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RNN for Repetitive Motion Generation of Redundant Robot Manipulators: An Orthogonal Projection-Based Scheme

文献类型：期刊论文

作者Xie, Zhengtai3,4; Jin, Long3,4; Luo, Xin1,5; Sun, Zhongbo2; Liu, Mei3,4
刊名IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
出版日期2022-02-01
卷号33期号:2页码:615-628
关键词Manipulators Kinematics Recurrent neural networks Task analysis Redundancy Error elimination gradient descent method orthogonal projection method recurrent neural network (RNN) redundant manipulators repetitive motion generation (RMG)
ISSN号2162-237X
DOI10.1109/TNNLS.2020.3028304
通讯作者Jin, Long(longjin@ieee.org) ; Luo, Xin(luoxin21@cigit.ac.cn)
英文摘要For the existing repetitive motion generation (RMG) schemes for kinematic control of redundant manipulators, the position error always exists and fluctuates. This article gives an answer to this phenomenon and presents the theoretical analyses to reveal that the existing RMG schemes exist a theoretical position error related to the joint angle error. To remedy this weakness of existing solutions, an orthogonal projection RMG (OPRMG) scheme is proposed in this article by introducing an orthogonal projection method with the position error eliminated theoretically, which decouples the joint space error and Cartesian space error with joint constraints considered. The corresponding new recurrent neural networks (NRNNs) are structured by exploiting the gradient descent method with the assistance of velocity compensation with theoretical analyses provided to embody the stability and feasibility. In addition, simulation results on a fixed-based redundant manipulator, a mobile manipulator, and a multirobot system synthesized by the existing RMG schemes and the proposed one are presented to verify the superiority and precise performance of the OPRMG scheme for kinematic control of redundant manipulators. Moreover, via adjusting the coefficient, simulations on the position error and joint drift of the redundant manipulator are conducted for comparison to prove the high performance of the OPRMG scheme. To bring out the crucial point, different controllers for the redundancy resolution of redundant manipulators are compared to highlight the superiority and advantage of the proposed NRNN. This work greatly improves the existing RMG solutions in theoretically eliminating the position error and joint drift, which is of significant contributions to increasing the accuracy and efficiency of high-precision instruments in manufacturing production.
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[2020ZJ-903] ; Key Laboratory of IoT of Qinghai[2020-ZJY16] ; Natural Science Foundation of Chongqing (China)[cstc2019jcyjjqX0013] ; Natural Science Foundation of Chongqing (China)[cstc2020jcyj-zdxm0047] ; Pioneer Hundred Talents Program of Chinese Academy of Sciences ; Open Fund of Robot Technology Used for Special Environment Key Laboratory of Sichuan Province[17kftk03] ; Natural Science Foundation of Gansu Province, China[18JR3RA264] ; Fundamental Research Funds for the Central Universities[lzujbky-2019-89] ; Fundamental Research Funds for the Central Universities[lzujbky-2020-it09] ; Fundamental Research Funds for the Central Universities[lzuxxxy2019-tm16]
WOS研究方向Computer Science ; Engineering
语种英语
WOS记录号WOS:000752016400016
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
源URL[http://119.78.100.138/handle/2HOD01W0/15260]  
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.Chinese Acad Sci, Chongqing Inst Green & Intelligent Technol, Chongqing Key Lab Big Data & Intelligent Comp, Chongqing 400714, Peoples R China
2.Changchun Univ Technol, Dept Control Engn, Changchun 130012, Peoples R China
3.Lanzhou Univ, Sch Informat Sci & Engn, Lanzhou 730000, Peoples R China
4.Acad Plateau Sci & Sustainabil, Xining 810016, Peoples R China
5.Chinese Acad Sci, Chongqing Engn Res Ctr Big Data Applicat Smart Ci, Chongqing 400714, Peoples R China
推荐引用方式
GB/T 7714		 Xie, Zhengtai,Jin, Long,Luo, Xin,et al. RNN for Repetitive Motion Generation of Redundant Robot Manipulators: An Orthogonal Projection-Based Scheme[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2022,33(2):615-628.
APA Xie, Zhengtai,Jin, Long,Luo, Xin,Sun, Zhongbo,&Liu, Mei.(2022).RNN for Repetitive Motion Generation of Redundant Robot Manipulators: An Orthogonal Projection-Based Scheme.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,33(2),615-628.
MLA Xie, Zhengtai,et al."RNN for Repetitive Motion Generation of Redundant Robot Manipulators: An Orthogonal Projection-Based Scheme".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 33.2(2022):615-628.

入库方式： OAI收割

来源：重庆绿色智能技术研究院

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