A novel methodology for vision-based path planning and obstacle avoidance in mobile robot applications

The availability of overhead views of the environments around mobile robots always present useful information provided that these views are correctly perceived. In this paper, a novel approach for vision-based path planning and obstacle avoidance for mobile robots is presented. The approach uses visual perception integrated with a proposed field-based path-planning algorithm to overcome some common path-planning issues such as local minima, problematic destinations, and following long paths around obstacles. An exponential angle field is generated around each obstacle that deviates from the robot's orientation as it directs towards the destination. Obstacle field is activated or deactivated based on a proposed collision prediction algorithm. To satisfy robot convergence to the goal point, the obstacle field parameters are determined using the Lyapunov stability criterion. The method also proposes a search algorithm for proper exit in the case of robot and/or goal point trapping. Both simulation and experiments are used in validating the algorithm. The results show the effectiveness of the proposed algorithm in converging towards target points, avoiding collision with stationary obstacles and overcoming famous path-planning problems.