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Towards a Fall-Tolerant Framework for Bipedal Robots
Towards a Fall-Tolerant Framework for Bipedal Robots
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20250211152052
- ISBN
- 9798382738284
- DDC
- 621.3
- 서명/저자
- Towards a Fall-Tolerant Framework for Bipedal Robots
- 발행사항
- [Sl] : University of Michigan, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 216 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 85-12, Section: B.
- 주기사항
- Advisor: Grizzle, Jessy.
- 학위논문주기
- Thesis (Ph.D.)--University of Michigan, 2024.
- 초록/해제
- 요약This dissertation focuses on developing a fall-tolerant framework for bipedal robots, aiming to enhance their ability to navigate challenging situations by effectively assessing, adapting, and responding to uncertainties and disturbances. Bipedal robots, with their unique capability to navigate diverse terrains and restore mobility, are ideal for assisting in critical and day-to-day tasks. However, their real-world deployment is limited due to factors like high-dimensional complex dynamics and a smaller support polygon, making it difficult to achieve stable motion, especially in the face of disturbances and uncertainties. To address these limitations, the dissertation develops robust controllers and reliable fall prediction algorithms. Feedback controllers have been used in the literature to ensure robustness against disturbances and uncertainties. However, the infeasibility of accounting for all disturbances and uncertainties during real-world operations makes falls inevitable. Falls are undesirable as they can prevent a robot from completing its task, result in damage to the surrounding area, or lead to injuries. Therefore, the dissertation emphasizes the importance of implementing robust controllers and employing methods to predict falls.This research begins by introducing a systematic method to design control objectives for highly constrained systems and concludes by presenting a 1D convolutional neural network fall prediction algorithm capable of not only predicting falls but also estimating the time to react. The effectiveness of the control objectives is demonstrated through robust, comfortable closed-loop sit-to-stand motions for a fully actuated lower-limb exoskeleton, Atalante. The performance of the proposed fall prediction algorithms is evaluated in simulation using a planar-four link robot based on Atalante and in hardware and simulation for the bipedal robot Digit.
- 일반주제명
- Electrical engineering
- 일반주제명
- Mechanical engineering
- 일반주제명
- Robotics
- 일반주제명
- Computer engineering
- 키워드
- Feedback control
- 키워드
- Fall prediction
- 키워드
- Bipedal robots
- 키워드
- Humanoids
- 키워드
- Exoskeletons
- 키워드
- Machine learning
- 기타저자
- University of Michigan Mechanical Engineering
- 기본자료저록
- Dissertations Abstracts International. 85-12B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■006m o d
■007cr#unu||||||||
■020 ▼a9798382738284
■035 ▼a(MiAaPQ)AAI31348866
■035 ▼a(MiAaPQ)umichrackham005484
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a621.3
■1001 ▼aMungai, Margaret Eva Wangari.
■24510▼aTowards a Fall-Tolerant Framework for Bipedal Robots
■260 ▼a[Sl]▼bUniversity of Michigan▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a216 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 85-12, Section: B.
■500 ▼aAdvisor: Grizzle, Jessy.
■5021 ▼aThesis (Ph.D.)--University of Michigan, 2024.
■520 ▼aThis dissertation focuses on developing a fall-tolerant framework for bipedal robots, aiming to enhance their ability to navigate challenging situations by effectively assessing, adapting, and responding to uncertainties and disturbances. Bipedal robots, with their unique capability to navigate diverse terrains and restore mobility, are ideal for assisting in critical and day-to-day tasks. However, their real-world deployment is limited due to factors like high-dimensional complex dynamics and a smaller support polygon, making it difficult to achieve stable motion, especially in the face of disturbances and uncertainties. To address these limitations, the dissertation develops robust controllers and reliable fall prediction algorithms. Feedback controllers have been used in the literature to ensure robustness against disturbances and uncertainties. However, the infeasibility of accounting for all disturbances and uncertainties during real-world operations makes falls inevitable. Falls are undesirable as they can prevent a robot from completing its task, result in damage to the surrounding area, or lead to injuries. Therefore, the dissertation emphasizes the importance of implementing robust controllers and employing methods to predict falls.This research begins by introducing a systematic method to design control objectives for highly constrained systems and concludes by presenting a 1D convolutional neural network fall prediction algorithm capable of not only predicting falls but also estimating the time to react. The effectiveness of the control objectives is demonstrated through robust, comfortable closed-loop sit-to-stand motions for a fully actuated lower-limb exoskeleton, Atalante. The performance of the proposed fall prediction algorithms is evaluated in simulation using a planar-four link robot based on Atalante and in hardware and simulation for the bipedal robot Digit.
■590 ▼aSchool code: 0127.
■650 4▼aElectrical engineering
■650 4▼aMechanical engineering
■650 4▼aRobotics
■650 4▼aComputer engineering
■653 ▼aFeedback control
■653 ▼aFall prediction
■653 ▼aBipedal robots
■653 ▼aHumanoids
■653 ▼aExoskeletons
■653 ▼aMachine learning
■653 ▼aTrajectory optimization
■690 ▼a0771
■690 ▼a0548
■690 ▼a0544
■690 ▼a0464
■690 ▼a0800
■71020▼aUniversity of Michigan▼bMechanical Engineering.
■7730 ▼tDissertations Abstracts International▼g85-12B.
■790 ▼a0127
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17162763▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
