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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. Promote research and application innovation in the fields of intelligence and robotics."><meta data-n-head="ssr" property="og:url" content="/ir"><meta data-n-head="ssr" property="og:type" content="OAE Publishing Inc."><meta data-n-head="ssr" property="og:site_name" content="Intelligence & Robotics"><meta data-n-head="ssr" property="og:title" content="Intelligence & Robotics"><title>Intelligence & Robotics - OAE Publishing Inc.</title><link data-n-head="ssr" rel="icon" type="image/x-icon" href="/favicon.ico"><link data-n-head="ssr" rel="canonical" href="https://www.oaepublish.com/ir"><script data-n-head="ssr" src="https://accounts.google.com/gsi/client" async></script><script data-n-head="ssr" src="https://g.oaes.cc/oae/dist/relijs.js" async></script><script data-n-head="ssr" src="https://www.googletagmanager.com/gtag/js?id=G-FM6KBJGRBV" async></script><link rel="preload" href="https://g.oaes.cc/oae/nuxt/b06ddfb.js" as="script"><link rel="preload" href="https://g.oaes.cc/oae/nuxt/0a3b980.js" as="script"><link rel="preload" href="https://g.oaes.cc/oae/nuxt/css/8176b15.css" as="style"><link rel="preload" href="https://g.oaes.cc/oae/nuxt/3e8004d.js" as="script"><link rel="preload" href="https://g.oaes.cc/oae/nuxt/css/f3a19d3.css" as="style"><link rel="preload" href="https://g.oaes.cc/oae/nuxt/b19d7ea.js" as="script"><link rel="preload" href="https://g.oaes.cc/oae/nuxt/css/4aeddf0.css" as="style"><link rel="preload" href="https://g.oaes.cc/oae/nuxt/dce480e.js" as="script"><link rel="preload" href="https://g.oaes.cc/oae/nuxt/css/cab3d9c.css" as="style"><link rel="preload" href="https://g.oaes.cc/oae/nuxt/fc5a5cb.js" as="script"><link rel="stylesheet" href="https://g.oaes.cc/oae/nuxt/css/8176b15.css"><link rel="stylesheet" href="https://g.oaes.cc/oae/nuxt/css/f3a19d3.css"><link rel="stylesheet" href="https://g.oaes.cc/oae/nuxt/css/4aeddf0.css"><link rel="stylesheet" href="https://g.oaes.cc/oae/nuxt/css/cab3d9c.css"> </head> <body > <div data-server-rendered="true" id="__nuxt"><div id="__layout"><div data-fetch-key="qklayoutc:0"><div><div class="PcComment" data-v-662e0091><div class="ipad_bg" style="display:none;" data-v-662e0091></div> <div class="head_top" data-v-662e0091><div class="wrapper head_box" data-v-662e0091><span class="qk_jx" data-v-662e0091><img src="https://i.oaes.cc/upload/journal_logo/ir.png" alt data-v-662e0091></span> <a href="/ir" aria-current="page" class="qk_a_name nuxt-link-exact-active nuxt-link-active" data-v-662e0091><span class="title font20" data-v-662e0091>Intelligence & Robotics</span></a> <div class="top_img" data-v-662e0091><a href="https://www.scopus.com/sourceid/21101199351" target="_blank" data-v-662e0091><img src="https://i.oaes.cc/uploads/20240813/49390c7e86ab40a58ee862e8c1af65ba.png" alt data-v-662e0091></a><a href="" target="_blank" data-v-662e0091><img src="https://i.oaes.cc/uploads/20240506/ea3d9071c35b4bf3982ffe25f1083620.png" alt data-v-662e0091></a></div> <div class="oae_menu_box" data-v-662e0091><a href="/alljournals" data-v-662e0091><span data-v-662e0091>All Journals</span></a></div> <span class="search" data-v-662e0091><i class="icon-search icon_right font24" data-v-662e0091></i> <span data-v-662e0091>Search</span></span> <span class="go_oae" data-v-662e0091><a href="https://oaemesas.com/login?JournalId=ir" target="_blank" data-v-662e0091><i class="icon-login-line icon_right font24" data-v-662e0091></i> <span data-v-662e0091>Log In</span></a></span></div></div> <div class="cg" style="height: 41px" data-v-662e0091></div>  <div class="head_text" style="border-bottom:3px solid rgb(0,71,187);" data-v-662e0091><div class="head_search wrapper" data-v-662e0091><div class="box_btn" data-v-662e0091><div class="qk_miss" data-v-662e0091><img src="https://i.oaes.cc/uploads/20231121/59802903b17e4eebae240e004311d193.jpg" alt class="qk_fm" data-v-662e0091> <div class="miss_right" data-v-662e0091><div class="miss_btn" data-v-662e0091><span data-v-662e0091><span class="font_b" data-v-662e0091>Editor-in-Chief:</span> Simon X. 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We are sincerely grateful for his trust and support. </span></div></div> <img src="https://i.oaes.cc/uploads/20241118/4c4a3d9e9d0544e283921d9dd52b65ff.jpg" alt data-v-5c8e3c6d></a></div><div class="swiper-slide" data-v-5c8e3c6d><a href="https://www.oaepublish.com/news/ir.852" data-v-5c8e3c6d><div class="tit" data-v-5c8e3c6d><div class="title" data-v-5c8e3c6d><a href="https://www.oaepublish.com/news/ir.852" data-v-5c8e3c6d><span data-v-5c8e3c6d><i>Intelligence & Robotics</i> Accepted for Inclusion in ESCI</span></a></div> <div class="title_sm" data-v-5c8e3c6d><span data-v-5c8e3c6d><i>Intelligence & Robotics</i> (<i>IR</i>) has been officially accepted for inclusion in the Emerging Sources Citation Index (ESCI) in September 2024. </span></div></div> <img src="https://i.oaes.cc/uploads/20240907/d959c5166b104a2c9622cc9317e948a6.jpg" alt data-v-5c8e3c6d></a></div><div class="swiper-slide" data-v-5c8e3c6d><a href="https://www.oaepublish.com/specials/ir.1517" data-v-5c8e3c6d><div class="tit" data-v-5c8e3c6d><div class="title" data-v-5c8e3c6d><a href="https://www.oaepublish.com/specials/ir.1517" data-v-5c8e3c6d><span data-v-5c8e3c6d>Congratulations! </span></a></div> <div class="title_sm" data-v-5c8e3c6d><span data-v-5c8e3c6d>The Special Issue "Swarm Intelligence for Robotic Systems" comes to a successful conclusion. </span></div></div> <img src="https://i.oaes.cc/uploads/20240923/455af769bc9d4c68bb19feb4b0e69567.jpg" alt data-v-5c8e3c6d></a></div><div class="swiper-slide" data-v-5c8e3c6d><a href="https://www.oaepublish.com/ir/awards" data-v-5c8e3c6d><div class="tit" data-v-5c8e3c6d><div class="title" data-v-5c8e3c6d><a href="https://www.oaepublish.com/ir/awards" data-v-5c8e3c6d><span data-v-5c8e3c6d>Announcement: <i>Intelligence & Robotics</i> Best Paper Award and Best 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-2c8370e1> <div class="hot_art" data-v-2c8370e1><div data-v-ab8748fc data-v-2c8370e1><div class="PcTitle" data-v-ab8748fc><div class="top_line" data-v-ab8748fc></div> <div class="tit_box" data-v-ab8748fc><a href="/ir/featured" class="title_h" data-v-ab8748fc>Featured Articles<i class="el-icon-arrow-right" data-v-ab8748fc></i></a></div> <div class="line_btn" style="margin-top: 10px;" data-v-ab8748fc></div></div></div> <div class="art_item" data-v-2c8370e1><a href="/articles/ir.2023.08" data-v-2c8370e1><h4 class="article-header bor_btn" data-v-2c8370e1>ChatGPT in connected and autonomous vehicles: benefits and challenges</h4></a></div><div class="art_item" data-v-2c8370e1><a href="/articles/ir.2021.02" data-v-2c8370e1><h4 class="article-header bor_btn" data-v-2c8370e1>Federated reinforcement learning: techniques, applications, and open challenges</h4></a></div><div class="art_item" data-v-2c8370e1><a href="/articles/ir.2021.16" data-v-2c8370e1><h4 class="article-header bor_btn" data-v-2c8370e1>Facial expression recognition using adapted residual based deep neural network</h4></a></div></div> <div data-v-0dd4c6f5 data-v-2c8370e1><div class="New_box" data-v-0dd4c6f5><div data-v-ab8748fc data-v-0dd4c6f5><div class="PcTitle" data-v-ab8748fc><div class="top_line" data-v-ab8748fc></div> <div class="tit_box" data-v-ab8748fc><a href="/ir/academic_talks" class="title_h" data-v-ab8748fc>Academic Talks<i class="el-icon-arrow-right" data-v-ab8748fc></i></a></div> <div class="line_btn" style="margin-top: 10px;" data-v-ab8748fc></div></div></div> <div class="New_item" data-v-0dd4c6f5><div class="img" data-v-0dd4c6f5><img src="https://i.oaes.cc/uploads/20240724/bc840a5f2a274ae8b5c25f2dfee136f6.jpg" alt data-itemid="386" data-itemhref="https://v.oaes.cc/uploads/20240724/bad86d7bf85849bf94d44614ff4c6268.mp4" data-itemimg="https://i.oaes.cc/uploads/20240724/bc840a5f2a274ae8b5c25f2dfee136f6.jpg" data-v-0dd4c6f5> <i data-itemid="386" data-itemhref="https://v.oaes.cc/uploads/20240724/bad86d7bf85849bf94d44614ff4c6268.mp4" data-itemimg="https://i.oaes.cc/uploads/20240724/bc840a5f2a274ae8b5c25f2dfee136f6.jpg" class="bo_icon" data-v-0dd4c6f5></i></div> <div class="cont" data-v-0dd4c6f5><a href="https://www.oaepublish.com/interviews/ir.267" data-v-0dd4c6f5><h4 class="New_header ellip5" data-v-0dd4c6f5>Special Interview with Prof. Marcelo H. 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data-v-5c8e3c6d>Two Associate Editors Recognized on Clarivate Highly Cited Researchers 2024 List</span></a></div> <div class="title_sm" data-v-5c8e3c6d><span data-v-5c8e3c6d>Congratulations to Prof. Chen Peng from Shanghai University and Prof. Xinsong Yang from Sichuan University on being named to the Clarivate Highly Cited Researchers 2024 list.</span></div></div> <img src="https://i.oaes.cc/uploads/20241122/d91ca52eba4a41ffb86e24b0bf97f53a.jpg" alt data-v-5c8e3c6d></a></div><div class="swiper-slide" data-v-5c8e3c6d><a href="https://www.oaepublish.com/news/ir.886" data-v-5c8e3c6d><div class="tit" data-v-5c8e3c6d><div class="title" data-v-5c8e3c6d><a href="https://www.oaepublish.com/news/ir.886" data-v-5c8e3c6d><span data-v-5c8e3c6d>Welcome Prof. Keping Yu to Join <i>Intelligence & Robotics</i> as Associate Editor </span></a></div> <div class="title_sm" data-v-5c8e3c6d><span data-v-5c8e3c6d>The Editorial Office of <i>Intelligence & Robotics</i> (<i>IR</i>) is thrilled to welcome Prof. 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{layout:"qklayouti",data:[{indexTop:{banner:[{id:1079,type:P,title:bH,sub_title:"Congratulations to Prof. Chen Peng from Shanghai University and Prof. Xinsong Yang from Sichuan University on being named to the Clarivate Highly Cited Researchers 2024 list.",href:"https:\u002F\u002Fwww.oaepublish.com\u002Fnews\u002Fir.905",target:f,img:"https:\u002F\u002Fi.oaes.cc\u002Fuploads\u002F20241122\u002Fd91ca52eba4a41ffb86e24b0bf97f53a.jpg",mobile_img:a},{id:1060,type:P,title:"Welcome Prof. Keping Yu to Join \u003Ci\u003EIntelligence & Robotics\u003C\u002Fi\u003E as Associate Editor\r\n",sub_title:"The Editorial Office of \u003Ci\u003EIntelligence & Robotics\u003C\u002Fi\u003E (\u003Ci\u003EIR\u003C\u002Fi\u003E) is thrilled to welcome Prof. Keping Yu to its editorial board as Associate Editor. We are sincerely grateful for his trust and support.\r\n",href:"https:\u002F\u002Fwww.oaepublish.com\u002Fnews\u002Fir.886",target:f,img:"https:\u002F\u002Fi.oaes.cc\u002Fuploads\u002F20241118\u002F4c4a3d9e9d0544e283921d9dd52b65ff.jpg",mobile_img:a},{id:1003,type:P,title:"\u003Ci\u003EIntelligence & Robotics\u003C\u002Fi\u003E Accepted for Inclusion in ESCI",sub_title:"\u003Ci\u003EIntelligence & Robotics\u003C\u002Fi\u003E (\u003Ci\u003EIR\u003C\u002Fi\u003E) has been officially accepted for inclusion in the Emerging Sources Citation Index (ESCI) in September 2024.\r\n",href:bI,target:f,img:"https:\u002F\u002Fi.oaes.cc\u002Fuploads\u002F20240907\u002Fd959c5166b104a2c9622cc9317e948a6.jpg",mobile_img:a},{id:971,type:P,title:"Congratulations!\r\n",sub_title:"The Special Issue \"Swarm Intelligence for Robotic Systems\" comes to a successful conclusion.\r\n",href:"https:\u002F\u002Fwww.oaepublish.com\u002Fspecials\u002Fir.1517",target:f,img:"https:\u002F\u002Fi.oaes.cc\u002Fuploads\u002F20240923\u002F455af769bc9d4c68bb19feb4b0e69567.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:f,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:bi,title:bj,id:167},{url_doi:bJ,title:bK,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:r,type:"academic",videos_home_url:"\u002Facademic_talks"}},indexMid:{articles:{recent:[{article_id:"7378",title:"Path planning method for USVs based on improved DWA and COLREGs",image:g,tag:"385-405",doi:"10.20517\u002Fir.2024.23",date_published:"1731945600",section:A,authors:"Shiqi Liu, ... Eugene Levin",journal_id:n,volume:p,year:Q,section_id:g,special_name:a,url_doi:"ir.2024.23",pdf:"https:\u002F\u002Ff.oaes.cc\u002Fxmlpdf\u002F2e840c3e-2dc5-4bea-bb3c-0b62e2478ad6\u002Fir4023.pdf",video_url:a,allauthors:"\u003Cimg src='https:\u002F\u002Fi.oaes.cc\u002Fimages\u002Fdefault_photo.png' class='author_photo' alt='Shiqi Liu'\u003E Shiqi Liu, \u003Cimg src='https:\u002F\u002Fi.oaes.cc\u002Fimages\u002Fdefault_photo.png' class='author_photo' alt='Xingmin Wang'\u003E Xingmin Wang, \u003Cimg src='https:\u002F\u002Fi.oaes.cc\u002Fimages\u002Fdefault_photo.png' class='author_photo' alt='Yang Wu'\u003E Yang Wu, \u003Cimg src='https:\u002F\u002Fi.oaes.cc\u002Fimages\u002Fdefault_photo.png' class='author_photo' alt='Qian Li'\u003E Qian Li, \u003Cimg src='https:\u002F\u002Fi.oaes.cc\u002Fimages\u002Fdefault_photo.png' class='author_photo' alt='Jiuxiang Yan'\u003E Jiuxiang Yan, \u003Cimg src='https:\u002F\u002Fi.oaes.cc\u002Fimages\u002Fdefault_photo.png' class='author_photo' alt='Eugene Levin'\u003E Eugene Levin",journal_path:j,video_img:a,image_list:g,abstract:"\u003Cp\u003EIn the navigation of unmanned surface vehicles (USVs), various types of obstacles may be encountered, which can be categorized into real-time collision avoidance among multiple USVs and obstacle avoidance between USVs and other obstacles. Most existing autonomous obstacle avoidance algorithms do not account for the nonlinear motion characteristics of USVs, often resulting in non-compliance with the International Regulations for Preventing Collisions at Sea (COLREGs) and a tendency to fall into local optima. To address these issues, this paper proposes a path planning algorithm that integrates the dynamic window approach (DWA) considering nonlinear characteristics with COLREGs, making the USV's motion trajectory more applicable to practical engineering scenarios. A kinematic mathematical model is established based on the motion characteristics of USVs, and an evaluation function for the optimal path is constructed using DWA. The fully informed search algorithm (FISA) is employed to select the optimal set of velocities and steering angles from the velocity sampling set, based on different cost calculation methods. The USVs use a laser radar for local obstacle detection, enabling real-time dynamic obstacle avoidance. To address the real-time collision avoidance problem among multiple USVs in open waters, the algorithm filters out COLREGs-compliant avoidance maneuvers during path planning. The correctness and feasibility of the fusion algorithm were verified through comparative simulations. In the simulated environment model, the influence of ocean currents on the USV was introduced, and multiple sets of experiments under different conditions were conducted to compare the motion trajectories, average travel distances, and average travel times of the USV. The simulation results indicate that the USV can perform accurate obstacle avoidance when encountering various types of obstacles. Compared to the traditional DWA algorithm, the proposed approach demonstrates advantages in terms of travel distance and travel time, while still achieving effective obstacle avoidance.\u003C\u002Fp\u003E"},{article_id:"7352",title:"Improved DDPG algorithm-based path planning for unmanned surface vehicles",image:g,tag:"363-84",doi:"10.20517\u002Fir.2024.22",date_published:bL,section:A,authors:"Menglong Hua, ... Zihao Chen",journal_id:n,volume:p,year:Q,section_id:g,special_name:a,url_doi:"ir.2024.22",pdf:"https:\u002F\u002Ff.oaes.cc\u002Fxmlpdf\u002F69bc157a-c03e-48d7-b08c-14cc87ec711f\u002Fir4022.pdf",video_url:a,allauthors:"\u003Cimg src='https:\u002F\u002Fi.oaes.cc\u002Fimages\u002Fdefault_photo.png' class='author_photo' alt='Menglong Hua'\u003E Menglong Hua, \u003Cimg src='https:\u002F\u002Fi.oaes.cc\u002Fimages\u002Fdefault_photo.png' class='author_photo' alt='Weixiang Zhou'\u003E Weixiang Zhou\u003Ca href='https:\u002F\u002Forcid.org\u002F0000-0002-6763-6391' target='_blank'\u003E\u003Cimg src='https:\u002F\u002Fi.oaes.cc\u002Fimages\u002Forcid.png' class='author_id' alt='Weixiang Zhou'\u003E\u003C\u002Fa\u003E, \u003Cimg src='https:\u002F\u002Fi.oaes.cc\u002Fimages\u002Fdefault_photo.png' class='author_photo' alt='Hongying Cheng'\u003E Hongying Cheng, \u003Cimg src='https:\u002F\u002Fi.oaes.cc\u002Fimages\u002Fdefault_photo.png' class='author_photo' alt='Zihao Chen'\u003E Zihao Chen",journal_path:j,video_img:a,image_list:g,abstract:"\u003Cp\u003EAs a promising mode of water transportation, unmanned surface vehicles (USVs) are used in various fields owing to their small size, high flexibility, favorable price, and other advantages. Traditional navigation algorithms are affected by various path planning issues. To address the limitations of the traditional deep deterministic policy gradient (DDPG) algorithm, namely slow convergence speed and sparse reward and punishment functions, we proposed an improved DDPG algorithm for USV path planning. First, the principle and workflow of the DDPG deep reinforcement learning (DRL) algorithm are described. Second, the improved method (based on the USVs kinematic model) is proposed, and a continuous state and action space is designed. The reward and punishment function are improved, and the principle of collision avoidance at sea is introduced. Dynamic region restriction is added, distant obstacles in the state space are ignored, and the nearby obstacles are observed to reduce the number of algorithm iterations and save computational resources. The introduction of a multi-intelligence approach combined with a prioritized experience replay mechanism accelerates algorithm convergence, thereby increasing the efficiency and robustness of training. Finally, through a combination of theory and simulation, the DDPG DRL is explored for USV obstacle avoidance and optimal path planning.\u003C\u002Fp\u003E"},{article_id:"7351",title:"Neurodynamics-based formation tracking control of leader-follower nonholonomic multiagent systems",image:g,tag:"339-62",doi:"10.20517\u002Fir.2024.21",date_published:bL,section:A,authors:"Xiao-Wen Zhao\u003Ca href='https:\u002F\u002Forcid.org\u002F0000-0001-7873-8708' target='_blank'\u003E\u003Cimg src='https:\u002F\u002Fi.oaes.cc\u002Fimages\u002Forcid.png' class='author_id' alt='Xiao-Wen Zhao'\u003E\u003C\u002Fa\u003E, ... Zhi-Wei Liu",journal_id:n,volume:p,year:Q,section_id:g,special_name:a,url_doi:"ir.2024.21",pdf:"https:\u002F\u002Ff.oaes.cc\u002Fxmlpdf\u002Ff39a731b-c097-4c5a-a2b2-af4573b6f3f1\u002Fir4021.pdf",video_url:a,allauthors:"\u003Cimg src='https:\u002F\u002Fi.oaes.cc\u002Fimages\u002Fdefault_photo.png' class='author_photo' alt='Xiao-Wen Zhao'\u003E Xiao-Wen Zhao\u003Ca href='https:\u002F\u002Forcid.org\u002F0000-0001-7873-8708' target='_blank'\u003E\u003Cimg src='https:\u002F\u002Fi.oaes.cc\u002Fimages\u002Forcid.png' class='author_id' alt='Xiao-Wen Zhao'\u003E\u003C\u002Fa\u003E, \u003Cimg src='https:\u002F\u002Fi.oaes.cc\u002Fimages\u002Fdefault_photo.png' class='author_photo' alt='Meng-Ke Li'\u003E Meng-Ke Li, \u003Cimg src='https:\u002F\u002Fi.oaes.cc\u002Fimages\u002Fdefault_photo.png' class='author_photo' alt='Qiang Lai'\u003E Qiang Lai, \u003Cimg src='https:\u002F\u002Fi.oaes.cc\u002Fimages\u002Fdefault_photo.png' class='author_photo' alt='Zhi-Wei Liu'\u003E Zhi-Wei Liu",journal_path:j,video_img:a,image_list:g,abstract:"\u003Cp\u003EThis paper uses a bioinspired neurodynamic (BIN) approach to investigate the formation control problem of leader-follower nonholonomic multiagent systems. In scenarios where not all followers can receive the leader's state, a distributed adaptive estimator is presented to estimate the leader's state. The distributed formation controller, designed using the backstepping technique, utilizes the estimated leader states and neighboring formation tracking error. To address the issue of impractical velocity jumps, a BIN-based approach is integrated into the backstepping controller. Furthermore, considering the practical applications of nonholonomic multiagent systems, a backstepping controller with a saturation velocity constraint is proposed. Rigorous proofs are provided. Finally, the effectiveness of the presented formation control law is illustrated through numerical simulations.\u003C\u002Fp\u003E"},{article_id:"7277",title:"Intelligent prediction of rail corrugation evolution trend based on self-attention bidirectional TCN and GRU",image:g,tag:"318-38",doi:"10.20517\u002Fir.2024.20",date_published:"1729180800",section:A,authors:"Jian-Hua Liu, ... Wei-Wei Yang",journal_id:n,volume:p,year:Q,section_id:g,special_name:a,url_doi:"ir.2024.20",pdf:"https:\u002F\u002Ff.oaes.cc\u002Fxmlpdf\u002F80d245c1-6593-4d42-8e24-7a9b32dfa757\u002Fir4020.pdf",video_url:"https:\u002F\u002Fv.oaes.cc\u002Fuploads\u002F20241021\u002F6879d4f4c749472a807548446dea5a90.mp4",allauthors:"\u003Cimg src='https:\u002F\u002Fi.oaes.cc\u002Fimages\u002Fdefault_photo.png' class='author_photo' alt='Jian-Hua Liu'\u003E Jian-Hua Liu, \u003Cimg src='https:\u002F\u002Fi.oaes.cc\u002Fimages\u002Fdefault_photo.png' class='author_photo' alt='Wei-Hao Yang'\u003E Wei-Hao Yang, \u003Cimg src='https:\u002F\u002Fi.oaes.cc\u002Fimages\u002Fdefault_photo.png' class='author_photo' alt='Jing He'\u003E Jing He, \u003Cimg src='https:\u002F\u002Fi.oaes.cc\u002Fimages\u002Fdefault_photo.png' class='author_photo' alt='Zhong-Mei Wang'\u003E Zhong-Mei Wang, \u003Cimg src='https:\u002F\u002Fi.oaes.cc\u002Fimages\u002Fdefault_photo.png' class='author_photo' alt='Lin Jia'\u003E Lin Jia, \u003Cimg src='https:\u002F\u002Fi.oaes.cc\u002Fimages\u002Fdefault_photo.png' class='author_photo' alt='Chang-Fan Zhang'\u003E Chang-Fan Zhang, \u003Cimg src='https:\u002F\u002Fi.oaes.cc\u002Fimages\u002Fdefault_photo.png' class='author_photo' alt='Wei-Wei Yang'\u003E Wei-Wei Yang",journal_path:j,video_img:"https:\u002F\u002Fi.oaes.cc\u002Fuploads\u002F20241021\u002Fd5f21a891ccd43739a59e3f817c67e42.png",image_list:g,abstract:"\u003Cp\u003EAnalyzing the evolution trend of rail corrugation using signal processing and deep learning is critical for railway safety, as current traditional methods struggle to capture the complex evolution of corrugation. This present study addresses the challenge of accurately capturing this trend, which relies significantly on expert judgment, by proposing an intelligent prediction method based on self-attention (SA), a bidirectional temporal convolutional network (TCN), and a bidirectional gated recurrent unit (GRU). First, multidomain feature extraction and adaptive feature screening were used to obtain the optimal feature set. These features were then combined with principal component analysis (PCA) and the Mahalanobis distance (MD) method to construct a comprehensive health indicator (CHI) that reflects the evolution of rail corrugation. A bidirectional fusion model architecture was employed to capture the temporal correlations between forward and backward information during corrugation evolution, with SA embedded in the model to enhance the focus on key information. The outcome was a rail corrugation trend prediction network that combined a bidirectional TCN, bidirectional GRU, and SA. Subsequently, a multi-strategy improved crested porcupine optimizer (CPO) algorithm was constructed to automatically obtain the optimal network hyperparameters. The proposed method was validated with on-site rail corrugation data, demonstrating superior predictive performance compared to other advanced methods. In summary, the proposed method can accurately predict the evolution trend of rail corrugation, offering a valuable tool for on-site railway maintenance.\u003C\u002Fp\u003E"},{article_id:"7201",title:"Distributed model predictive control for unmanned aerial vehicles and vehicle platoon systems: a review",image:bM,tag:"293-317",doi:"10.20517\u002Fir.2024.19",date_published:"1727107200",section:u,authors:"Yang Peng\u003Ca href='https:\u002F\u002Forcid.org\u002F0009-0008-0113-850X' target='_blank'\u003E\u003Cimg src='https:\u002F\u002Fi.oaes.cc\u002Fimages\u002Forcid.png' class='author_id' alt='Yang Peng'\u003E\u003C\u002Fa\u003E, ... Yunkai Lv\u003Ca href='https:\u002F\u002Forcid.org\u002F0000-0001-5212-8629' target='_blank'\u003E\u003Cimg src='https:\u002F\u002Fi.oaes.cc\u002Fimages\u002Forcid.png' class='author_id' alt='Yunkai Lv'\u003E\u003C\u002Fa\u003E",journal_id:n,volume:p,year:Q,section_id:bN,special_name:bk,url_doi:"ir.2024.19",pdf:"https:\u002F\u002Ff.oaes.cc\u002Fxmlpdf\u002Fe7dd7fcd-3c45-4298-b9e2-ec23a759ec19\u002Fir4019.pdf",video_url:a,allauthors:"\u003Cimg src='https:\u002F\u002Fi.oaes.cc\u002Fimages\u002Fdefault_photo.png' class='author_photo' alt='Yang Peng'\u003E Yang Peng\u003Ca href='https:\u002F\u002Forcid.org\u002F0009-0008-0113-850X' target='_blank'\u003E\u003Cimg src='https:\u002F\u002Fi.oaes.cc\u002Fimages\u002Forcid.png' class='author_id' alt='Yang Peng'\u003E\u003C\u002Fa\u003E, \u003Cimg src='https:\u002F\u002Fi.oaes.cc\u002Fimages\u002Fdefault_photo.png' class='author_photo' alt='Huaicheng Yan'\u003E Huaicheng Yan\u003Ca href='https:\u002F\u002Forcid.org\u002F0000-0001-5496-1809' target='_blank'\u003E\u003Cimg src='https:\u002F\u002Fi.oaes.cc\u002Fimages\u002Forcid.png' class='author_id' alt='Huaicheng Yan'\u003E\u003C\u002Fa\u003E, \u003Cimg src='https:\u002F\u002Fi.oaes.cc\u002Fimages\u002Fdefault_photo.png' class='author_photo' alt='Kai Rao'\u003E Kai Rao\u003Ca href='https:\u002F\u002Forcid.org\u002F0009-0008-0113-850X' target='_blank'\u003E\u003Cimg src='https:\u002F\u002Fi.oaes.cc\u002Fimages\u002Forcid.png' class='author_id' alt='Kai Rao'\u003E\u003C\u002Fa\u003E, \u003Cimg src='https:\u002F\u002Fi.oaes.cc\u002Fimages\u002Fdefault_photo.png' class='author_photo' alt='Penghui Yang'\u003E Penghui Yang\u003Ca href='https:\u002F\u002Forcid.org\u002F0000-0001-5496-1809' target='_blank'\u003E\u003Cimg src='https:\u002F\u002Fi.oaes.cc\u002Fimages\u002Forcid.png' class='author_id' alt='Penghui Yang'\u003E\u003C\u002Fa\u003E, \u003Cimg src='https:\u002F\u002Fi.oaes.cc\u002Fimages\u002Fdefault_photo.png' class='author_photo' alt='Yunkai Lv'\u003E Yunkai Lv\u003Ca href='https:\u002F\u002Forcid.org\u002F0000-0001-5212-8629' target='_blank'\u003E\u003Cimg src='https:\u002F\u002Fi.oaes.cc\u002Fimages\u002Forcid.png' class='author_id' alt='Yunkai Lv'\u003E\u003C\u002Fa\u003E",journal_path:j,video_img:a,image_list:bM,abstract:"\u003Cp\u003EThis paper reviews the application of distributed model predictive control (DMPC) for autonomous intelligent systems (AIS) with unmanned aerial vehicles (UAVs) and vehicle platoon systems. DMPC is an optimal control method that formulates and solves optimization problems to adjust control strategies by predicting future states based on system models while managing constraints, and this technique has been applied to an increasing number of industrial areas. As the essential parts of AIS, UAVs and vehicle platoon systems have received extensive attention in the civil, industrial, and military fields. DMPC has the ability to quickly solve optimization problems in real-time while taking into account the prediction of the future state of the system, which fits in well with the ability of AIS to predict the environment when making decisions, so the application of DMPC in AIS has a natural advantage. This paper first introduces the basic principles of DMPC and the theoretical results in multi-agent systems (MASs). It then reviews the application of DMPC methods to UAVs and vehicle platoon systems. Finally, the challenges of the existing methods are summarized to offer insights to advance the future development of DMPC in practical applications.\u003C\u002Fp\u003E"}],recent_ids:"7378,7352,7351,7277,7201",view:[{article_id:bO,title:bj,image:g,tag:bP,doi:bQ,date_published:bl,section:u,authors:bR,journal_id:n,volume:f,year:d,section_id:g,special_name:a,url_doi:bi,pdf:bS,video_url:bT,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:j,video_img:bU,image_list:g,abstract:bV},{article_id:bW,title:bX,image:az,tag:bY,doi:bZ,date_published:b_,section:u,authors:b$,journal_id:n,volume:k,year:s,section_id:g,special_name:a,url_doi:ca,pdf:cb,video_url:a,allauthors:"Danni Wu, Zichen Liang, Guang Chen",journal_path:j,video_img:a,image_list:az,abstract:cc},{article_id:"5115",title:"Deep reinforcement learning for real-world quadrupedal locomotion: a comprehensive review",image:g,tag:"275-97",doi:"10.20517\u002Fir.2022.20",date_published:"1661961600",section:u,authors:"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",journal_id:n,volume:k,year:s,section_id:g,special_name:a,url_doi:"ir.2022.20",pdf:"https:\u002F\u002Ff.oaes.cc\u002Fxmlpdf\u002F12bbd333-926b-4682-b8bc-e9945c452687\u002F5115.pdf",video_url:"https:\u002F\u002Fv1.oaepublish.com\u002Ffiles\u002Ftalkvideo\u002F5115.mp4",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:j,video_img:"https:\u002F\u002Fi.oaes.cc\u002Fuploads\u002F20240205\u002F647c73d0a5884b76a7d7697b7584a99d.jpg",image_list:g,abstract:"\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"},{article_id:cd,title:ce,image:aA,tag:cf,doi:cg,date_published:ch,section:u,authors:ci,journal_id:n,volume:f,year:d,section_id:bm,special_name:R,url_doi:cj,pdf:ck,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:j,video_img:a,image_list:aA,abstract:cl},{article_id:"4353",title:"Bio-inspired intelligence with applications to robotics: a survey",image:g,tag:"58-83",doi:"10.20517\u002Fir.2021.08",date_published:bl,section:u,authors:"Junfei Li, ... Simon X. Yang",journal_id:n,volume:f,year:d,section_id:"1201",special_name:bn,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:j,video_img:"https:\u002F\u002Fi.oaes.cc\u002Fuploads\u002F20240205\u002F29f1fb64c19a470fae5fe533b38418e7.jpg",image_list:g,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:bO,title:bj,image:g,tag:bP,doi:bQ,date_published:bl,section:u,authors:bR,journal_id:n,volume:f,year:d,section_id:g,special_name:a,url_doi:bi,pdf:bS,video_url:bT,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:j,video_img:bU,image_list:g,abstract:bV},{article_id:"5329",title:"Formation control of multiple autonomous underwater vehicles: a review",image:cm,tag:"1-22",doi:"10.20517\u002Fir.2023.01",date_published:"1673625600",section:u,authors:"Tao Yan, ... S. Andrew Gadsden",journal_id:n,volume:m,year:aB,section_id:g,special_name:a,url_doi:"ir.2023.01",pdf:"https:\u002F\u002Ff.oaes.cc\u002Fxmlpdf\u002Fffa25840-c01a-4638-9680-a6af89417f6a\u002F5329.pdf",video_url:"https:\u002F\u002Fv1.oaepublish.com\u002Ffiles\u002Ftalkvideo\u002F5329.mp4",allauthors:"\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",journal_path:j,video_img:"https:\u002F\u002Fi.oaes.cc\u002Fuploads\u002F20240205\u002Fc594ecf74c2a433e90b38a7211bdd0e7.jpg",image_list:cm,abstract:"\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"},{article_id:bW,title:bX,image:az,tag:bY,doi:bZ,date_published:b_,section:u,authors:b$,journal_id:n,volume:k,year:s,section_id:g,special_name:a,url_doi:ca,pdf:cb,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:j,video_img:a,image_list:az,abstract:cc},{article_id:cd,title:ce,image:aA,tag:cf,doi:cg,date_published:ch,section:u,authors:ci,journal_id:n,volume:f,year:d,section_id:bm,special_name:R,url_doi:cj,pdf:ck,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:j,video_img:a,image_list:aA,abstract:cl},{article_id:"5459",title:"GMAW welding procedure expert system based on machine learning",image:cn,tag:"56-75",doi:"10.20517\u002Fir.2023.03",date_published:"1677600000",section:A,authors:"Xuewu Wang\u003Ca href='https:\u002F\u002Forcid.org\u002F0000-0003-4072-8779' target='_blank'\u003E\u003Cimg src='https:\u002F\u002Fi.oaes.cc\u002Fimages\u002Forcid.png' class='author_id' alt='Xuewu Wang'\u003E\u003C\u002Fa\u003E, ... Huaicheng Yan\u003Ca href='https:\u002F\u002Forcid.org\u002F0000-0001-5496-1809' target='_blank'\u003E\u003Cimg src='https:\u002F\u002Fi.oaes.cc\u002Fimages\u002Forcid.png' class='author_id' alt='Huaicheng Yan'\u003E\u003C\u002Fa\u003E",journal_id:n,volume:m,year:aB,section_id:bN,special_name:bk,url_doi:"ir.2023.03",pdf:"https:\u002F\u002Ff.oaes.cc\u002Fxmlpdf\u002F21daad8e-7cfe-4d60-8835-b06300ee4b4d\u002F5459.pdf",video_url:"https:\u002F\u002Fv1.oaepublish.com\u002Ffiles\u002Ftalkvideo\u002F5459.mp4",allauthors:"\u003Cimg src='https:\u002F\u002Fi.oaes.cc\u002Fimages\u002Fdefault_photo.png' class='author_photo' alt='Xuewu Wang'\u003E Xuewu Wang\u003Ca href='https:\u002F\u002Forcid.org\u002F0000-0003-4072-8779' target='_blank'\u003E\u003Cimg src='https:\u002F\u002Fi.oaes.cc\u002Fimages\u002Forcid.png' class='author_id' alt='Xuewu Wang'\u003E\u003C\u002Fa\u003E, \u003Cimg src='https:\u002F\u002Fi.oaes.cc\u002Fimages\u002Fdefault_photo.png' class='author_photo' alt='Qian Chen'\u003E Qian Chen\u003Ca href='https:\u002F\u002Forcid.org\u002F0000-0003-3732-8248' target='_blank'\u003E\u003Cimg src='https:\u002F\u002Fi.oaes.cc\u002Fimages\u002Forcid.png' class='author_id' alt='Qian Chen'\u003E\u003C\u002Fa\u003E, \u003Cimg src='https:\u002F\u002Fi.oaes.cc\u002Fimages\u002Fdefault_photo.png' class='author_photo' alt='Hao Sun'\u003E Hao Sun, \u003Cimg src='https:\u002F\u002Fi.oaes.cc\u002Fimages\u002Fdefault_photo.png' class='author_photo' alt='Xiuwei Wang'\u003E Xiuwei Wang, \u003Cimg src='https:\u002F\u002Fi.oaes.cc\u002Fimages\u002Fdefault_photo.png' class='author_photo' alt='Huaicheng Yan'\u003E Huaicheng Yan\u003Ca href='https:\u002F\u002Forcid.org\u002F0000-0001-5496-1809' target='_blank'\u003E\u003Cimg src='https:\u002F\u002Fi.oaes.cc\u002Fimages\u002Forcid.png' class='author_id' alt='Huaicheng Yan'\u003E\u003C\u002Fa\u003E",journal_path:j,video_img:"https:\u002F\u002Fi.oaes.cc\u002Fuploads\u002F20240205\u002F0fab454c290048b6941990067715fe61.jpg",image_list:cn,abstract:"\u003Cp\u003EIn order to simplify the robot preparation before welding and improve the automation of the whole welding process, an intelligent expert system for Gas Metal Arc Welding is designed in this paper. In the system, the user inputs the initial welding information and the output interface displays suitable welding procedure parameter schemes. The user can choose the schemes according to the actual requirements or directly generate the welding procedure specification required by the enterprise format for direct use. In addition, the system also combines the database technology and XGBoost algorithm in the field of machine learning, migrates the model trained on the data set to predict the welding raw data, accumulates more data for daily use to optimize the model, which makes the whole system more systematic and intelligent, and achieves the goal of more accurate use.\u003C\u002Fp\u003E"}],download_ids:"4325,5329,4825,4477,5459"},cited:[{article_id:"5424",title:"An overview of intelligent image segmentation using active contour models",image:co,tag:"23-55",doi:"10.20517\u002Fir.2023.02",date_published:"1676995200",section:u,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:n,volume:m,year:aB,section_id:g,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:j,video_img:a,image_list:co,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:"5170",title:"A node selection algorithm to graph-based multi-waypoint optimization navigation and mapping",image:g,tag:"333-54",doi:"10.20517\u002Fir.2022.21",date_published:"1665504000",section:A,authors:"Timothy Sellers, ... Junfeng Ma",journal_id:n,volume:k,year:s,section_id:g,special_name:a,url_doi:"ir.2022.21",pdf:"https:\u002F\u002Ff.oaes.cc\u002Fxmlpdf\u002Fb6bde10c-1a17-43d0-9758-88dabfd18e62\u002F5170.pdf",video_url:"https:\u002F\u002Fv1.oaepublish.com\u002Ffiles\u002Ftalkvideo\u002F5170.mp4",allauthors:"\u003Cimg src='https:\u002F\u002Fi.oaes.cc\u002Fimages\u002Fdefault_photo.png' class='author_photo' alt='Timothy Sellers'\u003E Timothy Sellers, \u003Cimg src='https:\u002F\u002Fi.oaes.cc\u002Fimages\u002Fdefault_photo.png' class='author_photo' alt='Tingjun Lei'\u003E Tingjun Lei, \u003Cimg src='https:\u002F\u002Fi.oaes.cc\u002Fimages\u002Fdefault_photo.png' class='author_photo' alt='Chaomin Luo'\u003E Chaomin Luo\u003Ca href='http:\u002F\u002Forcid.org\u002F0000-0002-7578-3631' target='_blank'\u003E\u003Cimg src='https:\u002F\u002Fi.oaes.cc\u002Fimages\u002Forcid.png' class='author_id' alt='Chaomin Luo'\u003E\u003C\u002Fa\u003E, \u003Cimg src='https:\u002F\u002Fi.oaes.cc\u002Fimages\u002Fdefault_photo.png' class='author_photo' alt='Gene Eu Jan'\u003E Gene Eu Jan, \u003Cimg src='https:\u002F\u002Fi.oaes.cc\u002Fimages\u002Fdefault_photo.png' class='author_photo' alt='Junfeng Ma'\u003E Junfeng Ma",journal_path:j,video_img:"https:\u002F\u002Fi.oaes.cc\u002Fuploads\u002F20240205\u002Fb263c1d7fa6a447f89d0b42385dcee47.jpg",image_list:g,abstract:"\u003Cp\u003EAutonomous robot multi-waypoint navigation and mapping have been demanded in many real-world applications found in search and rescue (SAR), environmental exploration, and disaster response. Many solutions to this issue have been discovered via graph-based methods in need of switching the robotos trajectory between the nodes and edges within the graph to create a trajectory for waypoint-to-waypoint navigation. However, studies of how waypoints are locally bridged to nodes or edges on the graphs have not been adequately undertaken. In this paper, an adjacent node selection (ANS) algorithm is developed to implement such a protocol to build up regional path from waypoints to nearest nodes or edges on the graph. We propose this node selection algorithm along with the generalized Voronoi diagram (GVD) and Improved Particle Swarm Optimization (IPSO) algorithm as well as a local navigator to solve the safety-aware concurrent graph-based multi-waypoint navigation and mapping problem. Firstly, GVD is used to form a Voronoi diagram in an obstacle populated environment to construct safety-aware routes. Secondly, the sequence of multiple waypoints is created by the IPSO algorithm to minimize the total travelling cost. Thirdly, while the robot attempts to visit multiple waypoints, it traverses along the edges of the GVD to plan a collision-free trajectory. The regional path from waypoints to the nearest nodes or edges needs to be created to join the trajectory by the proposed ANS algorithm. Finally, a sensor-based histogram local reactive navigator is adopted for moving obstacle avoidance while local maps are constructed as the robot moves. An improved \u003Ci\u003EB\u003C\u002Fi\u003E-spline curve-based smooth scheme is adopted that further refines the trajectory and enables the robot to be navigated smoothly. Simulation and comparison studies validate the effectiveness and robustness of the proposed model.\u003C\u002Fp\u003E"},{article_id:"4665",title:bK,image:g,tag:"72-88",doi:"10.20517\u002Fir.2021.16",date_published:"1647878400",section:A,authors:"Ibrahima Bah, Yu Xue\u003Ca href='https:\u002F\u002Forcid.org\u002F0000-0002-9069-7547' target='_blank'\u003E\u003Cimg src='https:\u002F\u002Fi.oaes.cc\u002Fimages\u002Forcid.png' class='author_id' alt='Yu Xue'\u003E\u003C\u002Fa\u003E",journal_id:n,volume:k,year:s,section_id:bm,special_name:R,url_doi:bJ,pdf:"https:\u002F\u002Ff.oaes.cc\u002Fxmlpdf\u002F5375e325-1576-4eb7-a843-9f20e8b251f3\u002F4665.pdf",video_url:"https:\u002F\u002Fv1.oaepublish.com\u002Ffiles\u002Ftalkvideo\u002F4665.mp4",allauthors:"\u003Cimg src='https:\u002F\u002Fi.oaes.cc\u002Fimages\u002Fdefault_photo.png' class='author_photo' alt='Ibrahima Bah'\u003E Ibrahima Bah, \u003Cimg src='https:\u002F\u002Fi.oaes.cc\u002Fimages\u002Fdefault_photo.png' class='author_photo' alt='Yu Xue'\u003E Yu Xue\u003Ca href='https:\u002F\u002Forcid.org\u002F0000-0002-9069-7547' target='_blank'\u003E\u003Cimg src='https:\u002F\u002Fi.oaes.cc\u002Fimages\u002Forcid.png' class='author_id' alt='Yu Xue'\u003E\u003C\u002Fa\u003E",journal_path:j,video_img:"https:\u002F\u002Fi.oaes.cc\u002Fuploads\u002F20240205\u002F343f170e51c64018b64053b2c1df0731.jpg",image_list:g,abstract:"\u003Cp\u003EEmotion on our face can determine our feelings, mental state and can directly impact our decisions. Humans are subjected to undergo an emotional change in relation to their living environment and or at a present circumstance. These emotions can be anger, disgust, fear, sadness, happiness, surprise or neutral. Due to the intricacy and nuance of facial expressions and their relationship to emotions, accurate facial expression identification remains a difficult undertaking. As a result, we provide an end-to-end system that uses residual blocks to identify emotions and improve accuracy in this research field. After receiving a facial image, the framework returns its emotional state. The accuracy obtained on the test set of FERGIT dataset (an extension of the FER2013 dataset with 49300 images) was 75%. This proves the efficiency of the model in classifying facial emotions as this database poses a bunch of challenges such as imbalanced data, intraclass variance, and occlusion. To ensure the performance of our model, we also tested it on the CK+ database and its output accuracy was 97% on the test set.\u003C\u002Fp\u003E"}],cited_ids:"5424,5170,4665"},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:r,deadline:cp,image:"https:\u002F\u002Fi.oaes.cc\u002Fpublic\u002Fissue_special\u002F1005\u002F54_ir_sp1005.jpg",sub_link:"https:\u002F\u002Foaemesas.com\u002Flogin?JournalId=ir&IssueId=IR211221",email:B,issue_flyer:a,title:"Topic: Collaborative Optimization and Control of Intelligent Unmanned Autonomous Systems",count:v,author:["Prof. Hao Zhang","Dr. Changzhu Zhang"]},{section_id:1328,journal_id:r,deadline:cp,image:"https:\u002F\u002Fi.oaes.cc\u002Fpublic\u002Fissue_special\u002F1328\u002F82_ir_sp1328.jpg",sub_link:"https:\u002F\u002Foaemesas.com\u002Flogin?JournalId=ir&IssueId=IR221125",email:cq,issue_flyer:a,title:bk,count:v,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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Robot",ad:[],colour_tag:bB,score:a,mobile_top_img:a,impact_factor:[{factor:bG,url:aT},{factor:i,url:a}],rgba:bE,log_image:bF},webinfo:{},searchKey:a,loading:kL,appid:a,videoPlay:{show:aE,href:a}},editer:{editList:{list:{}}},userdata:{showLogin:aE,logined:aE}},serverRendered:kL,routePath:bw,config:{_app:{basePath:kM,assetsPath:kM,cdnURL:"https:\u002F\u002Fg.oaes.cc\u002Foae\u002Fnuxt\u002F"}}}}("",null,1,"2021",4,"1","0",0,"https:\u002F\u002Fi.oaes.cc\u002Fuploads\u002F20240506\u002Fea3d9071c35b4bf3982ffe25f1083620.png","ir","2",3,"3","40",2,"4",5,40,"2022","5","Review",6,"6","[]","https:\u002F\u002Fmjl.clarivate.com\u002Fsearch-results","2017","Research Article","assistant_editor@intellrobot.com","Intelligence & Robotics","Complex Engineering Systems",9,"33",10,15,20,"Lens",34,7,35,"2020",32,"image","2024","Topic: Evolutionary Computation for Deep Learning and Machine Learning",65,22,90,38,12,29,"Extracellular Vesicles and Circulating Nucleic Acids","Microbiome Research Reports","One Health & Implementation Research","Chemical Synthesis","Energy Materials","Journal of Materials Informatics","Microstructures","Minerals and Mineral Materials","Soft Science","Disaster Prevention and Resilience","Green Manufacturing Open","Journal of Smart Environments and Green Computing","Journal of Surveillance, Security and Safety","Ageing and Neurodegenerative Diseases","Artificial Intelligence Surgery","Cancer Drug Resistance","Connected Health And Telemedicine","Hepatoma Research","Journal of Cancer Metastasis and Treatment","Journal of Translational Genetics and Genomics","Metabolism and Target Organ Damage","Mini-invasive Surgery","Plastic and Aesthetic Research","Rare Disease and Orphan Drugs Journal","The Journal of Cardiovascular Aging","Vessel Plus","Carbon Footprints","Journal of Environmental Exposure Assessment","Water Emerging Contaminants & Nanoplastics","https:\u002F\u002Fimage.oaes.cc\u002Faa73a59e-0434-480a-98d9-6257473c8a85\u002F4825-coverimg.jpg","https:\u002F\u002Fimage.oaes.cc\u002F7cb2c33c-13bc-40ef-8ac0-4d653a4180fd\u002F4477-coverimg.jpg","2023",497,287,false,23,14,25,30,8,24,"ESCI, CAS, Dimensions, Lens, CNKI",17,11,55,"2015","ESCI, CAS, Scopus, Wanfang Data, CNKI, Dimensions, Embase",13,"39","https:\u002F\u002Fwww.scopus.com\u002Fsourceid\u002F21101199351",39,16,"Journal of Unexplored Medical Data",18,21,26,"ESCI, Scopus, CAS, Dimensions, Lens, CNKI","https:\u002F\u002Fi.oaes.cc\u002Fuploads\u002F20240809\u002Fccc8ac23b789404781787655e4495af4.png",33,"Neuroimmunology and Neuroinflammation","2014",31,"ISSN XXXX-XXXX (Coming soon)","Soil Health","Space Mission Planning & Operations","Stomatological Disease and Science","ir.2021.02","Federated reinforcement learning: techniques, applications, and open challenges","Topic: Robot System Intelligentization and Application: Learning, Control and Decision","1633968000","1002","Topic: Advances in Human-Assistive Technologies and Human-Robot Interactions",96,95,135,"https:\u002F\u002Fi.oaes.cc\u002Fimages_2018\u002Foae\u002Fcover_comengsys.jpg","comengsys","Hamid Reza Karimi",58,"Contact Us","\u002Fir","7","8","9","11","#0047bb","https:\u002F\u002Fi.oaes.cc\u002Fuploads\u002F20231121\u002F59802903b17e4eebae240e004311d193.jpg","Simon X. Yang","rgb(0,71,187)","https:\u002F\u002Fi.oaes.cc\u002Fupload\u002Fjournal_logo\u002Fir.png","https:\u002F\u002Fi.oaes.cc\u002Fuploads\u002F20240813\u002F49390c7e86ab40a58ee862e8c1af65ba.png","Two Associate Editors Recognized on Clarivate Highly Cited Researchers 2024 List","https:\u002F\u002Fwww.oaepublish.com\u002Fnews\u002Fir.852","ir.2021.16","Facial expression recognition using adapted residual based deep neural network","1731427200","https:\u002F\u002Fimage.oaes.cc\u002Fe7dd7fcd-3c45-4298-b9e2-ec23a759ec19\u002Fir4019-coverimg.jpg","1328","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","4477","Rail track condition monitoring: a review on deep learning approaches","151-75","10.20517\u002Fir.2021.14","1640793600","Albert Ji, ... Yang Thee Quek","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","https:\u002F\u002Fimage.oaes.cc\u002Fffa25840-c01a-4638-9680-a6af89417f6a\u002F5329-coverimg.jpg","https:\u002F\u002Fimage.oaes.cc\u002F21daad8e-7cfe-4d60-8835-b06300ee4b4d\u002F5459-coverimg.jpg","https:\u002F\u002Fimage.oaes.cc\u002F220eb536-974c-408a-8b4b-9f1a7e56035f\u002F5424-coverimg.jpg",1798675200,"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","About","Editorial Policies","Journals","Academic Talks","Biology & Life Science","Chemistry & Materials Science","Computer Science & Engineering","Medicine & Public Health","10","Environmental Science","#837fbc","ISSN 2769-5301 (Online)","https:\u002F\u002Fi.oaes.cc\u002Fuploads\u002F20231218\u002F6135b005a8674b878a7d1a5c91ac9869.jpg","https:\u002F\u002Fwww.oaepublish.com\u002Fand","Wei-Dong Le","CAS, Dimensions, Lens, CNKI","rgb(99,94,171)","https:\u002F\u002Fi.oaes.cc\u002Fupload\u002Fjournal_logo\u002Fand.jpg","and","#030072","ISSN 2771-0408 (Online)","https:\u002F\u002Fi.oaes.cc\u002Fuploads\u002F20231110\u002F14bc775310574f91b457ff09d6c6d950.jpg","https:\u002F\u002Fwww.oaepublish.com\u002Fais","Andrew A. Gumbs","36","ESCI, Scopus, Dimensions, Lens","rgb(3,0,114)","https:\u002F\u002Fi.oaes.cc\u002Fupload\u002Fjournal_logo\u002Fais.png","https:\u002F\u002Fi.oaes.cc\u002Fuploads\u002F20240813\u002Fb7358d0632aa413fa84630ef0a2fb7a0.png","https:\u002F\u002Fwww.scopus.com\u002Fsourceid\u002F21101189039","ais",83,"#e8b475","ISSN 2578-532X (Online)","https:\u002F\u002Fi.oaes.cc\u002Fuploads\u002F20231113\u002F9db4b73f2e954900807499f00094fcf0.jpg","https:\u002F\u002Fwww.oaepublish.com\u002Fcdr","Godefridus J. (Frits) Peters","2018","337","ESCI, PMC, Scopus, CAS, CNKI, Dimensions, Lens, Embase","rgb(224,155,71)","https:\u002F\u002Fi.oaes.cc\u002Fupload\u002Fjournal_logo\u002Fcdr.jpg","https:\u002F\u002Fi.oaes.cc\u002Fuploads\u002F20240104\u002F502e35965fc448e0bc9f2d6c1b0bb3d7.png","https:\u002F\u002Fwww.ncbi.nlm.nih.gov\u002Fpmc\u002Fjournals\u002F4180\u002F","https:\u002F\u002Fi.oaes.cc\u002Fuploads\u002F20240611\u002F5def3ab5829743ce8d55e664cabe0dcb.png","https:\u002F\u002Fwww.scopus.com\u002Fsourceid\u002F21101047803","https:\u002F\u002Fi.oaes.cc\u002Fuploads\u002F20240620\u002Fed79fcbfce8e4cd38a36cc4d5f7d473d.png","https:\u002F\u002Fjcr.clarivate.com\u002Fjcr-jp\u002Fjournal-profile?journal=CANCER%20DRUG%20RESIST&year=2022","https:\u002F\u002Fi.oaes.cc\u002Fuploads\u002F20240620\u002F86311aafb7a945a98a7ee95c3627173b.png","cdr",418,"#1d57a5","ISSN 2770-6249 (Online)","https:\u002F\u002Fwww.oaepublish.com\u002Fcomengsys","37","Scopus, Dimensions, Lens","rgb(29,87,165)","https:\u002F\u002Fi.oaes.cc\u002Fupload\u002Fjournal_logo\u002Fces.png","https:\u002F\u002Fi.oaes.cc\u002Fuploads\u002F20240722\u002F4748814aab87441d809e5b9ddcd9d56e.png","https:\u002F\u002Fwww.scopus.com\u002Fsourceid\u002F21101189038",71,"#75ac9d","ISSN 2993-2920 (Online)","https:\u002F\u002Fi.oaes.cc\u002Fuploads\u002F20231218\u002F0b7a80c0f6e545818e143ea48fe9be90.jpg","https:\u002F\u002Fwww.oaepublish.com\u002Fchatmed","Yuan-Ting Zhang","20","rgb(117, 172, 157)","https:\u002F\u002Fi.oaes.cc\u002Fupload\u002Fjournal_logo\u002Fchatmed.jpg","chatmed","#00a5b3","ISSN 2769-5247 (Online)","https:\u002F\u002Fi.oaes.cc\u002Fuploads\u002F20231121\u002F1598f33bf2284642830511e489eb31bf.jpg","https:\u002F\u002Fwww.oaepublish.com\u002Fcs","Bao-Lian Su","67","Scopus, CAS, Dimensions, Lens, CNKI","rgb(0,165,179)","https:\u002F\u002Fi.oaes.cc\u002Fupload\u002Fjournal_logo\u002Fcs.jpg","https:\u002F\u002Fi.oaes.cc\u002Fuploads\u002F20240813\u002F6d91bea2ff474cb9b7ec10017b26ec2a.png","https:\u002F\u002Fwww.scopus.com\u002Fsourceid\u002F21101189036","cs",157,51,"#6cc24a","ISSN 2831-932X (Online)","https:\u002F\u002Fi.oaes.cc\u002Fuploads\u002F20231110\u002Fb1a3c337f6a54bcb945e0d9ab6afe0ec.jpg","https:\u002F\u002Fwww.oaepublish.com\u002Fcf","Yong Geng","16","rgb(86,164,55)","https:\u002F\u002Fi.oaes.cc\u002Fupload\u002Fjournal_logo\u002Fcf.jpg","cf",45,"#01588b","ISSN 2832-4056 (Online)","https:\u002F\u002Fi.oaes.cc\u002Fuploads\u002F20231110\u002Fe5808de306274ba287f6f24bf45eb910.jpg","https:\u002F\u002Fwww.oaepublish.com\u002Fdpr","Jie Li","14","rgb(1,88,139)","https:\u002F\u002Fi.oaes.cc\u002Fupload\u002Fjournal_logo\u002Fdpr.jpg","dpr","#008c15","ISSN 2770-5900 (Online)","https:\u002F\u002Fi.oaes.cc\u002Fuploads\u002F20231110\u002Fe1df1779a10c4a7486e89c5f5c4994f3.jpg","https:\u002F\u002Fwww.oaepublish.com\u002Fenergymater","Yuping Wu","92","rgb(0,140,21)","https:\u002F\u002Fi.oaes.cc\u002Fupload\u002Fjournal_logo\u002Fem.jpg","https:\u002F\u002Fi.oaes.cc\u002Fuploads\u002F20240621\u002Fab41932db942489c841586b032349a31.png","https:\u002F\u002Fi.oaes.cc\u002Fuploads\u002F20240625\u002F3f1358bb24954c8eae11907a77ab8b18.png","energymater",196,"#b15ee9","ISSN 2767-6641 (Online)","https:\u002F\u002Fi.oaes.cc\u002Fuploads\u002F20231110\u002Fb7fc9d6a51bf4adda4ff6dec5002690b.jpg","https:\u002F\u002Fwww.oaepublish.com\u002Fevcna","Yoke Peng Loh","68","ESCI, Scopus, CAS, CNKI, Dimensions, 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Cao","465","rgb(239,108,62)","https:\u002F\u002Fi.oaes.cc\u002Fupload\u002Fjournal_logo\u002Fhr.jpg","https:\u002F\u002Fi.oaes.cc\u002Fuploads\u002F20240313\u002Fee358186a06449d5b2c8ea15f712cbed.png","https:\u002F\u002Fwww.scopus.com\u002Fsourceid\u002F21101058282","https:\u002F\u002Fi.oaes.cc\u002Fuploads\u002F20240625\u002Faf5ee9028a554c6aa6f0030bbcbfe55d.png","https:\u002F\u002Fjcr.clarivate.com\u002Fjcr-jp\u002Fjournal-profile?journal=HEPATOMA%20RES&year=2023","hr",543,"ISSN 2770-3541 (Online)","https:\u002F\u002Fwww.oaepublish.com\u002Fir","ESCI, Scopus, Google Scholar, Dimensions, Lens",60,"#44b762","ISSN 2454-2857 (Online)\u003Cbr\u003EISSN 2394-4722 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Martin-Moreno","rgb(67,176,42)","https:\u002F\u002Fi.oaes.cc\u002Fupload\u002Fjournal_logo\u002Fohir.jpg",105,"ohir","#c45284","ISSN 2349-6150 (Online)\u003Cbr\u003EISSN 2347-9264 (Print)","https:\u002F\u002Fi.oaes.cc\u002Fuploads\u002F20231110\u002F8d72c3f669d5490ea3b9abc424d66cb6.jpg","https:\u002F\u002Fwww.oaepublish.com\u002Fpar","Wen-Guo Cui","520","rgb(196,82,132)","ESCI, Scopus, Dimensions, CNKI, Lens, Wanfang","https:\u002F\u002Fi.oaes.cc\u002Fupload\u002Fjournal_logo\u002Fpar.jpg","https:\u002F\u002Fi.oaes.cc\u002Fuploads\u002F20240607\u002F9bbbcad710cf457e85d63b1811faefb1.png","https:\u002F\u002Fwww.scopus.com\u002Fsourceid\u002F21101111784","https:\u002F\u002Fclarivate.com\u002Fproducts\u002Fscientific-and-academic-research\u002Fresearch-discovery-and-workflow-solutions\u002Fwebofscience-platform\u002Fweb-of-science-core-collection\u002Femerging-sources-citation-index\u002F","par",619,"#d62598","ISSN 2771-2893 (Online)","https:\u002F\u002Fi.oaes.cc\u002Fuploads\u002F20231121\u002F6ee75e8ab583446b90c97865c2c68464.jpg","https:\u002F\u002Fwww.oaepublish.com\u002Frdodj","Daniel Scherman","28","Dimensions, Lens","rgb(214,37,152)","https:\u002F\u002Fi.oaes.cc\u002Fupload\u002Fjournal_logo\u002Frdodj.jpg",106,"rdodj",77,"#1f4e79","ISSN 2769-5441 (Online)","https:\u002F\u002Fi.oaes.cc\u002Fuploads\u002F20231110\u002F98e2ed5023ee466c8890d749bfd97597.jpg","https:\u002F\u002Fwww.oaepublish.com\u002Fss","YongAn Huang","58","ESCI, Scopus, CAS, Lens, Dimensions, CNKI","rgb(31,78,121)","https:\u002F\u002Fi.oaes.cc\u002Fupload\u002Fjournal_logo\u002Fss.jpg",107,"https:\u002F\u002Fi.oaes.cc\u002Fuploads\u002F20240606\u002Fcfa200290630438c8e3a3cae1536af37.png","https:\u002F\u002Fwww.scopus.com\u002Fsourceid\u002F21101178114","ss",116,54,"#a96318","https:\u002F\u002Fi.oaes.cc\u002Fimages_2018\u002Foae\u002Fcover_sh.jpg","https:\u002F\u002Fwww.oaepublish.com\u002Fsh","Manoj Shukla","rgb(169,99,24)","https:\u002F\u002Fi.oaes.cc\u002Fupload\u002Fjournal_logo\u002Fsh.jpg",108,"sh","#4475e1","https:\u002F\u002Fi.oaes.cc\u002Fuploads\u002F20231218\u002F79ccda0bc909441587a0b70e0a8ef1cc.jpg","https:\u002F\u002Fwww.oaepublish.com\u002Fsmpo","Madjid Tavana","rgb(68,117,225)","https:\u002F\u002Fi.oaes.cc\u002Fupload\u002Fjournal_logo\u002Fsmpo.jpg",109,"smpo","#53be9b","ISSN 2573-0002 (Online)","https:\u002F\u002Fi.oaes.cc\u002Fuploads\u002F20231218\u002F54ba11ec5c57448a84169c318bb94fd4.jpg","https:\u002F\u002Fwww.oaepublish.com\u002Fsds","Nikolaos G. Nikitakis","rgb(62,163,130)","https:\u002F\u002Fi.oaes.cc\u002Fupload\u002Fjournal_logo\u002Fsds.jpg",110,"sds","#0038a0","ISSN 2768-5993 (Online)","https:\u002F\u002Fi.oaes.cc\u002Fuploads\u002F20231113\u002F695c0f0de82c43f189957ff572f0798c.jpg","https:\u002F\u002Fwww.oaepublish.com\u002Fjca","89","rgb(0,56,160)","https:\u002F\u002Fi.oaes.cc\u002Fupload\u002Fjournal_logo\u002Fjca.png","https:\u002F\u002Fi.oaes.cc\u002Fuploads\u002F20240611\u002Fd7d617bceee246428da2ed07a84efd5e.png","https:\u002F\u002Fwww.scopus.com\u002Fsourceid\u002F21101176723","https:\u002F\u002Fwww.oaepublish.com\u002Fnews\u002Fjca.838","jca",129,36,"#db6868","ISSN 2574-1209 (Online)","https:\u002F\u002Fi.oaes.cc\u002Fuploads\u002F20231110\u002Fe64051237c8a454085bb02abfdc6dd12.jpg","https:\u002F\u002Fwww.oaepublish.com\u002Fvp","290","CAS, Scopus, CNKI, Dimensions, 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#25262b; } #noIe .logo-container { width: 100%; text-align: center; padding-top: 100px; } #noIe .logo { height: 24px; } @media screen and (max-width: 820px) { #noIe .container { width: 432px; left: 50%; margin-left: -216px; margin-top: 48px; position: relative; top: 0; } #noIe .container ul { width: 290px; height: 352px; margin-left: -30px; margin-top: 40px; } #noIe .container li { margin-left: 30px; margin-top: 24px; } #noIe .logo-container { padding-top: 121px; padding-bottom: 20px; } } </style> <div class="container"> <p class="title">The current browser is not compatible</p> <p class="title2">The following browsers are recommended for the best use experience</p> <ul> <li> <a href="https://www.google.cn/chrome/" target="_blank"> <img src="https://gw.alicdn.com/imgextra/i2/O1CN01Nn0IoE1cmXZ6gFiM3_!!6000000003643-2-tps-230-230.png" /> <p>Chrome</p> </a> </li> <li> <a href="http://www.firefox.com.cn/" target="_blank"> <img 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" 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