基于组合优化算法的6R机器人逆运动学求解

针对逆运动学求解存在的多解、精度低及通用性差等问题，提出了一种适用于各类6R工业机器人求逆解的组合优化算法。根据经典D-H法建立了机器人运动学模型，以最小化位姿误差为目标，结合运动平稳性原则构造了逆解问题的目标函数，以线性加权和法设计了适应度函数。通过混沌映射初始化种群、收敛因子非线性更新、自适应惯性权重位置调整及引入模拟退火策略等4种措施得到了一种改进的鲸鱼优化算法，并用于逆运动学求解。组合算法将鲸鱼算法求解的结果作为初始值，再利用Newton-Raphson数值法迭代出满足精度要求的运动学逆解。仿真试验结果表明：改进后的鲸鱼算法求解性能得到了较大提高,相比于直接利用鲸鱼算法进行逆运动学求解，组合优化算法具有求解速度快、稳定性好、精度高的特点，证明了该算法求逆的可行性与有效性。

Solution of Inverse Kinematics for 6R Robots Based on Combinatorial Optimization Algorithm#br#

In order to solve the problems of multiple solutions, low accuracy and poor generality in inverse kinematics, a combinatorial optimization algorithm was proposed to solve the inverse kinematics for all kinds of 6R industrial robots. The kinematics models of the robot were established according to the classical D-H method. The objective function of the inverse kinematics was constructed by minimizing the pose errors with the principle of motion stability, and its fitness function was designed by linear weighing-sum method. An improved whale optimization algorithm for inverse kinematics was developed by using four methods including chaotic mapping initializing population, nonlinear updating of convergence factor, adaptive inertia weight and simulated annealing strategy. The combinatorial algorithm took the results of the whale optimization algorithm as the initial value, and then the inverse kinematics solution satisfying the accuracy requirements was iterated by the Newton-Raphson numerical method. The results of simulation experiments indicate that the performance of the improved whale optimization algorithm is greatly enhanced. Compared with the direct use of whale optimization algorithm for inverse kinematics, the combinatorial optimization algorithm has the advantages of faster solution speed, better stability and ultra-high accuracy, which also proves that the proposed algorithm is feasible and effective for inverse kinematics problems.