Reducing Redundancy of Musculoskeletal Robot With Convex Hull Vertexes Selection

doi:10.1109/TCDS.2019.2953642

CORC > 自动化研究所 > 中国科学院自动化研究所 > 复杂系统管理与控制国家重点实验室 > 机器人应用与理论组

	Reducing Redundancy of Musculoskeletal Robot With Convex Hull Vertexes Selection
	Zhong, Shanlin 4,5; Chen, Jiahao 1,5; Niu, Xingyu 2; Fu, Hang 2; Qiao, Hong 3,4,5
刊名	IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS
	2020-09-01
卷号	12 期号:3 页码:601-617
关键词	Muscles Robots Mathematical model Hardware Redundancy Numerical models Convex hull vertexes human-like robot motion experiment musculoskeletal system reduce redundancy
ISSN号	2379-8920
DOI	10.1109/TCDS.2019.2953642
通讯作者	Zhong, Shanlin(zhongshanlin2016@ia.ac.cn)
英文摘要	It has been a long-term dream of roboticist to create a robot that has similar behavior and appearance to human. With highly redundant joints and actuators, the musculoskeletal system of human can fulfill movement and operation with high precision, flexibility, and reliability. However, the redundancy of the musculoskeletal system also brings great difficulties to the control and fabrication of a human-like robot. Therefore, we propose an algorithm based on the convex hull theory to effectively reduce the number of redundant muscles. Specifically, taking the strength limitation and state-dependent characteristics of muscles into consideration, by analyzing the torque contribution of muscles for the discrete motion of joints, each muscle can be represented by a feature vector, and the convex hulls are formed by these vectors in high-dimensional space. By applying the proposed algorithm, the muscles corresponding to the vertexes of the convex hull are reserved while the redundant muscles within the convex hull are deleted, and a simplified model can be obtained. Sets of motion experiments demonstrate that the simplified model can achieve high-precision movement as a complete model. Based on the simplified model, a hardware platform of a robotic arm is constructed, which consists of six degrees of freedom and 11 pneumatic muscles.
资助项目	National Key Research and Development Program of China[2017YFB1300200] ; National Key Research and Development Program of China[2017YFB1300203] ; National Natural Science Foundation of China[91648205] ; National Natural Science Foundation of China[61627808] ; Strategic Priority Research Program of Chinese Academy of Science[XDB32000000]
WOS关键词	COMPUTED MUSCLE CONTROL ; DYNAMIC SIMULATIONS ; UPPER EXTREMITY ; MODEL ; OPTIMIZATION ; SYNERGIES
WOS研究方向	Computer Science ; Robotics ; Neurosciences & Neurology
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:000568663000020
资助机构	National Key Research and Development Program of China ; National Natural Science Foundation of China ; Strategic Priority Research Program of Chinese Academy of Science
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/41967]
专题	自动化研究所_复杂系统管理与控制国家重点实验室_机器人应用与理论组
通讯作者	Zhong, Shanlin
作者单位	1.Chinese Acad Sci, Inst Automat, Res Ctr Brain Inspired Intelligence, Beijing 100190, Peoples R China 2.Univ Sci & Technol Beijing, Key Labs Res & Applicat Robot Intelligence Hand E, Beijing 100190, Peoples R China 3.Chinese Acad Sci, Inst Neurosci, Ctr Excellence Brain Sci & Intelligence Technol, Shanghai 200031, Peoples R China 4.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 5.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
推荐引用方式 GB/T 7714	Zhong, Shanlin,Chen, Jiahao,Niu, Xingyu,et al. Reducing Redundancy of Musculoskeletal Robot With Convex Hull Vertexes Selection[J]. IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS,2020,12(3):601-617.
APA	Zhong, Shanlin,Chen, Jiahao,Niu, Xingyu,Fu, Hang,&Qiao, Hong.(2020).Reducing Redundancy of Musculoskeletal Robot With Convex Hull Vertexes Selection.IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS,12(3),601-617.
MLA	Zhong, Shanlin,et al."Reducing Redundancy of Musculoskeletal Robot With Convex Hull Vertexes Selection".IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS 12.3(2020):601-617.