欢迎访问《太阳能学报》官方网站,今天是 分享到:
ISSN 0254-0096 CN 11-2082/K

太阳能学报 ›› 2022, Vol. 43 ›› Issue (10): 43-51.DOI: 10.19912/j.0254-0096.tynxb.2021-0427

• • 上一篇    下一篇

基于BP神经网络的光伏阵列清洁机械臂逆运动学分析与时间最短运动规划

芮宏斌1, 曹伟1, 孙宁宁2   

  1. 1. 西安理工大学机械与精密仪器工程学院,西安 710048;
    2. 国家电力投资集团公司光伏创新中心,西安 710061
  • 收稿日期:2021-04-20 出版日期:2022-10-28 发布日期:2023-04-28
  • 通讯作者: 曹伟(1997——),男,硕士研究生,主要从事特种移动机器人设计及运动控制方面的研究。939643182@qq.com
  • 基金资助:
    国家科学自然基金面上项目(51775432); 陕西省技术创新引导专项(2018ZKC-160)

INVERSE KINEMATICS ANALYSIS AND TIME OPTIMAL MOTION PLANNING OF PHOTOVOLTAIC ARRAY CLEANING MANIPULATOR BASED ON BP NEURAL NETWORK

Rui Hongbin1, Cao Wei1, Sun Ningning2   

  1. 1. School of Mechanical and Precision Instrument Engineering, Xi'an University of Technology, Xi'an 710048, China;
    2. Photovoltaic Innovation Center of State Power Investment Corporation, Xi'an 710061, China;
  • Received:2021-04-20 Online:2022-10-28 Published:2023-04-28

摘要: 针对光伏阵列积灰效应带来的光电转换效率下降问题,依据中国西北地区安装地形特点和清洁作业环境设计一款车载式光伏阵列清洁机器人。清洁机械臂逆运动学分析是末端清洁器轨迹规划的难点,针对解析法求解逆运动学方法复杂且得不到最优解,提出基于BP神经网络求解机械臂逆运动学的方法,建立BP神经网络模型并利用Matlab进行仿真验证。为了提高光伏阵列清洁效率,针对清洁机械臂点到点运动的时间最优问题进行时间最短运动规划。利用五次多项式插值法对机械臂各个关节变量在关节空间坐标系中进行路径拟合,并利用遗传算法对清洁机械臂运动时间进行优化。最终根据优化结果对机械臂进行轨迹规划仿真。结果表明,建立的机械臂运动学模型及使用的BP神经网络求解清洁机械臂逆运动学方法符合清洁机械臂时间最优运动规划,提高了光伏阵列清洁效率,在一定程度上为光伏阵列清洁机器人机械臂提供了时间最优控制规划。

关键词: 光伏阵列, 移动机器人, 逆运动学, 遗传算法, 运动规划, BP神经网络

Abstract: Aiming at the problem of photoelectric conversion efficiency decline caused by the ash deposition effect of photovoltaic panels, a vehicle mounted photovoltaic panel cleaning robot is designed according to the installation terrain characteristics and clean working environment in Northwest China. Inverse kinematics analysis of cleaning manipulator is the difficulty problem of trajectory planning of end cleaner. In view of the complexity of analytical method to solve inverse kinematics and the lack of optimal solution, a method based on BP neural network to solve inverse kinematics of cleaning manipulator is proposed. BP neural network model is established and simulated by MATLAB. In order to improve the cleaning efficiency of photovoltaic panels, the shortest time motion planning is carried out for the point-to-point motion time optimization problem of cleaning manipulator. The quintic polynomial interpolation method is used to fit the path of each joint variable of the manipulator in the joint space coordinate system, and the genetic algorithm is used to optimize the motion time of the cleaning manipulator. Finally, according to the optimization results, the trajectory planning of the manipulator is simulated. The results show that the established kinematics model of the manipulator and the BP neural network used to solve the inverse kinematics method of the cleaning manipulator accord with the time optimal motion planning of the cleaning manipulator, which improves the cleaning efficiency of photovoltaic panels, and provides the time optimal control planning for the cleaning robot arm of photovoltaic panels to a certain extent.

中图分类号: