Interpretable Fuzzy Logic Control for Multirobot Coordination in a Cluttered Environment

Chang, YC; Shi, Y; Dostovalova, A; Cao, Z; Kim, J; Gibbons, D; Lin, CT

Interpretable Fuzzy Logic Control for Multirobot Coordination in a Cluttered Environment

Chang, YC Shi, Y

Dostovalova, A Cao, Z

Kim, J Gibbons, D Lin, CT

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Publisher:: Institute of Electrical and Electronics Engineers
Publication Type:: Journal Article
Citation:: IEEE Transactions on Fuzzy Systems, 2021, 29, (12), pp. 3676-3685
Issue Date:: 2021-12-01

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Field	Value	Language
dc.contributor.author	Chang, YC
dc.contributor.author	Shi, Y https://orcid.org/0000-0003-2302-4980
dc.contributor.author	Dostovalova, A
dc.contributor.author	Cao, Z https://orcid.org/0000-0003-3656-0328
dc.contributor.author	Kim, J
dc.contributor.author	Gibbons, D
dc.contributor.author	Lin, CT
dc.date.accessioned	2022-04-11T02:15:37Z
dc.date.available	2022-04-11T02:15:37Z
dc.date.issued	2021-12-01
dc.identifier.citation	IEEE Transactions on Fuzzy Systems, 2021, 29, (12), pp. 3676-3685
dc.identifier.issn	1063-6706
dc.identifier.issn	1941-0034
dc.identifier.uri	http://hdl.handle.net/10453/156044
dc.description.abstract	Mobile robot navigation is an essential problem in robotics. We propose a method for constructing and training fuzzy logic controllers (FLCs) to coordinate a robotic team performing collision-free navigation and arriving simultaneously at a target location in an unknown environment. Our FLCs are organized in a multilayered architecture to reduce the number of tunable parameters and improve the scalability of the solution. In addition, in contrast to simple traditional switching mechanisms between target seeking and obstacle avoidance, we develop a novel rule-embedded FLC to improve the navigation performance. Moreover, we design a grouping and merging mechanism to obtain transparent fuzzy sets and integrate this mechanism into the training process for all FLCs, thus increasing the interpretability of the fuzzy models. We train the proposed FLCs using a novel multiobjective hybrid approach combining a genetic algorithm and particle swarm optimization. Simulation results demonstrate the effectiveness of our algorithms in reliably solving the proposed navigation problem.
dc.language	English
dc.publisher	Institute of Electrical and Electronics Engineers
dc.relation	http://purl.org/au-research/grants/arc/DP210101093
dc.relation.ispartof	IEEE Transactions on Fuzzy Systems
dc.relation.isbasedon	10.1109/TFUZZ.2021.3111446
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.subject	0102 Applied Mathematics, 0801 Artificial Intelligence and Image Processing, 0906 Electrical and Electronic Engineering
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	Interpretable Fuzzy Logic Control for Multirobot Coordination in a Cluttered Environment
dc.type	Journal Article
utslib.citation.volume	29
utslib.for	0102 Applied Mathematics
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0906 Electrical and Electronic Engineering
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAII - Australian Artificial Intelligence Institute
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	open_access	*
pubs.consider-herdc	false
utslib.copyright.embargo	2023-12-31T00:00:00+1000Z
dc.date.updated	2022-04-11T02:15:34Z
pubs.issue	12
pubs.publication-status	Published
pubs.volume	29
utslib.citation.issue	12

Abstract:

Mobile robot navigation is an essential problem in robotics. We propose a method for constructing and training fuzzy logic controllers (FLCs) to coordinate a robotic team performing collision-free navigation and arriving simultaneously at a target location in an unknown environment. Our FLCs are organized in a multilayered architecture to reduce the number of tunable parameters and improve the scalability of the solution. In addition, in contrast to simple traditional switching mechanisms between target seeking and obstacle avoidance, we develop a novel rule-embedded FLC to improve the navigation performance. Moreover, we design a grouping and merging mechanism to obtain transparent fuzzy sets and integrate this mechanism into the training process for all FLCs, thus increasing the interpretability of the fuzzy models. We train the proposed FLCs using a novel multiobjective hybrid approach combining a genetic algorithm and particle swarm optimization. Simulation results demonstrate the effectiveness of our algorithms in reliably solving the proposed navigation problem.

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http://hdl.handle.net/10453/156044