基于改进粒子群算法的冗余机械臂逆运动学求解

Inverse Kinematics of Redundant Manipulator Basedon Improved Particle Swarm Algorithm

为构造最小位姿误差及最优适应值的目标函数,对标准粒子群算法进行优化改进,以解决冗余机械臂逆运动学求解问题,从4个方面对标准粒子群算法进行改进,包括种群初始化、惯性权重参数调整、差分变异及局部深度搜索等,形成的改进粒子群算法(IPSO2)具备粒子群进化及差分变异进化两种性质。使用IPSO2、IPSO1、PSOTD、PSOd及传统PSO进行冗余机械臂逆运动学求解,通过Matlab仿真实验可知,IPSO2算法相较于其他算法,其最优、最差及平均适应值、标准差、最优解成功率更加出色,模拟曲线收敛精度更佳,收敛速度更快。为了验证IPSO2算法的实用性及鲁棒性,将其用于PUMA型机器人的逆运动学求解,试验结果表明,最优适应值精度可维持在10-15数量级,说明IPSO2算法在冗余机械臂逆运动学求解中具有实用性,可为相关研究提供参考。

In order to construct the objective function of minimum pose error and optimal fitness,the study improves the standard particle swarm to solve the inverse kinematics problem of redundant manipulator.The study reforms standard particle swarm optimization from 4 aspects:population initialization,inertia weight parameter adjustment,difference variation and local depth search,etc.Improved particle swarm optimization(IPSO2)has two properties:particle swarm evolution and differential variation evolution.The study conducts IPSO2,IPSO1,PSOTD,PSOd and traditional PSO to solve the inverse kinematics of the redundant manipulator.Matlab simulation experiment shows that compared with other algorithms,IPSO2 algorithm has better optimal,worst and average fit value,standard deviation and success rate of optimal solution,better convergence accuracy of simulation curve and faster convergence speed.In order to verify the practicability and robustness of IPSO2 algorithm,it is used to solve the inverse kinematics of PUMA robot.The experimental results show that the accuracy of the optimal adaptive value can be maintained in the order of 10^(-15),which indicates that the IPSO2 algorithm is practical in solving the inverse kinematics of the redundant robot arm,and can provide reference for related research.