Trajectory tracking and obstacle avoidance of a redundant robotic manipulator based on the improved grey wolf optimizer

doi:10.37188/OPE.20233124.3595

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	Trajectory tracking and obstacle avoidance of a redundant robotic manipulator based on the improved grey wolf optimizer
	J. Cui, Y. Zhou, S. He, Z. Xu and M. Zhu
	2023
发表期刊	Guangxue Jingmi Gongcheng/Optics and Precision Engineering
ISSN	1004924X
卷号	31 期号:24 页码:3595-3605
摘要	In this study, the trajectory tracking and obstacle avoidance of redundant robotic manipulators are unified as an optimization problem, and a trajectory-tracking optimizer with obstacle avoidance capability based on an improved grey wolf optimizer (IGWO) is proposed. First, the obstacle avoidance space is modeled using the bounding box method, and the GJK algorithm is used to calculate the minimum distance between the robotic manipulator and the obstacle. Second, a fitness function is derived, and a reward function for obstacle avoidance is introduced to actively reward the optimizer such that the manipulator can track the target trajectory while avoiding obstacles. Third, the grey wolf optimizer (GWO) is improved using a random dispersion strategy to improve its global search ability and solve optimization problems more accurately. Finally, the effectiveness and superiority of the proposed method were verified using a nine-degree-of-freedom redundant robotic manipulator. The experimental results show that for a circular target trajectory, the tracking error of the robotic manipulator is 0.21 mm. During the tracking process, the distance between the robotic manipulator and obstacle is not shorter than 70 mm. Compared to the GWO, the IGWO improved the tracking accuracy by 13%. The proposed trajectory tracking optimizer can perform the trajectory tracking and obstacle avoidance tasks of redundant robotic manipulators with millimeter-level accuracy;the IGWO can effectively improve the convergence accuracy of the classical GWO. © 2023 Chinese Academy of Sciences. All rights reserved.
DOI	10.37188/OPE.20233124.3595
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引用统计
文献类型	期刊论文
条目标识符	http://ir.ciomp.ac.cn/handle/181722/67415
专题	中国科学院长春光学精密机械与物理研究所
推荐引用方式 GB/T 7714	J. Cui, Y. Zhou, S. He, Z. Xu and M. Zhu. Trajectory tracking and obstacle avoidance of a redundant robotic manipulator based on the improved grey wolf optimizer[J]. Guangxue Jingmi Gongcheng/Optics and Precision Engineering,2023,31(24):3595-3605.
APA	J. Cui, Y. Zhou, S. He, Z. Xu and M. Zhu.(2023).Trajectory tracking and obstacle avoidance of a redundant robotic manipulator based on the improved grey wolf optimizer.Guangxue Jingmi Gongcheng/Optics and Precision Engineering,31(24),3595-3605.
MLA	J. Cui, Y. Zhou, S. He, Z. Xu and M. Zhu."Trajectory tracking and obstacle avoidance of a redundant robotic manipulator based on the improved grey wolf optimizer".Guangxue Jingmi Gongcheng/Optics and Precision Engineering 31.24(2023):3595-3605.

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