Control and Decision
1001-0920
10.13195/j.kzyjc.2021.0107
article
基于改进天牛须算法的电力攀爬机器人运动学逆解算法
Inverse kinematics solution algorithm of electric climbing robot based on improved beetle antennae search algorithm.
为了提高电力系统的自动化水平,减轻电力工人在检修高压输电系统时的劳动强度,同时保障电力工人人身安全, 设计并提出一种可以攀爬电力铁塔的六自由度关节式机器人. 并针对该构型进行运动学分析与求解.为解决传统的解析法用于机械臂逆运动学求解过程中存在操作繁琐和奇异点无法逆运算等问题 提出一种基于改进天牛须算法的电力攀爬机器人运动学逆解算法.对电力攀爬机器人进行DH建模，得到正运动学方程。使用正运动学方程与目标位姿建立代价函数, 采用改进天牛须算法对代价函数优化, 并使用MATLAB实现此算法进行仿真验证.对比传统的天牛须算法、改进遗传算法、改进粒子群算法 ,基于改进天牛群算法的电力攀爬机器人运动学逆解算法具有较好的收敛性,求解精度高.
In order to improve the automation level of power system, reduce the labor intensity of power workers in the maintenance of high voltage transmission system, and ensure their personal safety, 6-DOF articulated robot for climbing power tower is designed and proposed.And kinematics analysis and solution are carried out for the configuration. In order to solve that the traditional analytical method used in the inverse kinematics of manipulator problems such as complicated operation and singular points can not be inverse operation, this paper presents an inverse kinematics algorithm for electric climbing robot based on the improved beetle antennae search algorithm.The DH model of electric climbing robot is established,and the forward kinematics equation is obtained.Establish a cost function according to the positive kinematics equation and the target pose, the cost function is optimized by the improved beetle antennae search algorithm, and use matlab to realize this algorithm for simulation verification. Contrast with the traditional beetle antennae search algorithm,improved genetic algorithm and improved particle swarm optimization, the inverse kinematics solution algorithm of electric climbing robot based on the improved beetle antennae search algorithm has good convergence and high solution accuracy, which can be used in the robot real-time control system.
电力攀爬机器人 改进天牛须算法 运动学逆解 轨迹规划
electric climbing robot improved beetle algorithm inverse kinematics solution trajectory planning
都海波,葛展展,张金锋,谢枫
douhaibo,gezhanzhan,zhangjinfeng,xiefeng
1.合肥工业大学电气与自动化工程学院;2.国网安徽省电力有限公司;3.中国能源建设集团安徽省电力设计院有限公司
School of Electrical Engineering and Automation ,HFUT
kzyjc/article/abstract/2021-0107