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Dynamic Collision-Free Motion Planning for Robotic Manipulation Using Graphs of Convex Sets- [electronic resource]
Dynamic Collision-Free Motion Planning for Robotic Manipulation Using Graphs of Convex Sets- [electronic resource]
상세정보
- 자료유형
- 학위논문파일 국외
- 최종처리일시
- 20240214100453
- ISBN
- 9798379616007
- DDC
- 629.8
- 서명/저자
- Dynamic Collision-Free Motion Planning for Robotic Manipulation Using Graphs of Convex Sets - [electronic resource]
- 발행사항
- [S.l.]: : Harvard University., 2023
- 발행사항
- Ann Arbor : : ProQuest Dissertations & Theses,, 2023
- 형태사항
- 1 online resource(111 p.)
- 주기사항
- Source: Dissertations Abstracts International, Volume: 84-12, Section: B.
- 주기사항
- Includes supplementary digital materials.
- 주기사항
- Advisor: Tedrake, Russ;Wood, Robert.
- 학위논문주기
- Thesis (Ph.D.)--Harvard University, 2023.
- 사용제한주기
- This item must not be sold to any third party vendors.
- 초록/해제
- 요약Enabling robots to generates physically feasible and collision-frees trajectories is a fundamental problem in robotics. Current solutions take one of two approaches, using sampling based motion planners to probabilistically find a path between obstacles, or using trajectory optimization to exactly handle the dynamic constraints of the robot. The sampling based motion planners can handle the messy problem of planning a configuration-space trajectory in the presence of task-space obstacles despite the nonlinear mapping between the two spaces. However, they struggle as the dimension of the robot's configuration space grows due to the curse of dimensionality and cannot handle dynamic constraints directly. Meanwhile, trajectory optimization can handle the nonlinear dynamics and scales well to high degree of freedom robots, but the collision avoidance constraints make the optimization difficult, requiring extensive solve times or good initialization.We present a motion planning pipeline that seeks to fill the gap between these two approaches. The pipeline starts by decomposing the free-space into convex collision-free regions of the configuration space using Iterative Regional Inflation by Semidefinite & Nonlinear Programming (IRIS-NP). These regions can then be planned between using Graph of Convex Sets (GCS) Trajectory Optimization to create smooth collision-free trajectories. These trajectories can be made dynamically feasible using existing time parametrization algorithms, such as Time Optimal Path Parameterization by Reachability Analysis (TOPP-RA). Finally, we demonstrate how GCS Trajectory Optimization can be expanded to plan sequential trajectories using multi-modal planning where multiple interconnected graphs are planned through. We validate our algorithms performance on a variety of robot platforms and tasks, demonstrating that they serve as a foundation for future work in collision-free motion planning.
- 일반주제명
- Robotics.
- 일반주제명
- Computer science.
- 키워드
- Motion planning
- 기타저자
- Harvard University Engineering and Applied Sciences - Engineering Sciences
- 기본자료저록
- Dissertations Abstracts International. 84-12B.
- 기본자료저록
- Dissertation Abstract International
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■00520240214100453
■006m o d
■007cr#unu||||||||
■020 ▼a9798379616007
■035 ▼a(MiAaPQ)AAI30492228
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a629.8
■1001 ▼aPetersen, Mark Alexander.▼0(orcid)0009-0005-1427-6096
■24510▼aDynamic Collision-Free Motion Planning for Robotic Manipulation Using Graphs of Convex Sets▼h[electronic resource]
■260 ▼a[S.l.]:▼bHarvard University. ▼c2023
■260 1▼aAnn Arbor :▼bProQuest Dissertations & Theses, ▼c2023
■300 ▼a1 online resource(111 p.)
■500 ▼aSource: Dissertations Abstracts International, Volume: 84-12, Section: B.
■500 ▼aIncludes supplementary digital materials.
■500 ▼aAdvisor: Tedrake, Russ;Wood, Robert.
■5021 ▼aThesis (Ph.D.)--Harvard University, 2023.
■506 ▼aThis item must not be sold to any third party vendors.
■520 ▼aEnabling robots to generates physically feasible and collision-frees trajectories is a fundamental problem in robotics. Current solutions take one of two approaches, using sampling based motion planners to probabilistically find a path between obstacles, or using trajectory optimization to exactly handle the dynamic constraints of the robot. The sampling based motion planners can handle the messy problem of planning a configuration-space trajectory in the presence of task-space obstacles despite the nonlinear mapping between the two spaces. However, they struggle as the dimension of the robot's configuration space grows due to the curse of dimensionality and cannot handle dynamic constraints directly. Meanwhile, trajectory optimization can handle the nonlinear dynamics and scales well to high degree of freedom robots, but the collision avoidance constraints make the optimization difficult, requiring extensive solve times or good initialization.We present a motion planning pipeline that seeks to fill the gap between these two approaches. The pipeline starts by decomposing the free-space into convex collision-free regions of the configuration space using Iterative Regional Inflation by Semidefinite & Nonlinear Programming (IRIS-NP). These regions can then be planned between using Graph of Convex Sets (GCS) Trajectory Optimization to create smooth collision-free trajectories. These trajectories can be made dynamically feasible using existing time parametrization algorithms, such as Time Optimal Path Parameterization by Reachability Analysis (TOPP-RA). Finally, we demonstrate how GCS Trajectory Optimization can be expanded to plan sequential trajectories using multi-modal planning where multiple interconnected graphs are planned through. We validate our algorithms performance on a variety of robot platforms and tasks, demonstrating that they serve as a foundation for future work in collision-free motion planning.
■590 ▼aSchool code: 0084.
■650 4▼aRobotics.
■650 4▼aComputer science.
■653 ▼aConvex optimization
■653 ▼aMotion planning
■653 ▼aTrajectory optimization
■690 ▼a0771
■690 ▼a0984
■71020▼aHarvard University▼bEngineering and Applied Sciences - Engineering Sciences.
■7730 ▼tDissertations Abstracts International▼g84-12B.
■773 ▼tDissertation Abstract International
■790 ▼a0084
■791 ▼aPh.D.
■792 ▼a2023
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T16932402▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
■980 ▼a202402▼f2024
