Tackling Dead End Scenarios by Improving Follow Gap Method with Genetic Programming

Abstract

In this paper the problem of local minimum in obstacle avoidance is solved using improved follow gap method (FGM) through combination with genetic programming (GP). Two stages of controller are proposed and applied on Robotino mobile robot equipped with nine infrared sensors. The first stage implements FGM when there is a gap between front obstacles whereas the second stage deals with the case of no front gap through the utilization of GP. Simulation and experimental work prove the effectiveness of the proposed method.