基于改进 PSO 算法的 6R 机器人逆运动学分析

对于有封闭解的 6R 机器人的逆运动学运算，虽然可采用解析解法、数值解法，但均需要庞大 的计算量。此外，对于机械臂逆向运动学问题，经典粒子群（PSO）算法的多次仿真实验中，存在不稳定问 题和易陷入局部最优与种群单一的问题。为此，提出一种改进的 PSO 算法：引入动态权重因子，利用动态 权重调整因子结合 CMA-ES 算法步长更新方法，平衡全局搜索和局部搜索能力；引入收缩学习因子，防止 在迭代过程中陷入局部最优。并以 REBot-V-6R 机器人为例，建立了机器人的正运动学模型，将机器人的逆 运动学求解问题转换为改进 PSO 算法的寻优问题，分别对机器人的位置误差与姿态误差进行仿真。通过将 仿真结果与经典 PSO 算法和遗传算法的仿真结果进行对比，得知在求解精度和求解稳定性方面，所提改进 算法的性能明显提升，因而验证了算法的可行性与有效性。

Inverse Kinematics Analysis of 6R Robot Based on an Improved PSO Algorithm

Despite the fact that the inverse kinematics of 6R robot with closed solution can be solved analytically and numerically, it requires a huge amount of calculation. In addition, as for the inverse kinematics of manipulator, the classical particle swarm optimization (PSO) algorithm is characterized with such flaws as instability, local optimization and single population in many simulation experiments. Therefore, an improved PSO algorithm has thus been proposed; by introducing the dynamic weight factor, the dynamic weight adjustment factor is to be combined with CMA-ES algorithm so as to balance global search and local search ability; the contraction learning factor is introduced for an avoidance of falling into local optimum in the iterative process. Taking REBot-V-6R robot as an example, the forward kinematics model of the robot is established, and the inverse kinematics problem of the robot is transformed into the optimization problem of the improved PSO algorithm, with the position error and attitude error of the robot simulated respectively as well. Based on a comparison between the simulation results and those of the classical PSO algorithm and the genetic algorithm, it can be found that the performance of the proposed improved algorithm has been significantly improved in terms of solution accuracy and stability, thus verifying the feasibility and effectiveness of the algorithm.