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Perturbed Manipulability Optimization in a Distributed Network of Redundant Robots

文献类型：期刊论文

作者Jin, Long3; Zhang, Jiazheng3; Luo, Xin4,5; Liu, Mei3; Li, Shuai3; Xiao, Lin2; Yang, Zihao1
刊名IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
出版日期2021-08-01
卷号68期号:8页码:7209-7220
ISSN号0278-0046
关键词Manipulators Optimization Task analysis Kinematics Recurrent neural networks Distributed control generalized recurrent neural network (GRNN) manipulability optimization (MO) redundancy resolution kinematic control
DOI10.1109/TIE.2020.3007099
通讯作者Jin, Long(jinlongsysu@foxmail.com) ; Luo, Xin(luoxin21@cigit.ac.cn)
英文摘要For avoiding a singularity arising in the cooperative control of multiple redundant robot manipulators, an efficient way is to maximize the manipulability. In this article, by making progress along this direction, a distributed manipulability optimization scheme is proposed to maximize the manipulability of redundant robot manipulators in a distributed network with limited communication. With manipulability optimization incorporated in the proposed scheme, all the involved manipulators can be regulated to track their optimal configurations dynamically, in addition to the collaboration among them to complete the specified tasks. To do this, the distributed scheme is transformed into a dynamic quadratic programming (QP) problem by considering the time dependence of the parameters. Then, a generalized recurrent neural network (GRNN) is constructed and proposed to deal with the QP problem online with perturbations considered. Theoretical analysis is conducted, which confirms that the proposed GRNN is able to globally converge to the optimal solution to the dynamic QP problem in the presence of noises and perturbations. Finally, simulation results based on a distributed network of redundant robots are conducted and presented to verify the superior performance of the proposed distributed manipulability optimization scheme.
资助项目National Natural Science Foundation of China[61703189] ; National Key Research and Development Program of China[2017YFE0118900] ; Team Project of Natural Science Foundation of Qinghai Province, China[2020-ZJ-903] ; Key Laboratory of IoT of Qinghai[2020-ZJ-Y16] ; Natural Science Foundation of Gansu Province, China[18JR3RA264] ; Program for Guangdong Introducing Innovative and Entrepreneurial Teams[2017ZT07X183] ; Sichuan Science and Technology Program[19YYJC1656] ; Natural Science Foundation of Chongqing (China)[cstc2019jcyjjqX0013] ; Pioneer Hundred Talents Program of Chinese Academy of Sciences ; Fundamental Research Funds for the Central Universities[lzujbky-2019-89] ; Fundamental Research Funds for the Central Universities[lzujbky-2020-it09]
WOS研究方向Automation & Control Systems ; Engineering ; Instruments & Instrumentation
语种英语
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号WOS:000647484000076
源URL[http://119.78.100.138/handle/2HOD01W0/13559]  
专题中国科学院重庆绿色智能技术研究院
通讯作者Jin, Long; Luo, Xin
作者单位1.Dongguan WHEELTEC Intelligent Technol Co Ltd, Dongguan 523000, Peoples R China
2.Hunan Normal Univ, Coll Informat Sci & Engn, Changsha 410082, Peoples R China
3.Lanzhou Univ, Sch Informat Sci & Engn, Lanzhou 730000, Peoples R China
4.Chinese Acad Sci, Chongqing Inst Green & Intelligent Technol, Chongqing Key Lab Big Data & Intelligent Comp, Chongqing 400714, Peoples R China
5.Dept Big Data Anal Tech, Chongqing 401331, Peoples R China
推荐引用方式
GB/T 7714		 Jin, Long,Zhang, Jiazheng,Luo, Xin,et al. Perturbed Manipulability Optimization in a Distributed Network of Redundant Robots[J]. IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS,2021,68(8):7209-7220.
APA Jin, Long.,Zhang, Jiazheng.,Luo, Xin.,Liu, Mei.,Li, Shuai.,...&Yang, Zihao.(2021).Perturbed Manipulability Optimization in a Distributed Network of Redundant Robots.IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS,68(8),7209-7220.
MLA Jin, Long,et al."Perturbed Manipulability Optimization in a Distributed Network of Redundant Robots".IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 68.8(2021):7209-7220.

入库方式： OAI收割

来源：重庆绿色智能技术研究院

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