现代制造工程 ›› 2025, Vol. 540 ›› Issue (9): 41-52.doi: 10.16731/j.cnki.1671-3133.2025.09.006

• 机器人技术 • 上一篇    下一篇

基于改进RNN元启发式的RRT冗余机械臂路径规划

胡江瑜1,2, 马珺杰2,3, 李展2,4, 黄德青2,3   

  1. 1 四川水利职业技术学院电力工程学院,成都 611231;
    2 西南交通大学ISST系统科学与技术研究所,成都 610097;
    3 西南交通大学电气工程学院,成都 610097;
    4 西南交通大学智慧城市与交通学院,成都 610097
  • 收稿日期:2024-11-02 出版日期:2025-09-18 发布日期:2025-09-23
  • 作者简介:胡江瑜,硕士,讲师,工程师,研究方向为智能机器人路径规划技术。E-mail:hjy20107545@163.com。
  • 基金资助:
    国家自然科学基金面上项目(62173279);四川水利职业技术学院校级重点科研项目(KY2025-01)

Path planning of redundant manipulator based on improved RNN meta-heuristic RRT

HU Jiangyu1,2, MA Junjie2,3, LI Zhan2,4, HUANG Deqing2,3   

  1. 1 College of Electric Power Engineering,Sichuan Water Conservancy Vocational College,Chengdu 611231,China;
    2 ISST Institute of Systems Science and Technology, Southwest Jiaotong University,Chengdu 610097,China;
    3 College of Electrical Engineering,Southwest Jiaotong University,Chengdu 610097,China;
    4 The Institute of Smart City and Intelligent Transportation, Southwest Jiaotong University,Chengdu 610097,China
  • Received:2024-11-02 Online:2025-09-18 Published:2025-09-23

摘要: 为满足铁路接触网腕臂智能检修作业中机械臂自动导航需求,提出一种综合解决路径规划和障碍物避让问题的研究方法。该方法将双重目标转化为单一的约束优化问题。在此基础上,对标准快速搜索随机树(Rapidly exploring Random Tree,RRT)算法进行改进,引入地图复杂程度评估策略和高斯混合分布采样策略,以约束随机采样点的生成方向。通过加入角度约束策略和临近障碍物的变步长机制,确保随机树始终向目标点方向生长,从而规划出渐进最优的路径。此外,设计一种基于甲虫嗅觉探测的递归神经网络(Recurrent Neural Network based on Beetle Olfactory Detection,RNNBOD)算法,配置最优关节角度,驱动冗余机械臂末端执行器沿规划的参考路径移动,从而降低其计算成本。仿真结果表明,该方法不仅有效提升了标准RRT算法的搜索效率、节点利用率和路径质量,还成功解决了冗余机械臂在运行过程中的跟踪控制难题。

关键词: 接触网检修, 路径规划, 避障, 递归神经网络算法, 跟踪控制

Abstract: In order to meet the requirement of automatic navigation of robot arm in intelligent maintenance of railway overhead contact system,a research method is proposed to comprehensively solve the problem of path planning and obstacle avoidance. This method transforms the dual objective into a single constrained optimization problem. On this basis,the standard Rapidly exploring Random Tree (RRT) algorithm is improved,the map complexity assessment strategies and Gaussian mixed distribution sampling strategies are introduced to constrain the generation direction of random sampling points. By adding the angle constraint strategy and the variable step size mechanism near the obstacle,the generation direction of the random tree is always growing towards the target point,so that the asymptotically optimal path is planned.In addition,a Recurrent Neural Network based on the Beetle Olfactory Detection (RNNBOD) algorithm is designed to configure the optimal joint angle to drive the redundant manipulator end effector to move along the planned reference path,thereby reducing its computing cost. The simulation results show that the proposed method not only improves the search efficiency,node utilization and path quality of the standard RRT algorithm,but also successfully solves the problem of tracking and controlling the redundant manipulator during operation.

Key words: maintenance of overhead contact line, path planning, obstacle avoidance, Recurrent Neural Network (RNN) algorithm, tracking control

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