Tracking of a Moving Target by Improved Potential Field Controller in Cluttered Environments

Abstract

In this paper, robot tracking of a moving target in cluttered environments by using an improved potential field controller is proposed. Genetic algorithms are used to improve the potential field controller by optimizing the forces applied to the robot. This improvement makes the robot path much more smoother during the tracking. A measure of smoothness is used to guide the genetic algorithm during the optimization. Of course more smoothing gives less distance and more speed to reach the goal. The optimized controller is simulated with different cases on Windows Vista using Matlab Software. These cases include environments with single obstacle up to three obstacles and multi-knee corridor. Results are compared to previous work, illustrating the superiority of the proposed work. Tracking of a moving target in the same cases are also simulated.