PSO optimization of mobile robot trajectories in unknown environments

Abstract

The Canonical Force Field (CF2) method is an approach of mobile robot path planning. The variations of CF2 parameters P, c, k, Q and ρ0 are however vital to its performance. In this paper, we used the multi-objective particle swarm optimization (PSO) approach to optimize these parameters. The computation of the optimal parameters is restarted in each new position of the robot. PSO is used to minimize the distance between this position and the target and to maximize the safe distance between this position and the obstacles. The effectiveness of the method is demonstrated by computer simulations in the Webots environment. Simulations are carried out in various known and unknown environments. In the known environments, the obstacle position is recognized by the robot at the beginning of navigation and the path planning is global. But in the unknown environments, the robot localization is based on the sensor readings and the path planning is local.