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Multi-Robot Coordination and Coopera...

Gosrich, Walker T.,

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Multi-Robot Coordination and Cooperation via Graph-Based Computation /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Multi-Robot Coordination and Cooperation via Graph-Based Computation // Walker T Gosrich.
Author:	Gosrich, Walker T.,
Description:	1 electronic resource (140 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 86-03, Section: B.
Contained By:	Dissertations Abstracts International86-03B.
Subject:	Robotics. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31489734
ISBN:	9798384044208

Multi-Robot Coordination and Cooperation via Graph-Based Computation /
Gosrich, Walker T.,

Multi-Robot Coordination and Cooperation via Graph-Based Computation /Walker T Gosrich. - 1 electronic resource (140 pages)

Source: Dissertations Abstracts International, Volume: 86-03, Section: B.

Multi-robot coordination and cooperation are critical behaviors that improve team capabilities and enable new tasks in application areas like autonomous construction, agriculture, and extended operation in large unknown regions. This dissertation examines these behaviors in the context of the multi-robot resource allocation problem, where robots must be allocated to regions of service. In particular, we are interested in uncertainty-tolerant approaches that apply to large multi-robot teams. We introduce a graph-based modeling framework for the multi-robot resource allocation problem that offers unprecedented richness in representing inter-region relationships and reward models. We first address the multi-agent coverage control problem, introducing graph-based computation via Graph Neural Networks, which boasts improved performance and scalability by leveraging learned inter-agent communication strategies. We then address the multi-robot task allocation problem in complex multi-task missions where coordination and cooperation are explicitly required. We introduce a network-flow-based planning approach that produces high quality solutions to large problems in seconds. We expand this approach into an online setting that re-plans around task failures and unexpected observations. We demonstrate empirically that these modeling approaches and algorithms bring performance improvements that further the state of the art by leveraging the fundamental graph structure present in some multi-robot problems.

English

ISBN: 9798384044208Subjects--Topical Terms:

519753
Robotics.
Subjects--Index Terms:

Machine learning

Multi-Robot Coordination and Cooperation via Graph-Based Computation /
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520 $a Multi-robot coordination and cooperation are critical behaviors that improve team capabilities and enable new tasks in application areas like autonomous construction, agriculture, and extended operation in large unknown regions. This dissertation examines these behaviors in the context of the multi-robot resource allocation problem, where robots must be allocated to regions of service. In particular, we are interested in uncertainty-tolerant approaches that apply to large multi-robot teams. We introduce a graph-based modeling framework for the multi-robot resource allocation problem that offers unprecedented richness in representing inter-region relationships and reward models. We first address the multi-agent coverage control problem, introducing graph-based computation via Graph Neural Networks, which boasts improved performance and scalability by leveraging learned inter-agent communication strategies. We then address the multi-robot task allocation problem in complex multi-task missions where coordination and cooperation are explicitly required. We introduce a network-flow-based planning approach that produces high quality solutions to large problems in seconds. We expand this approach into an online setting that re-plans around task failures and unexpected observations. We demonstrate empirically that these modeling approaches and algorithms bring performance improvements that further the state of the art by leveraging the fundamental graph structure present in some multi-robot problems.
546 $a English
590 $a School code: 0175
650 4 $a Robotics. $3 519753
650 4 $a Mechanics. $3 525881
650 4 $a Mechanical engineering. $3 649730
653 $a Machine learning
653 $a Multi-robot systems
653 $a Task planning
653 $a Multi-robot coordination
690 $a 0771
690 $a 0346
690 $a 0548
710 2 $a University of Pennsylvania. $b Mechanical Engineering and Applied Mechanics. $e degree granting institution. $3 3821391
720 1 $a Yim, Mark $e degree supervisor.
720 1 $a Kumar, Vijay $e degree supervisor.
773 0 $t Dissertations Abstracts International $g 86-03B.
790 $a 0175
791 $a Ph.D.
792 $a 2024
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