Solution of Inverse Kinematics for General Robot Manipulators Based on Multiple Population Genetic Algorithm

Abstract

If one robot does not meet the Pieper criterion, it is then called a general robot. In such case, the closed-form methods cannot be applied to solve the inverse kinematics problem, while the numerical methods may cause considerable computational load. To solve these issues, a multiple population genetic algorithm based inverse kinematics method is proposed. To achieve the same convergence accuracy between the position and the posture, the proposed method decomposes the objective function into position function and posture function, and introduces weight coefficients to balance the convergence rate of the two functions. To avoid local convergence, a crossover operator is applied, which combines multi-point crossover with uniform crossover. To accelerate the convergence rate, a mutation operator of dynamic mutation rate and a migration operator is utilized to overcome the blindness of global convergence. Taking a general 6R robotic manipulator as an example, experiments are conducted by using the single population genetic algorithm and the proposed method. The results indicate that the proposed method can not only guarantee the stability, avoid local convergence, but also improve the convergence accuracy and rate significantly.