Multi-objective region reaching control for a swarm of robots

Miao, Z; Yu, J; Ji, J; Zhou, J

Multi-objective region reaching control for a swarm of robots

Miao, Z Yu, J Ji, J

Zhou, J

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Publisher:: PERGAMON-ELSEVIER SCIENCE LTD
Publication Type:: Journal Article
Citation:: Automatica, 2019, 103, (IEEE Transaction Robotics and Automation 14 6 1998), pp. 81-87
Issue Date:: 2019-05-01

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Field	Value	Language
dc.contributor.author	Miao, Z
dc.contributor.author	Yu, J
dc.contributor.author	Ji, J https://orcid.org/0000-0003-3280-5463
dc.contributor.author	Zhou, J
dc.date.accessioned	2020-04-30T06:14:57Z
dc.date.available	2020-04-30T06:14:57Z
dc.date.issued	2019-05-01
dc.identifier.citation	Automatica, 2019, 103, (IEEE Transaction Robotics and Automation 14 6 1998), pp. 81-87
dc.identifier.issn	0005-1098
dc.identifier.issn	1873-2836
dc.identifier.uri	http://hdl.handle.net/10453/140363
dc.description.abstract	© 2019 Elsevier Ltd This paper is concerned with the multi-objective region reaching control for a swarm of robots which are formulated by Lagrangian dynamics. Two distributed multi-objective region reaching control protocols are proposed for the networked robotic systems under directed acyclic topology, and a unifying methodology is presented to perform the convergence analysis for the robotic systems with static and moving target regions. The control strategy is developed by using the potential energy function approach, and the specified shapes of the various desired regions are constructed by selecting appropriate objective functions. In this control strategy, a network of a large number of robots evolves into multiple groups, and the robots in each group only require communicating with their neighbors. Thus, the proposed control strategy is effective for multi-objective region reaching control for a swarm of robots in practical applications. Finally, simulation examples are given to show the validity of the theoretical results.
dc.language	English
dc.publisher	PERGAMON-ELSEVIER SCIENCE LTD
dc.relation.ispartof	Automatica
dc.relation.isbasedon	10.1016/j.automatica.2019.01.017
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	01 Mathematical Sciences, 08 Information and Computing Sciences, 09 Engineering
dc.subject.classification	Industrial Engineering & Automation
dc.title	Multi-objective region reaching control for a swarm of robots
dc.type	Journal Article
utslib.citation.volume	103
utslib.for	01 Mathematical Sciences
utslib.for	08 Information and Computing Sciences
utslib.for	09 Engineering
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Mechanical and Mechatronic Engineering
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2020-04-30T06:14:42Z
pubs.issue	IEEE Transaction Robotics and Automation 14 6 1998
pubs.publication-status	Published
pubs.volume	103
utslib.start-page	81
utslib.citation.issue	IEEE Transaction Robotics and Automation 14 6 1998

Abstract:

© 2019 Elsevier Ltd This paper is concerned with the multi-objective region reaching control for a swarm of robots which are formulated by Lagrangian dynamics. Two distributed multi-objective region reaching control protocols are proposed for the networked robotic systems under directed acyclic topology, and a unifying methodology is presented to perform the convergence analysis for the robotic systems with static and moving target regions. The control strategy is developed by using the potential energy function approach, and the specified shapes of the various desired regions are constructed by selecting appropriate objective functions. In this control strategy, a network of a large number of robots evolves into multiple groups, and the robots in each group only require communicating with their neighbors. Thus, the proposed control strategy is effective for multi-objective region reaching control for a swarm of robots in practical applications. Finally, simulation examples are given to show the validity of the theoretical results.

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http://hdl.handle.net/10453/140363