基于遗传粒子群的PUMA机器人逆运动求解  

Solving PUMA Robot Inverse Kinematics Based on Genetic Particle Swarm Optimization

作　　者：吴振宇[1] 姚明江[1] 冯林[1] 金博[1] 

机构地区：[1]大连理工大学创新实验学院,辽宁大连116023

出　　处：《计算机仿真》2011年第10期173-176,共4页Computer Simulation

基　　金：国家自然科学基金(60773213)

摘　　要：在优化机器人动态特性的中研究,由于机器人逆运动问题。随着运动关节的增多,逆运动求解问题越来越复杂,要建立通用的解析算法相当困难。针对目前运用的粒子群算法求解时收敛精度不高、易陷入局部最优的情况,提出利用遗传粒子群优化算法求解机器人逆运动问题。运用D-H表示法建立机器人数学模型,然后从正向运动方程出发建立优化算法的目标函数,在粒子群算法中引入遗传操作求解问题。对PUMA机器人的仿真结果表明,改进方法提高了机器人位置和姿态方面的求解精度,达到了有效计算及控制机器人参数的效果。The research is about robot＇s inverse kinematics.As the kinetic joints increase,the problem of inverse kinematics is becoming more complicated,and it is very difficult to establish the general analyzing algorithm for it.Since using particle swarm algorithm in the problem has low convergence accuracy,and it is limited by local optimum,in this paper,genetic particle swarm optimization algorithm was adopted to solve the problem of robot＇s inverse kinematics.In the proposed algorithm,robot＇s mathematical model was built by using D-H representation at first.Then the objective function for optimization was established based on the equations of forward kinematics.Finally,the genetic operation was adopted into the particle swarm algorithm to solve the problem.The experimental results of simulation on PUMA robot show that,the proposed algorithm raises the accuracy in computing for the locations and postures,achieves the effective effects in computing and controlling the robot＇s parameters.

关 键 词：机器人建模 逆运动学 遗传粒子群优化 仿真 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]