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

CUI Jingkai; ZHOU Yufei; HE Shunfeng; XU Zhenbang; ZHU Mingchao

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

Micro/Nano Technology and Fine Mechanics | 更新时间：2023-12-28

- Trajectory tracking and obstacle avoidance of a redundant robotic manipulator based on the improved grey wolf optimizer
- Optics and Precision Engineering Vol. 31, Issue 24, Pages: 3595-3605(2023)
- 作者机构：
  
  1.中国科学院长春光学精密机械与物理研究所，吉林长春130033
  2.中国科学院大学，北京100049
- 作者简介：
- 基金信息：
- DOI：10.37188/OPE.20233124.3595
  CLC： TP394.1;TH691.9
- Received：09 May 2023，
  
  Revised：12 July 2023，
  
  Published：25 December 2023
- 稿件说明：
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崔靖凯,周宇飞,贺顺锋等.基于改进灰狼算法的冗余机械臂轨迹跟踪与避障[J].光学精密工程,2023,31(24):3595-3605.

CUI Jingkai,ZHOU Yufei,HE Shunfeng,et al.Trajectory tracking and obstacle avoidance of a redundant robotic manipulator based on the improved grey wolf optimizer[J].Optics and Precision Engineering,2023,31(24):3595-3605.
崔靖凯,周宇飞,贺顺锋等.基于改进灰狼算法的冗余机械臂轨迹跟踪与避障[J].光学精密工程,2023,31(24):3595-3605. DOI： 10.37188/OPE.20233124.3595.

CUI Jingkai,ZHOU Yufei,HE Shunfeng,et al.Trajectory tracking and obstacle avoidance of a redundant robotic manipulator based on the improved grey wolf optimizer[J].Optics and Precision Engineering,2023,31(24):3595-3605. DOI： 10.37188/OPE.20233124.3595.

摘要

本文将冗余机械臂的轨迹跟踪和避障规划统一为优化问题，提出了一种基于改进灰狼算法的避障跟踪优化器。首先，基于包围盒法对避障空间进行了建模，使用GJK算法计算机械臂与障碍物之间的最小距离。其次，设计了适应度函数，引入避障奖励项对优化器进行主动奖励，使机械臂在跟踪目标轨迹的同时避开障碍物。然后，使用随机分散策略对灰狼算法进行了改进，以增强算法的全局搜索能力，从而更好地求解优化问题。最后，使用九自由度冗余机械臂验证了所提出方法的有效性和优越性。实验结果表明：对于圆形目标轨迹，机械臂的末端跟踪误差为0.21 mm；跟踪过程中，机械臂与障碍物的距离不小于70 mm；相比于经典灰狼算法，改进灰狼算法使跟踪精度提高了13%。本文提出的避障跟踪优化器能以毫米级的精度同时满足冗余机械臂的轨迹跟踪和避障任务；改进的灰狼算法能有效提高经典灰狼算法的收敛精度。

Abstract

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.

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