Obstacle Avoidance and Tracking Control of Redundant Robotic Manipulator: An RNN-Based Metaheuristic Approach

doi:10.1109/TII.2019.2941916

	Obstacle Avoidance and Tracking Control of Redundant Robotic Manipulator: An RNN-Based Metaheuristic Approach
	Khan, Ameer Hamza 1; Li, Shuai 2; Luo, Xin 3,4
刊名	IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
	2020-07-01
卷号	16 期号:7 页码:4670-4680
关键词	Manipulators Collision avoidance Trajectory Optimization Task analysis Kinematics Metaheuristic optimization obstacle avoidance recurrent neural network (RNN) tracking control
ISSN号	1551-3203
DOI	10.1109/TII.2019.2941916
通讯作者	Li, Shuai(shuaili@ieee.org) ; Luo, Xin(luoxin21@cigit.ac.cn)
英文摘要	In this article, we present a metaheuristic-based control framework, called beetle antennae olfactory recurrent neural network, for simultaneous tracking control and obstacle avoidance of a redundant manipulator. The ability to avoid obstacles while tracking a predefined reference path is critical for any industrial manipulator. The formulated control framework unifies the tracking control and obstacle avoidance into a single constrained optimization problem by introducing a penalty term into the objective function, which actively rewards the optimizer for avoiding the obstacles. One of the significant features of the proposed framework is the way that the penalty term is formulated following a straightforward principle: maximize the minimum distance between a manipulator and an obstacle. The distance calculations are based on Gilbert-Johnson-Keerthi algorithm, which calculates the distance between a manipulator and an obstacle by directly using their three-dimensional geometries, which also implies that our algorithm works for a manipulator and an arbitrarily shaped obstacle. Theoretical treatment proves the stability and convergence, and simulations results using an LBR IIWA seven-DOF manipulator are presented to analyze the performance of the proposed framework.
WOS研究方向	Automation & Control Systems ; Computer Science ; Engineering
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:000522523000034
内容类型	期刊论文
源URL	[http://119.78.100.138/handle/2HOD01W0/10758]
专题	中国科学院重庆绿色智能技术研究院
通讯作者	Li, Shuai; Luo, Xin
作者单位	1.Hong Kong Polytech Univ, Dept Comp, Hong Kong, Peoples R China 2.Swansea Univ, Swansea SA2 8PP, W Glam, Wales 3.Chinese Acad Sci, Chongqing Engn Res Ctr Big Data Applicat Smart Ci, Dept Elect & Elect Engn, Chongqing 400714, Peoples R China 4.Chinese Acad Sci, Chongqing Key Lab Big Data & Intelligent Comp, Chongqing Inst Green & Intelligent Technol, Chongqing 400714, Peoples R China
推荐引用方式 GB/T 7714	Khan, Ameer Hamza,Li, Shuai,Luo, Xin. Obstacle Avoidance and Tracking Control of Redundant Robotic Manipulator: An RNN-Based Metaheuristic Approach[J]. IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS,2020,16(7):4670-4680.
APA	Khan, Ameer Hamza,Li, Shuai,&Luo, Xin.(2020).Obstacle Avoidance and Tracking Control of Redundant Robotic Manipulator: An RNN-Based Metaheuristic Approach.IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS,16(7),4670-4680.
MLA	Khan, Ameer Hamza,et al."Obstacle Avoidance and Tracking Control of Redundant Robotic Manipulator: An RNN-Based Metaheuristic Approach".IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 16.7(2020):4670-4680.