Obstacle Avoidance Planning of Manipulator Joints Based on an Improved Artificial Potential Field Method

Yue Xu; Zhou Haibo; Shao Yanpeng; Lu Shuai; Xu Wangbei; Deng Yuxin

doi:10.16578/j.issn.1004.2539.2023.10.004

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Obstacle Avoidance Planning of Manipulator Joints Based on an Improved Artificial Potential Field Method

Theory·Research | 更新时间：2023-10-09

- Obstacle Avoidance Planning of Manipulator Joints Based on an Improved Artificial Potential Field Method
- Journal of Mechanical Transmission Vol. 47, Issue 10, Pages: 23-30(2023)
- 作者机构：
  
  1.天津理工大学机电工程国家级实验教学示范中心，天津 300384
  2.天津理工大学天津市先进机电系统设计与智能控制重点实验室，天津 300384
- 作者简介：
- 基金信息：
- DOI：10.16578/j.issn.1004.2539.2023.10.004
  CLC：
- Published：15 October 2023，
  
  Received：02 August 2022，
  
  Revised：31 August 2022，
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岳旭,周海波,邵艳朋等.改进人工势场法的机械手关节空间避障规划[J].机械传动,2023,47(10):23-30.

Yue Xu,Zhou Haibo,Shao Yanpeng,et al.Obstacle Avoidance Planning of Manipulator Joints Based on an Improved Artificial Potential Field Method[J].Journal of Mechanical Transmission,2023,47(10):23-30.
岳旭,周海波,邵艳朋等.改进人工势场法的机械手关节空间避障规划[J].机械传动,2023,47(10):23-30. DOI： 10.16578/j.issn.1004.2539.2023.10.004.

Yue Xu,Zhou Haibo,Shao Yanpeng,et al.Obstacle Avoidance Planning of Manipulator Joints Based on an Improved Artificial Potential Field Method[J].Journal of Mechanical Transmission,2023,47(10):23-30. DOI： 10.16578/j.issn.1004.2539.2023.10.004.

摘要

针对人工势场（Artificial Potential Field，APF）法对机械手进行路径规划时存在的问题，提出了关节空间APF自适应变步长和目标偏置的快速扩展随机树（Rapidly-exploring Random Tree，RRT）相结合的方法。在关节空间中进行APF法路径规划，减少逆向运动学次数和关节角突变；通过改进斥力和引力势场函数，解决APF法中碰撞和目标不可达问题；采用柯西概率分布，根据末端点与障碍物的距离来改变关节角步长；通过调节RRT算法的目标偏置值，产生合适临时目标点，从而解决APF法局部极小值问题。在APF法存在局部极小值情况下进行机械臂避障仿真，结果表明，自适应变步长路径规划能够生成平滑轨迹并能提高搜索效率，目标偏置RRT选取临时目标点后整体路径长度变短。捡拾机械手在该改进算法下能够有效实现避障拾取任务需求。

Abstract

Aiming at the problems in the path planning of the manipulator by artificial potential field (APF) method

a method combining the APF adaptive variable step size in the joint space and goal-biased rapidly-exploring random tree (RRT) is proposed. The APF obstacle avoidance planning is performed in the joint space to reduce the number of inverse kinematics and the sudden change of joint angles. The collision and target unreachability problems in the path planning are solved by improving the repulsive and gravitational potential field functions. The Cauchy probability distribution is used to change the joint angle step size through the distance between the end point and the obstacle. By adjusting the bias of the RRT algorithm

suitable temporary target points are generated to solve the local minima problem of the APF. The obstacle avoidance simulation of the manipulator is carried out in the presence of local minima of the APF. Adaptive variable-step path planning can generate smooth trajectories and improve the search efficiency. The goal-biased RRT selects the temporary target point and the overall path length becomes smaller. The picking manipulator can effectively meet the requirements of obstacle avoidance picking tasks under the improved algorithm.

关键词

捡拾机械臂人工势场法自适应变步长快速扩展随机树避障规划

Keywords

Pickup robotArtificial potential field methodAdaptive variable step sizeRapidly-exploring random treeObstacle avoidance planning

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