Obstacle recognition for path planning in autonomous mobile robots

Abstract

Computer vision is an important task in robotics applications. This work proposes an approach for autonomous mobile robot navigation using the integration of the template-matching filters for obstacle detection and the evolutionary artificial potential field method for path planning. The recognition system employs a digital camera to sense the environment of a mobile robot. The captured scene is processed by a bank of space variant filters in order to find the obstacles and a feasible area for the robot navigation. The path planning employs evolutionary artificial potential fields to derive optimal potential field functions using evolutionary computation. Simulation results to validate the analysis and implementation are provided; they were specifically made to show the effectiveness and the efficiency of the proposal.