Pareto Optimal Multirobot Motion Planning
This article studies a class of multirobot coordination problems where a team of robots aim to reach their goal regions with minimum time and avoid collisions with obstacles and other robots. A novel numerical algorithm is proposed to identify the Pareto optimal solutions where no robot can unilater...
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| Veröffentlicht in: | IEEE transactions on automatic control 2021-09, Vol.66 (9), p.3984-3999 |
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| container_title | IEEE transactions on automatic control |
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| creator | Zhao, Guoxiang Zhu, Minghui |
| description | This article studies a class of multirobot coordination problems where a team of robots aim to reach their goal regions with minimum time and avoid collisions with obstacles and other robots. A novel numerical algorithm is proposed to identify the Pareto optimal solutions where no robot can unilaterally reduce its traveling time without extending others'. The consistent approximation of the algorithm in the epigraphical profile sense is guaranteed using set-valued numerical analysis. Experiments on an indoor multirobot platform and computer simulations show the anytime property of the proposed algorithm, i.e., it is able to quickly return a feasible control policy that safely steers the robots to their goal regions and it keeps improving policy optimality if more time is given. |
| doi_str_mv | 10.1109/TAC.2020.3025870 |
| format | Article |
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A novel numerical algorithm is proposed to identify the Pareto optimal solutions where no robot can unilaterally reduce its traveling time without extending others'. The consistent approximation of the algorithm in the epigraphical profile sense is guaranteed using set-valued numerical analysis. Experiments on an indoor multirobot platform and computer simulations show the anytime property of the proposed algorithm, i.e., it is able to quickly return a feasible control policy that safely steers the robots to their goal regions and it keeps improving policy optimality if more time is given.</description><identifier>ISSN: 0018-9286</identifier><identifier>EISSN: 1558-2523</identifier><identifier>DOI: 10.1109/TAC.2020.3025870</identifier><identifier>CODEN: IETAA9</identifier><language>eng</language><publisher>New York: IEEE</publisher><subject>Algorithms ; Collision avoidance ; Heuristic algorithms ; Motion planning ; Multiple robots ; Multirobot coordination ; Numerical analysis ; Optimization ; Pareto optimality ; Pareto optimization ; Pareto optimum ; Planning ; Robot motion ; Robot sensing systems ; robotic motion planning ; Robots ; Travel time</subject><ispartof>IEEE transactions on automatic control, 2021-09, Vol.66 (9), p.3984-3999</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. 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Experiments on an indoor multirobot platform and computer simulations show the anytime property of the proposed algorithm, i.e., it is able to quickly return a feasible control policy that safely steers the robots to their goal regions and it keeps improving policy optimality if more time is given.</description><subject>Algorithms</subject><subject>Collision avoidance</subject><subject>Heuristic algorithms</subject><subject>Motion planning</subject><subject>Multiple robots</subject><subject>Multirobot coordination</subject><subject>Numerical analysis</subject><subject>Optimization</subject><subject>Pareto optimality</subject><subject>Pareto optimization</subject><subject>Pareto optimum</subject><subject>Planning</subject><subject>Robot motion</subject><subject>Robot sensing systems</subject><subject>robotic motion planning</subject><subject>Robots</subject><subject>Travel time</subject><issn>0018-9286</issn><issn>1558-2523</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNo9kD1rwzAQhkVpoW7avdDF0KmD3dPJkqUxhH5BQjKks5BtuTi4livLQ_99FRI6HQfPex8PIfcUckpBPe-XqxwBIWeAXJZwQRLKucyQI7skCQCVmUIprsnNNB1iK4qCJuRpZ7wNLt2Oofs2fbqZ-9B5V7mQblzo3JDuejMM3fB1S65a00_27lwX5PP1Zb96z9bbt4_Vcp3VKFjIyoa3DI1kSiEKVYMSjNXxItUUIEyjKgs1pw2KxrayaaVBEWEjWqw4loItyONp7ujdz2ynoA9u9kNcqZGL-I3itIgUnKjau2nyttWjjw_4X01BH4XoKEQfheizkBh5OEU6a-0_rhCKgpfsDwjCWc8</recordid><startdate>20210901</startdate><enddate>20210901</enddate><creator>Zhao, Guoxiang</creator><creator>Zhu, Minghui</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>7TB</scope><scope>8FD</scope><scope>FR3</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><orcidid>https://orcid.org/0000-0001-8904-6141</orcidid><orcidid>https://orcid.org/0000-0003-3879-7820</orcidid></search><sort><creationdate>20210901</creationdate><title>Pareto Optimal Multirobot Motion Planning</title><author>Zhao, Guoxiang ; Zhu, Minghui</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c263t-7d5f32a83992269c09633c5879d406ad9be0c51d26def8df8a26839a6f2b52763</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Algorithms</topic><topic>Collision avoidance</topic><topic>Heuristic algorithms</topic><topic>Motion planning</topic><topic>Multiple robots</topic><topic>Multirobot coordination</topic><topic>Numerical analysis</topic><topic>Optimization</topic><topic>Pareto optimality</topic><topic>Pareto optimization</topic><topic>Pareto optimum</topic><topic>Planning</topic><topic>Robot motion</topic><topic>Robot sensing systems</topic><topic>robotic motion planning</topic><topic>Robots</topic><topic>Travel time</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Zhao, Guoxiang</creatorcontrib><creatorcontrib>Zhu, Minghui</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>IEEE transactions on automatic control</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Zhao, Guoxiang</au><au>Zhu, Minghui</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Pareto Optimal Multirobot Motion Planning</atitle><jtitle>IEEE transactions on automatic control</jtitle><stitle>TAC</stitle><date>2021-09-01</date><risdate>2021</risdate><volume>66</volume><issue>9</issue><spage>3984</spage><epage>3999</epage><pages>3984-3999</pages><issn>0018-9286</issn><eissn>1558-2523</eissn><coden>IETAA9</coden><abstract>This article studies a class of multirobot coordination problems where a team of robots aim to reach their goal regions with minimum time and avoid collisions with obstacles and other robots. 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| source | IEEE Electronic Library (IEL) |
| subjects | Algorithms Collision avoidance Heuristic algorithms Motion planning Multiple robots Multirobot coordination Numerical analysis Optimization Pareto optimality Pareto optimization Pareto optimum Planning Robot motion Robot sensing systems robotic motion planning Robots Travel time |
| title | Pareto Optimal Multirobot Motion Planning |
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