Integrated particle swarm optimization algorithm based obstacle avoidance control design for home service robot

Abstract

This paper presents a new particle swarm optimization (PSO) algorithm, called the PSO-IAC algorithm, to resolve the goal of reaching with the obstacle avoidance problem for a 6-DOF manipulator of the home service robot. The proposed PSO-IAC algorithm integrates the improved adaptive inertia weight and the constriction factor with the standard PSO. Both the free-space and obstacle avoidance states are established for evaluations in computer simulations and real-time experiments. The performance comparisons of the PSO-IAC algorithm with respect to the existing inertia weighted PSO (PSO-W), constriction factor based PSO (PSO-C), constriction factor and inertia weighted PSO (PSO-CW), and adaptive inertia weighted PSO (PSO-A) algorithms are examined. Simulation results indicate that the PSO-IAC algorithm provides the fastest convergence capability. Finally, the proposed control scheme can make the manipulator of the home service robot arrive at the goal position with and without obstacles in all real-time experiments.