关键词: 工业机器人, 逆运动学, 适应度函数, 鲸鱼优化算法, 组合算法 

Abstract: 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.

Key words: industrial robot, inverse kinematics, fitness function, whale optimization algorithm, combinatorial algorithm