SK-PSO: A Particle Swarm Optimization Framework with SOM and K-Means for Inverse Kinematics of Manipulators

Abstract

In this paper, a particle swarm optimizer that integrates self-organizing maps and k-means clustering (SK-PSO) is proposed. This optimizer generates an asymmetric Cartesian space from random joint configurations when addressing the inverse kinematics of manipulators, followed by K-means clustering applied to the Cartesian space. The resulting clusters are used to reduce the dimensionality of the corresponding joint space using Self-Organizing Maps (SOM). During the solving process, the target point’s clustering region is determined first, and then the joint space point closest to the target point is selected as the initial population for the particle swarm algorithm. The simulation results demonstrate the effectiveness of the SK-PSO algorithm. Given the inherent asymmetry among different algorithms in handling the problem, SK-PSO achieves an average fitness value that is 0.02–0.62 times better than five other algorithms, with an asymmetric solving time that is only 0.03–0.34 times that of the other algorithms. Therefore, compared to the other algorithms, the SK-PSO algorithm offers high accuracy, speed, and precision.