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	Obstacle Avoidance and Tracking Control of Redundant Robotic Manipulator: An RNN-Based Metaheuristic Approach
	Khan, Ameer Hamza1; Li, Shuai2; Luo, Xin3,4
	2020-07-01
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.
关键词	Manipulators Collision avoidance Trajectory Optimization Task analysis Kinematics Metaheuristic optimization obstacle avoidance recurrent neural network (RNN) tracking control
DOI	10.1109/TII.2019.2941916
发表期刊	IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
ISSN	1551-3203
卷号	16期号:7页码:4670-4680
通讯作者	Li, Shuai(shuaili@ieee.org) ; Luo, Xin(luoxin21@cigit.ac.cn)
收录类别	SCI
WOS记录号	WOS:000522523000034
语种	英语