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

Display Omitted A new adaptive PSO method is proposed and verified by simulations and a real robot.Our proposed method has been successful applied to three-dimensional obstacle avoidance with manipulator for the home service robot.Both the free-space and obstacle avoidance states are established for evaluations in computer simulations and real-time experiments. Our PSO-IAC algorithm has achieved outstanding performance compared to other methods in these experiments. 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.

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