'\u003E Bo Gao, \u003Cimg src='https:\u002F\u002Fi.oaes.cc\u002Fimages\u002Fdefault_photo.png' class='author_photo' alt='Yingyue Zhou'\u003E Yingyue Zhou, \u003Cimg src='https:\u002F\u002Fi.oaes.cc\u002Fimages\u002Fdefault_photo.png' class='author_photo' alt='Qiushi Cui'\u003E Qiushi Cui",journal_path:d,video_img:a,image_list:b,abstract:"\u003Cp\u003EThe live-line operation of 10 kV distribution networks is critical for ensuring uninterrupted and high-quality power supply. However, operational sites face challenges such as insufficient intelligent monitoring and suboptimal realtime performance. To address these issues, this study proposes the FEM-YOLOv8 algorithm, specifically designed for protective equipment detection in live-line operation scenarios. The proposed algorithm is deployed on edge devices compatible with unmanned aerial vehicles (UAVs), enabling remote, autonomous, and intelligent monitoring. Key improvements include the introduction of an enhanced FAST-C2f module, replacing the original C2f module in the Backbone to improve feature extraction efficiency while reducing model complexity. Additionally, a lightweight efficient channel attention (ECA) mechanism is incorporated into the Backbone and Neck to enhance target feature detection and representation capabilities. The bounding box regression loss function is replaced with metric preserving distance intersection over union (MPDIoU) to further boost detection accuracy and robustness. The FEM-YOLOv8 model is implemented on the Atlas 200I DK A2 edge device, which is suitable for UAV deployment. Experimental results demonstrate that the improved FEM-YOLOv8 model achieves 93.1% precision (P), 85.9% recall (R), and 92.3% mean average precision (mAP), surpassing the baseline model by 2.8, 3.2, and 2.2 percentage points, respectively. With a detection speed of 83 frames per second (FPS) and a power consumption of only 10.2 W, the model satisfies real-time performance and detection accuracy requirements, providing significant contributions to grid intelligence and power operation safety.\u003C\u002Fp\u003E"},{article_id:"7657",title:"Adaptive variational empirical mode decomposition aware intelligent data-driven modeling for complex industrial processes",image:b,tag:"50-69",doi:"10.20517\u002Fir.2025.04",date_published:"1736956800",section:t,authors:"Yujun Chen, ... Kairui Sheng",journal_id:g,volume:h,year:q,section_id:"1912",special_name:"Topic: Performance Evaluation and Optimization for Intelligent Systems",url_doi:"ir.2025.04",pdf:"https:\u002F\u002Ff.oaes.cc\u002Fxmlpdf\u002F00d1f5ad-b235-4833-965c-2a506355e761\u002Fir5004.pdf",video_url:a,allauthors:"\u003Cimg src='https:\u002F\u002Fi.oaes.cc\u002Fimages\u002Fdefault_photo.png' class='author_photo' alt='Yujun Chen'\u003E Yujun Chen, \u003Cimg src='https:\u002F\u002Fi.oaes.cc\u002Fimages\u002Fdefault_photo.png' class='author_photo' alt='Xiuli Zhu'\u003E Xiuli Zhu, \u003Cimg src='https:\u002F\u002Fi.oaes.cc\u002Fimages\u002Fdefault_photo.png' class='author_photo' alt='Peng Wang'\u003E Peng Wang, \u003Cimg src='https:\u002F\u002Fi.oaes.cc\u002Fimages\u002Fdefault_photo.png' class='author_photo' alt='Kuangrong Hao'\u003E Kuangrong Hao, \u003Cimg src='https:\u002F\u002Fi.oaes.cc\u002Fimages\u002Fdefault_photo.png' class='author_photo' alt='Kairui Sheng'\u003E Kairui Sheng",journal_path:d,video_img:a,image_list:b,abstract:"\u003Cp\u003EDue to the strong noise, high dimensionality and time-varying characteristics of industrial process data, data-driven modeling faces challenges in feature extraction and model interpretability. To address these issues, this paper proposes a new prediction model based on adaptive variational empirical mode decomposition-guided (AVEMDG) graph convolutional networks (GCNs). First, each sensor signal is decomposed into high-frequency and low-frequency features using empirical mode decomposition (EMD) to effectively capture multi-band information. Second, the weights of these features are adaptively updated through variational inference (Ⅵ) combined with Bayesian reasoning to handle the importance and uncertainty of features. Next, the GCN is used to model the spatiotemporal dependencies in the sensor network and is trained using the reweighted feature data. Last, the proposed method is applied to the prediction of the melt viscosity index (MVI), a key performance indicator (KPI) of the actual polyester fiber polymerization process. Ablation study and comparative experiment are conducted to evaluate the contribution of each component and the generality of the proposed model. Experimental results show that this method can effectively improve the model prediction accuracy, thereby enhancing the interpretability of the soft sensor model and providing guidance for the production of industrial processes.\u003C\u002Fp\u003E"}],recent_ids:"7691,7712,7680,7679,7657",view:[{article_id:R,title:C,image:b,tag:S,doi:T,date_published:D,section:i,authors:U,journal_id:g,volume:c,year:o,section_id:b,special_name:a,url_doi:B,pdf:V,video_url:W,allauthors:"Jiaju Qi, Qihao Zhou\u003Ca href='https:\u002F\u002Forcid.org\u002F0000-0002-1839-1439' target='_blank'\u003E\u003Cimg src='https:\u002F\u002Fi.oaes.cc\u002Fimages\u002Forcid.png' class='author_id' alt='Qihao Zhou'\u003E\u003C\u002Fa\u003E, Lei Lei\u003Ca href='https:\u002F\u002Forcid.org\u002F0000-0002-2577-6644' target='_blank'\u003E\u003Cimg src='https:\u002F\u002Fi.oaes.cc\u002Fimages\u002Forcid.png' class='author_id' alt='Lei Lei'\u003E\u003C\u002Fa\u003E, Kan Zheng",journal_path:d,video_img:X,image_list:b,abstract:Y},{article_id:Z,title:_,image:u,tag:$,doi:aa,date_published:ab,section:i,authors:ac,journal_id:g,volume:e,year:v,section_id:b,special_name:a,url_doi:ad,pdf:ae,video_url:a,allauthors:"Danni Wu, Zichen Liang, Guang Chen",journal_path:d,video_img:a,image_list:u,abstract:af},{article_id:ag,title:ah,image:b,tag:ai,doi:aj,date_published:ak,section:i,authors:al,journal_id:g,volume:e,year:v,section_id:b,special_name:a,url_doi:am,pdf:an,video_url:ao,allauthors:"Hongyin Zhang, Li He, Donglin Wang\u003Ca href='http:\u002F\u002Forcid.org\u002F0000-0002-8188-3735' target='_blank'\u003E\u003Cimg src='https:\u002F\u002Fi.oaes.cc\u002Fimages\u002Forcid.png' class='author_id' alt='Donglin Wang'\u003E\u003C\u002Fa\u003E",journal_path:d,video_img:ap,image_list:b,abstract:aq},{article_id:ar,title:as,image:w,tag:at,doi:au,date_published:av,section:i,authors:aw,journal_id:g,volume:c,year:o,section_id:ax,special_name:x,url_doi:ay,pdf:az,video_url:a,allauthors:"Albert Ji, Wai Lok Woo\u003Ca href='https:\u002F\u002Forcid.org\u002F0000-0002-8698-7605' target='_blank'\u003E\u003Cimg src='https:\u002F\u002Fi.oaes.cc\u002Fimages\u002Forcid.png' class='author_id' alt='Wai Lok Woo'\u003E\u003C\u002Fa\u003E, Eugene Wai Leong Wong, Yang Thee Quek",journal_path:d,video_img:a,image_list:w,abstract:aA},{article_id:"4353",title:"Bio-inspired intelligence with applications to robotics: a survey",image:b,tag:"58-83",doi:"10.20517\u002Fir.2021.08",date_published:D,section:i,authors:"Junfei Li, ... Simon X. Yang",journal_id:g,volume:c,year:o,section_id:"1201",special_name:E,url_doi:"ir.2021.08",pdf:"https:\u002F\u002Ff.oaes.cc\u002Fxmlpdf\u002F446fa24e-e8c7-4d94-93c5-bbfff4f32e6b\u002F4353.pdf",video_url:"https:\u002F\u002Fv1.oaepublish.com\u002Ffiles\u002Ftalkvideo\u002F4353.mp4",allauthors:"Junfei Li, Zhe Xu\u003Ca href='https:\u002F\u002Forcid.org\u002F0000-0001-7227-640X' target='_blank'\u003E\u003Cimg src='https:\u002F\u002Fi.oaes.cc\u002Fimages\u002Forcid.png' class='author_id' alt='Zhe Xu'\u003E\u003C\u002Fa\u003E, Danjie Zhu, Kevin Dong, Tao Yan\u003Ca href='https:\u002F\u002Forcid.org\u002F0000-0002-4494-9598' target='_blank'\u003E\u003Cimg src='https:\u002F\u002Fi.oaes.cc\u002Fimages\u002Forcid.png' class='author_id' alt='Tao Yan'\u003E\u003C\u002Fa\u003E, Zhu Zeng, Simon X. Yang",journal_path:d,video_img:"https:\u002F\u002Fi.oaes.cc\u002Fuploads\u002F20240205\u002F29f1fb64c19a470fae5fe533b38418e7.jpg",image_list:b,abstract:"\u003Cp\u003EIn the past decades, considerable attention has been paid to bio-inspired intelligence and its applications to robotics. This paper provides a comprehensive survey of bio-inspired intelligence, with a focus on neurodynamics approaches, to various robotic applications, particularly to path planning and control of autonomous robotic systems. Firstly, the bio-inspired shunting model and its variants (additive model and gated dipole model) are introduced, and their main characteristics are given in detail. Then, two main neurodynamics applications to real-time path planning and control of various robotic systems are reviewed. A bio-inspired neural network framework, in which neurons are characterized by the neurodynamics models, is discussed for mobile robots, cleaning robots, and underwater robots. The bio-inspired neural network has been widely used in real-time collision-free navigation and cooperation without any learning procedures, global cost functions, and prior knowledge of the dynamic environment. In addition, bio-inspired backstepping controllers for various robotic systems, which are able to eliminate the speed jump when a large initial tracking error occurs, are further discussed. Finally, the current challenges and future research directions are discussed in this paper.\u003C\u002Fp\u003E"}],view_ids:"4325,4825,5115,4477,4353",download:[{article_id:R,title:C,image:b,tag:S,doi:T,date_published:D,section:i,authors:U,journal_id:g,volume:c,year:o,section_id:b,special_name:a,url_doi:B,pdf:V,video_url:W,allauthors:"\u003Cimg src='https:\u002F\u002Fi.oaes.cc\u002Fimages\u002Fdefault_photo.png' class='author_photo' alt='Jiaju Qi'\u003E Jiaju Qi, \u003Cimg src='https:\u002F\u002Fi.oaes.cc\u002Fimages\u002Fdefault_photo.png' class='author_photo' alt='Qihao Zhou'\u003E Qihao Zhou\u003Ca href='https:\u002F\u002Forcid.org\u002F0000-0002-1839-1439' target='_blank'\u003E\u003Cimg src='https:\u002F\u002Fi.oaes.cc\u002Fimages\u002Forcid.png' class='author_id' alt='Qihao Zhou'\u003E\u003C\u002Fa\u003E, \u003Cimg src='https:\u002F\u002Fi.oaes.cc\u002Fimages\u002Fdefault_photo.png' class='author_photo' alt='Lei Lei'\u003E Lei Lei\u003Ca href='https:\u002F\u002Forcid.org\u002F0000-0002-2577-6644' target='_blank'\u003E\u003Cimg src='https:\u002F\u002Fi.oaes.cc\u002Fimages\u002Forcid.png' class='author_id' alt='Lei Lei'\u003E\u003C\u002Fa\u003E, \u003Cimg src='https:\u002F\u002Fi.oaes.cc\u002Fimages\u002Fdefault_photo.png' class='author_photo' alt='Kan Zheng'\u003E Kan Zheng",journal_path:d,video_img:X,image_list:b,abstract:Y},{article_id:aB,title:aC,image:y,tag:aD,doi:aE,date_published:aF,section:i,authors:aG,journal_id:g,volume:f,year:r,section_id:b,special_name:a,url_doi:aH,pdf:aI,video_url:aJ,allauthors:aK,journal_path:d,video_img:aL,image_list:y,abstract:aM},{article_id:Z,title:_,image:u,tag:$,doi:aa,date_published:ab,section:i,authors:ac,journal_id:g,volume:e,year:v,section_id:b,special_name:a,url_doi:ad,pdf:ae,video_url:a,allauthors:"\u003Cimg src='https:\u002F\u002Fi.oaes.cc\u002Fimages\u002Fdefault_photo.png' class='author_photo' alt='Danni Wu'\u003E Danni Wu, \u003Cimg src='https:\u002F\u002Fi.oaes.cc\u002Fimages\u002Fdefault_photo.png' class='author_photo' alt='Zichen Liang'\u003E Zichen Liang, \u003Cimg src='https:\u002F\u002Fi.oaes.cc\u002Fimages\u002Fdefault_photo.png' class='author_photo' alt='Guang Chen'\u003E Guang Chen",journal_path:d,video_img:a,image_list:u,abstract:af},{article_id:ar,title:as,image:w,tag:at,doi:au,date_published:av,section:i,authors:aw,journal_id:g,volume:c,year:o,section_id:ax,special_name:x,url_doi:ay,pdf:az,video_url:a,allauthors:"\u003Cimg src='https:\u002F\u002Fi.oaes.cc\u002Fimages\u002Fdefault_photo.png' class='author_photo' alt='Albert Ji'\u003E Albert Ji, \u003Cimg src='https:\u002F\u002Fi.oaes.cc\u002Fimages\u002Fdefault_photo.png' class='author_photo' alt='Wai Lok Woo'\u003E Wai Lok Woo\u003Ca href='https:\u002F\u002Forcid.org\u002F0000-0002-8698-7605' target='_blank'\u003E\u003Cimg src='https:\u002F\u002Fi.oaes.cc\u002Fimages\u002Forcid.png' class='author_id' alt='Wai Lok Woo'\u003E\u003C\u002Fa\u003E, \u003Cimg src='https:\u002F\u002Fi.oaes.cc\u002Fimages\u002Fdefault_photo.png' class='author_photo' alt='Eugene Wai Leong Wong'\u003E Eugene Wai Leong Wong, \u003Cimg src='https:\u002F\u002Fi.oaes.cc\u002Fimages\u002Fdefault_photo.png' class='author_photo' alt='Yang Thee Quek'\u003E Yang Thee Quek",journal_path:d,video_img:a,image_list:w,abstract:aA},{article_id:ag,title:ah,image:b,tag:ai,doi:aj,date_published:ak,section:i,authors:al,journal_id:g,volume:e,year:v,section_id:b,special_name:a,url_doi:am,pdf:an,video_url:ao,allauthors:"\u003Cimg src='https:\u002F\u002Fi.oaes.cc\u002Fimages\u002Fdefault_photo.png' class='author_photo' alt='Hongyin Zhang'\u003E Hongyin Zhang, \u003Cimg src='https:\u002F\u002Fi.oaes.cc\u002Fimages\u002Fdefault_photo.png' class='author_photo' alt='Li He'\u003E Li He, \u003Cimg src='https:\u002F\u002Fi.oaes.cc\u002Fimages\u002Fdefault_photo.png' class='author_photo' alt='Donglin Wang'\u003E Donglin Wang\u003Ca href='http:\u002F\u002Forcid.org\u002F0000-0002-8188-3735' target='_blank'\u003E\u003Cimg src='https:\u002F\u002Fi.oaes.cc\u002Fimages\u002Forcid.png' class='author_id' alt='Donglin Wang'\u003E\u003C\u002Fa\u003E",journal_path:d,video_img:ap,image_list:b,abstract:aq}],download_ids:"4325,5329,4825,4477,5115"},cited:[{article_id:"5424",title:"An overview of intelligent image segmentation using active contour models",image:aN,tag:"23-55",doi:"10.20517\u002Fir.2023.02",date_published:"1676995200",section:i,authors:"Yiyang Chen\u003Ca href='https:\u002F\u002Forcid.org\u002F0000-0001-9960-9040' target='_blank'\u003E\u003Cimg src='https:\u002F\u002Fi.oaes.cc\u002Fimages\u002Forcid.png' class='author_id' alt='Yiyang Chen'\u003E\u003C\u002Fa\u003E, ... Hongtian Chen\u003Ca href='http:\u002F\u002Forcid.org\u002F0000-0002-8600-9668' target='_blank'\u003E\u003Cimg src='https:\u002F\u002Fi.oaes.cc\u002Fimages\u002Forcid.png' class='author_id' alt='Hongtian Chen'\u003E\u003C\u002Fa\u003E",journal_id:g,volume:f,year:r,section_id:b,special_name:a,url_doi:"ir.2023.02",pdf:"https:\u002F\u002Ff.oaes.cc\u002Fxmlpdf\u002F220eb536-974c-408a-8b4b-9f1a7e56035f\u002F5424.pdf",video_url:a,allauthors:"\u003Cimg src='https:\u002F\u002Fi.oaes.cc\u002Fimages\u002Fdefault_photo.png' class='author_photo' alt='Yiyang Chen'\u003E Yiyang Chen\u003Ca href='https:\u002F\u002Forcid.org\u002F0000-0001-9960-9040' target='_blank'\u003E\u003Cimg src='https:\u002F\u002Fi.oaes.cc\u002Fimages\u002Forcid.png' class='author_id' alt='Yiyang Chen'\u003E\u003C\u002Fa\u003E, \u003Cimg src='https:\u002F\u002Fi.oaes.cc\u002Fimages\u002Fdefault_photo.png' class='author_photo' alt='Pengqiang Ge'\u003E Pengqiang Ge, \u003Cimg src='https:\u002F\u002Fi.oaes.cc\u002Fimages\u002Fdefault_photo.png' class='author_photo' alt='Guina Wang'\u003E Guina Wang, \u003Cimg src='https:\u002F\u002Fi.oaes.cc\u002Fimages\u002Fdefault_photo.png' class='author_photo' alt='Guirong Weng'\u003E Guirong Weng, \u003Cimg src='https:\u002F\u002Fi.oaes.cc\u002Fimages\u002Fdefault_photo.png' class='author_photo' alt='Hongtian Chen'\u003E Hongtian Chen\u003Ca href='http:\u002F\u002Forcid.org\u002F0000-0002-8600-9668' target='_blank'\u003E\u003Cimg src='https:\u002F\u002Fi.oaes.cc\u002Fimages\u002Forcid.png' class='author_id' alt='Hongtian Chen'\u003E\u003C\u002Fa\u003E",journal_path:d,video_img:a,image_list:aN,abstract:"\u003Cp\u003EThe active contour model (ACM) approach in image segmentation is regarded as a research hotspot in the area of computer vision, which is widely applied in different kinds of applications in practice, such as medical image processing. The essence of ACM is to make use ofuse an enclosed and smooth curve to signify the target boundary, which is usually accomplished by minimizing the associated energy function by means ofthrough the standard descent method. This paper presents an overview of ACMs for handling image segmentation problems in various fields. It begins with an introduction briefly reviewing different ACMs with their pros and cons. Then, some basic knowledge in of the theory of ACMs is explained, and several popular ACMs in terms of three categories, including region-based ACMs, edge-based ACMs, and hybrid ACMs, are detailedly reviewed with their advantages and disadvantages. After that, twelve ACMs are chosen from the literature to conduct three sets of segmentation experiments to segment different kinds of images, and compare the segmentation efficiency and accuracy with different methods. Next, two deep learning-based algorithms are implemented to segment different types of images to compare segmentation results with several ACMs. Experimental results confirm some useful conclusions about their sharing strengths and weaknesses. Lastly, this paper points out some promising research directions that need to be further studied in the future.\u003C\u002Fp\u003E"},{article_id:"6014",title:"Deep learning-based scene understanding for autonomous robots: a survey",image:b,tag:"374-401",doi:"10.20517\u002Fir.2023.22",date_published:"1692028800",section:i,authors:"Jianjun Ni\u003Ca href='http:\u002F\u002Forcid.org\u002F0000-0002-7130-8331' target='_blank'\u003E\u003Cimg src='https:\u002F\u002Fi.oaes.cc\u002Fimages\u002Forcid.png' class='author_id' alt='Jianjun Ni'\u003E\u003C\u002Fa\u003E, ... Pengfei Shi\u003Ca href='https:\u002F\u002Forcid.org\u002F0000-0002-2966-1676' target='_blank'\u003E\u003Cimg src='https:\u002F\u002Fi.oaes.cc\u002Fimages\u002Forcid.png' class='author_id' alt='Pengfei Shi'\u003E\u003C\u002Fa\u003E",journal_id:g,volume:f,year:r,section_id:"1243",special_name:"Topic: Scene Understanding for Autonomous Robotics",url_doi:"ir.2023.22",pdf:"https:\u002F\u002Ff.oaes.cc\u002Fxmlpdf\u002F1fac2add-6180-4dc1-83e1-e46b3dc9e6c5\u002Fir3022.pdf",video_url:"https:\u002F\u002Fv.oaes.cc\u002Fuploads\u002F20230816\u002Fa802fc5ba47f4c08ba0a95089779d731.mp4",allauthors:"\u003Cimg src='https:\u002F\u002Fi.oaes.cc\u002Fimages\u002Fdefault_photo.png' class='author_photo' alt='Jianjun Ni'\u003E Jianjun Ni\u003Ca href='http:\u002F\u002Forcid.org\u002F0000-0002-7130-8331' target='_blank'\u003E\u003Cimg src='https:\u002F\u002Fi.oaes.cc\u002Fimages\u002Forcid.png' class='author_id' alt='Jianjun Ni'\u003E\u003C\u002Fa\u003E, \u003Cimg src='https:\u002F\u002Fi.oaes.cc\u002Fimages\u002Fdefault_photo.png' class='author_photo' alt='Yan Chen'\u003E Yan Chen\u003Ca href='https:\u002F\u002Forcid.org\u002F0000-0003-4969-5723' target='_blank'\u003E\u003Cimg src='https:\u002F\u002Fi.oaes.cc\u002Fimages\u002Forcid.png' class='author_id' alt='Yan Chen'\u003E\u003C\u002Fa\u003E, \u003Cimg src='https:\u002F\u002Fi.oaes.cc\u002Fimages\u002Fdefault_photo.png' class='author_photo' alt='Guangyi Tang'\u003E Guangyi Tang\u003Ca href='https:\u002F\u002Forcid.org\u002F0000-0002-1542-6392' target='_blank'\u003E\u003Cimg src='https:\u002F\u002Fi.oaes.cc\u002Fimages\u002Forcid.png' class='author_id' alt='Guangyi Tang'\u003E\u003C\u002Fa\u003E, \u003Cimg src='https:\u002F\u002Fi.oaes.cc\u002Fimages\u002Fdefault_photo.png' class='author_photo' alt='Jiamei Shi'\u003E Jiamei Shi\u003Ca href='https:\u002F\u002Forcid.org\u002F0009-0005-5313-842X' target='_blank'\u003E\u003Cimg src='https:\u002F\u002Fi.oaes.cc\u002Fimages\u002Forcid.png' class='author_id' alt='Jiamei Shi'\u003E\u003C\u002Fa\u003E, \u003Cimg src='https:\u002F\u002Fi.oaes.cc\u002Fimages\u002Fdefault_photo.png' class='author_photo' alt='Weidong Cao'\u003E Weidong Cao\u003Ca href='https:\u002F\u002Forcid.org\u002F0000-0002-0394-9639' target='_blank'\u003E\u003Cimg src='https:\u002F\u002Fi.oaes.cc\u002Fimages\u002Forcid.png' class='author_id' alt='Weidong Cao'\u003E\u003C\u002Fa\u003E, \u003Cimg src='https:\u002F\u002Fi.oaes.cc\u002Fimages\u002Fdefault_photo.png' class='author_photo' alt='Pengfei Shi'\u003E Pengfei Shi\u003Ca href='https:\u002F\u002Forcid.org\u002F0000-0002-2966-1676' target='_blank'\u003E\u003Cimg src='https:\u002F\u002Fi.oaes.cc\u002Fimages\u002Forcid.png' class='author_id' alt='Pengfei Shi'\u003E\u003C\u002Fa\u003E",journal_path:d,video_img:"https:\u002F\u002Fi.oaes.cc\u002Fuploads\u002F20240205\u002F5aa534648ac046f0ba42673856c3bb89.jpg",image_list:b,abstract:"\u003Cp\u003EAutonomous robots are a hot research subject within the fields of science and technology, which has a big impact on social-economic development. The ability of the autonomous robot to perceive and understand its working environment is the basis for solving more complicated issues. In recent years, an increasing number of artificial intelligence-based methods have been proposed in the field of scene understanding for autonomous robots, and deep learning is one of the current key areas in this field. Outstanding gains have been attained in the field of scene understanding for autonomous robots based on deep learning. Thus, this paper presents a review of recent research on the deep learning-based scene understanding for autonomous robots. This survey provides a detailed overview of the evolution of robotic scene understanding and summarizes the applications of deep learning methods in scene understanding for autonomous robots. In addition, the key issues in autonomous robot scene understanding are analyzed, such as pose estimation, saliency prediction, semantic segmentation, and object detection. Then, some representative deep learning-based solutions for these issues are summarized. Finally, future challenges in the field of the scene understanding for autonomous robots are discussed.\u003C\u002Fp\u003E"},{article_id:aB,title:aC,image:y,tag:aD,doi:aE,date_published:aF,section:i,authors:aG,journal_id:g,volume:f,year:r,section_id:b,special_name:a,url_doi:aH,pdf:aI,video_url:aJ,allauthors:aK,journal_path:d,video_img:aL,image_list:y,abstract:aM}],cited_ids:"5424,6014,5329"},indexFot:{webinars:[],osi:{url:"https:\u002F\u002Fwww.oaepublish.com\u002Fir\u002Faims_and_scope",img:"https:\u002F\u002Fi.oaes.cc\u002Fuploads\u002F20240510\u002F542b779f322141c1a9299c64d6272563.jpg",list:[{section_id:1005,journal_id:m,deadline:aO,image:"https:\u002F\u002Fi.oaes.cc\u002Fpublic\u002Fissue_special\u002F1005\u002F54_ir_sp1005.jpg",sub_link:"https:\u002F\u002Fwww.oaecenter.com\u002Flogin?JournalId=ir&IssueId=ir2612311005",email:n,issue_flyer:a,title:"Topic: Collaborative Optimization and Control of Intelligent Unmanned Autonomous Systems",count:aP,author:["Prof. Hao Zhang","Dr. Changzhu Zhang"]},{section_id:1328,journal_id:m,deadline:aO,image:"https:\u002F\u002Fi.oaes.cc\u002Fpublic\u002Fissue_special\u002F1328\u002F82_ir_sp1328.jpg",sub_link:"https:\u002F\u002Fwww.oaecenter.com\u002Flogin?JournalId=ir&IssueId=ir2612311328",email:aQ,issue_flyer:a,title:"Topic: Robot System Intelligentization and Application: Learning, Control and Decision",count:aP,author:["Prof. Huaicheng Yan","Prof. Yueying Wang","Prof. Dan Zhang","Dr. Yang Wu"]}]},editor:[{id:1510,name:"Simon Yang",img:"https:\u002F\u002Foaepublishstorage.blob.core.windows.net\u002F95d7ca1b-f755-4363-a984-ab040ef2c973\u002FSimon X. 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Scholar":"https:\u002F\u002Fscholar.google.com.hk\u002Fcitations?view_op=list_works&hl=zh-CN&hl=zh-CN&user=-Hx5OVYAAAAJ",Dimensions:"https:\u002F\u002Fapp.dimensions.ai\u002Fdiscover\u002Fpublication?and_facet_source_title=jour.1423782",Lens:"https:\u002F\u002Fwww.lens.org\u002Flens\u002Fsearch\u002Fscholar\u002Flist?p=0&n=10&s=date_published&d=%2B&f=false&e=false&l=en&authorField=author&dateFilterField=publishedYear&orderBy=%2Bdate_published&presentation=false&preview=true&stemmed=true&useAuthorId=false&publicationType.must=journal%20article&sourceTitle.must=Intelligence%20%26%20Robotics&publisher.must=OAE%20Publishing%20Inc."},editor:"Simon X. Yang",journal_rank:a,journal_flyer:"https:\u002F\u002Ff.oaes.cc\u002Findex_ad\u002Fflyer\u002FIR-flyer.pdf",qksearch:["Intelligence","Robotics","Reinforcement Learning","Machine Learning","Unmanned Vehicles","UAV"],sitetag:"Intell Robot",ad:[],colour_tag:"#0047bb",score:a,mobile_top_img:a,impact_factor:[{factor:"https:\u002F\u002Fi.oaes.cc\u002Fuploads\u002F20240813\u002F49390c7e86ab40a58ee862e8c1af65ba.png",url:bn},{factor:"https:\u002F\u002Fi.oaes.cc\u002Fuploads\u002F20240506\u002Fea3d9071c35b4bf3982ffe25f1083620.png",url:a}],rgba:"rgb(0,71,187)",log_image:"https:\u002F\u002Fi.oaes.cc\u002Fupload\u002Fjournal_logo\u002Fir.png"},webinfo:{},searchKey:a,loading:bo,appid:a,videoPlay:{show:A,href:a}},editer:{editList:{list:{}}},userdata:{showLogin:A,logined:A}},serverRendered:bo,routePath:J,config:{_app:{basePath:bp,assetsPath:bp,cdnURL:"https:\u002F\u002Fg.oaes.cc\u002Foae\u002Fnuxt\u002F"}}}}("","0","1","ir","2","3","40","5","Review","4","6",0,40,"assistant_editor@intellrobot.com","2021","image","2025","2023",497,"Research Article","https:\u002F\u002Fimage.oaes.cc\u002Faa73a59e-0434-480a-98d9-6257473c8a85\u002F4825-coverimg.jpg","2022","https:\u002F\u002Fimage.oaes.cc\u002F7cb2c33c-13bc-40ef-8ac0-4d653a4180fd\u002F4477-coverimg.jpg","Topic: Evolutionary Computation for Deep Learning and Machine Learning","https:\u002F\u002Fimage.oaes.cc\u002Fffa25840-c01a-4638-9680-a6af89417f6a\u002F5329-coverimg.jpg",287,false,"ir.2021.02","Federated reinforcement learning: techniques, applications, and open challenges","1633968000","Topic: Advances in Human-Assistive Technologies and Human-Robot Interactions",6,38,"Contact Us","Intelligence & Robotics","\u002Fir","7","8","9","https:\u002F\u002Fwww.raiic.org\u002F","https:\u002F\u002Fimage.oaes.cc\u002F8725948d-9e72-44b9-b975-58656279e74b\u002Fir5008-coverimg.jpg","https:\u002F\u002Fimage.oaes.cc\u002Fc3a967a3-ac3d-471f-85b5-e34b251968a3\u002Fir5006-coverimg.jpg","1737388800","4325","18-57","10.20517\u002Fir.2021.02","Jiaju Qi, ... Kan Zheng","https:\u002F\u002Ff.oaes.cc\u002Fxmlpdf\u002F38ebf366-5fba-47ab-86ac-aa0a6cc124ef\u002F4325.pdf","https:\u002F\u002Fv1.oaepublish.com\u002Ffiles\u002Ftalkvideo\u002F4325.mp4","https:\u002F\u002Fi.oaes.cc\u002Fuploads\u002F20240205\u002F1103dab1ab644cf2818a1cab0dd2b8ff.jpg","\u003Cp\u003EThis paper presents a comprehensive survey of federated reinforcement learning (FRL), an emerging and promising field in reinforcement learning (RL). Starting with a tutorial of federated learning (FL) and RL, we then focus on the introduction of FRL as a new method with great potential by leveraging the basic idea of FL to improve the performance of RL while preserving data-privacy. According to the distribution characteristics of the agents in the framework, FRL algorithms can be divided into two categories, \u003Ci\u003E\u003Ci\u003Ei.e.\u003C\u002Fi\u003E\u003C\u002Fi\u003E, Horizontal Federated Reinforcement Learning and vertical federated reinforcement learning (VFRL). We provide the detailed definitions of each category by formulas, investigate the evolution of FRL from a technical perspective, and highlight its advantages over previous RL algorithms. In addition, the existing works on FRL are summarized by application fields, including edge computing, communication, control optimization, and attack detection. Finally, we describe and discuss several key research directions that are crucial to solving the open problems within FRL.\u003C\u002Fp\u003E","4825","Deep learning for LiDAR-only and LiDAR-fusion 3D perception: a survey","105-29","10.20517\u002Fir.2021.20","1650816000","Danni Wu, ... Guang Chen","ir.2021.20","https:\u002F\u002Ff.oaes.cc\u002Fxmlpdf\u002Faa73a59e-0434-480a-98d9-6257473c8a85\u002F4825.pdf","\u003Cp\u003EThe perception system for robotics and autonomous cars relies on the collaboration among multiple types of sensors to understand the surrounding environment. LiDAR has shown great potential to provide accurate environmental information, and thus deep learning on LiDAR point cloud draws increasing attention. However, LiDAR is unable to handle severe weather. The sensor fusion between LiDAR and other sensors is an emerging topic due to its supplementary property compared to a single LiDAR. Challenges exist in deep learning methods that take LiDAR point cloud fusion data as input, which need to seek a balance between accuracy and algorithm complexity due to data redundancy. This work focuses on a comprehensive survey of deep learning on LiDAR-only and LiDAR-fusion 3D perception tasks. Starting with the representation of LiDAR point cloud, this paper then introduces its unique characteristics and the evaluation dataset as well as metrics. This paper gives a review according to four key tasks in the field of LiDAR-based perception: object classification, object detection, object tracking, and segmentation (including semantic segmentation and instance segmentation). Finally, we present the overlooked aspects of the current algorithms and possible solutions, hoping this paper can serve as a reference for the related research.\u003C\u002Fp\u003E","5115","Deep reinforcement learning for real-world quadrupedal locomotion: a comprehensive review","275-97","10.20517\u002Fir.2022.20","1661961600","Hongyin Zhang, ... Donglin Wang\u003Ca href='http:\u002F\u002Forcid.org\u002F0000-0002-8188-3735' target='_blank'\u003E\u003Cimg src='https:\u002F\u002Fi.oaes.cc\u002Fimages\u002Forcid.png' class='author_id' alt='Donglin Wang'\u003E\u003C\u002Fa\u003E","ir.2022.20","https:\u002F\u002Ff.oaes.cc\u002Fxmlpdf\u002F12bbd333-926b-4682-b8bc-e9945c452687\u002F5115.pdf","https:\u002F\u002Fv1.oaepublish.com\u002Ffiles\u002Ftalkvideo\u002F5115.mp4","https:\u002F\u002Fi.oaes.cc\u002Fuploads\u002F20240205\u002F647c73d0a5884b76a7d7697b7584a99d.jpg","\u003Cp\u003EBuilding controllers for legged robots with agility and intelligence has been one of the typical challenges in the pursuit of artificial intelligence (AI). As an important part of the AI field, deep reinforcement learning (DRL) can realize sequential decision making without physical modeling through end-to-end learning and has achieved a series of major breakthroughs in quadrupedal locomotion research. In this review article, we systematically organize and summarize relevant important literature, covering DRL algorithms from problem setting to advanced learning methods. These algorithms alleviate the specific problems encountered in the practical application of robots to a certain extent. We first elaborate on the general development trend in this field from several aspects, such as the DRL algorithms, simulation environments, and hardware platforms. Moreover, core components in the algorithm design, such as state and action spaces, reward functions, and solutions to reality gap problems, are highlighted and summarized. We further discuss open problems and propose promising future research directions to discover new areas of research.\u003C\u002Fp\u003E","4477","Rail track condition monitoring: a review on deep learning approaches","151-75","10.20517\u002Fir.2021.14","1640793600","Albert Ji, ... Yang Thee Quek","1002","ir.2021.14","https:\u002F\u002Ff.oaes.cc\u002Fxmlpdf\u002F7cb2c33c-13bc-40ef-8ac0-4d653a4180fd\u002F4477.pdf","\u003Cp\u003ERail track is a critical component of rail systems. Accidents or interruptions caused by rail track anomalies usually possess severe outcomes. Therefore, rail track condition monitoring is an important task. Over the past decade, deep learning techniques have been rapidly developed and deployed. In the paper, we review the existing literature on applying deep learning to rail track condition monitoring. Potential challenges and opportunities are discussed for the research community to decide on possible directions. Two application cases are presented to illustrate the implementation of deep learning to rail track condition monitoring in practice before we conclude the paper.\u003C\u002Fp\u003E","5329","Formation control of multiple autonomous underwater vehicles: a review","1-22","10.20517\u002Fir.2023.01","1673625600","Tao Yan, ... S. Andrew Gadsden","ir.2023.01","https:\u002F\u002Ff.oaes.cc\u002Fxmlpdf\u002Fffa25840-c01a-4638-9680-a6af89417f6a\u002F5329.pdf","https:\u002F\u002Fv1.oaepublish.com\u002Ffiles\u002Ftalkvideo\u002F5329.mp4","\u003Cimg src='https:\u002F\u002Fi.oaes.cc\u002Fimages\u002Fdefault_photo.png' class='author_photo' alt='Tao Yan'\u003E Tao Yan, \u003Cimg src='https:\u002F\u002Fi.oaes.cc\u002Fimages\u002Fdefault_photo.png' class='author_photo' alt='Zhe Xu'\u003E Zhe Xu, \u003Cimg src='https:\u002F\u002Fi.oaes.cc\u002Fimages\u002Fdefault_photo.png' class='author_photo' alt='Simon X. Yang'\u003E Simon X. Yang\u003Ca href='https:\u002F\u002Forcid.org\u002F0000-0002-6888-7993' target='_blank'\u003E\u003Cimg src='https:\u002F\u002Fi.oaes.cc\u002Fimages\u002Forcid.png' class='author_id' alt='Simon X. Yang'\u003E\u003C\u002Fa\u003E, \u003Cimg src='https:\u002F\u002Fi.oaes.cc\u002Fimages\u002Fdefault_photo.png' class='author_photo' alt='S. Andrew Gadsden'\u003E S. Andrew Gadsden","https:\u002F\u002Fi.oaes.cc\u002Fuploads\u002F20240205\u002Fc594ecf74c2a433e90b38a7211bdd0e7.jpg","\u003Cp\u003EThis paper presents a comprehensive overview of recent developments in formation control of multiple autonomous underwater vehicles (AUVs). Several commonly used structures and approaches for formation coordination are listed, and the advantages and deficiencies of each method are discussed. The difficulties confronted in synthesis of a practical AUVs formation system are clarified and analyzed in terms of the characteristic of AUVs, adverse underwater environments, and communication constraints. The state-of-the-art solutions available for addressing these challenges are reviewed comprehensively. Based on that, a brief discussion is made, and a list of promising future work is pointed out, which aims to be helpful for the further promotion of AUVs formation applications.\u003C\u002Fp\u003E","https:\u002F\u002Fimage.oaes.cc\u002F220eb536-974c-408a-8b4b-9f1a7e56035f\u002F5424-coverimg.jpg",1798675200,7,"editorial@intellrobot.com",1517,1719792000,"https:\u002F\u002Fi.oaes.cc\u002Fuploads\u002F20230525\u002Fe20ca0a2b2aa4ef4aad1afc5cc4f3aff.jpg","Topic: Swarm Intelligence for Robotic Systems",1201,1685491200,"https:\u002F\u002Fi.oaes.cc\u002Fpublic\u002Fissue_special\u002F1201\u002F99_ir_sp1201.jpg",1002,1672444800,"https:\u002F\u002Fi.oaes.cc\u002Fpublic\u002Fissue_special\u002F1002\u002F77_ir_sp1002.jpg",622,"Complex Engineering Systems","About","Editorial Policies","Journals","Academic Talks","10","11","5555","\u002Fir\u002Fcontact_us","Video Abstract Guidelines","\u002Fir\u002Fvideo_abstract_guidelines","2770-3541 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