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Multi-Robot Coordination and Cooperation via Graph-Based Computation
Multi-Robot Coordination and Cooperation via Graph-Based Computation
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20250211152721
- ISBN
- 9798384044208
- DDC
- 629.8
- 서명/저자
- Multi-Robot Coordination and Cooperation via Graph-Based Computation
- 발행사항
- [Sl] : University of Pennsylvania, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 140 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 86-03, Section: B.
- 주기사항
- Advisor: Yim, Mark;Kumar, Vijay.
- 학위논문주기
- Thesis (Ph.D.)--University of Pennsylvania, 2024.
- 초록/해제
- 요약Multi-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.
- 일반주제명
- Robotics
- 일반주제명
- Mechanics
- 일반주제명
- Mechanical engineering
- 키워드
- Machine learning
- 키워드
- Task planning
- 기타저자
- University of Pennsylvania Mechanical Engineering and Applied Mechanics
- 기본자료저록
- Dissertations Abstracts International. 86-03B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■020 ▼a9798384044208
■035 ▼a(MiAaPQ)AAI31489734
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a629.8
■1001 ▼aGosrich, Walker T.
■24510▼aMulti-Robot Coordination and Cooperation via Graph-Based Computation
■260 ▼a[Sl]▼bUniversity of Pennsylvania▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a140 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 86-03, Section: B.
■500 ▼aAdvisor: Yim, Mark;Kumar, Vijay.
■5021 ▼aThesis (Ph.D.)--University of Pennsylvania, 2024.
■520 ▼aMulti-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.
■590 ▼aSchool code: 0175.
■650 4▼aRobotics
■650 4▼aMechanics
■650 4▼aMechanical engineering
■653 ▼aMachine learning
■653 ▼aMulti-robot systems
■653 ▼aTask planning
■653 ▼aMulti-robot coordination
■690 ▼a0771
■690 ▼a0346
■690 ▼a0548
■71020▼aUniversity of Pennsylvania▼bMechanical Engineering and Applied Mechanics.
■7730 ▼tDissertations Abstracts International▼g86-03B.
■790 ▼a0175
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17163540▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
