An improved particle swarm optimization algorithm for inverse kinematics solution of multi-DOF serial robotic manipulators

Inverse kinematic solution is a critical procedure for the control of manipulators; however, only a small number of manipulators that meet certain conditions have closed-form solutions. In this paper, the conventional solution was replaced by the optimization process of a proposed fitness function. The fitness function aims to obtain a set of accurate joint angles for the desired position and orientation of the end effector. The function is based on forward kinematics; hence, this approach can avoid singularities and be suitable for solving many manipulators. For solving this optimization problem, particle swarm optimization algorithm was chosen as the basis due to its easy principle and excellent performance. To enhance the performance of the algorithm in searching global optimum, an adaptive inertia weight strategy applied to adjust the velocity of particles was proposed. Besides, for particles, to avoid trapping in local optimum at the boundary, a special boundary treatment was presented. In this study, two serial robotic manipulators were used to test the performance of adaptive particle swarm optimization (APSO). Meanwhile, several powerful PSO variants improved from disparate methods were selected to compare with APSO. The experimental results demonstrate that the proposed fitness function with APSO can solve the inverse kinematic problem of multi-DOF manipulators efficiently and accurately.