An Efficient Potential-Function Based Path-Planning Algorithm for Mobile Robots in Dynamic Environments with Moving Targets

Existing potential-field based path planning methods in the literature often do not take into account environmental constraints and robot dimensions. Moreover, they normally do not provide the shortest path either. In this paper, we develop a new repulsive potential function that incorporates robot dimensions as well as the clearance between the robot and obstacles; using this repulsive function, we mathematically prove that the robot is guaranteed to reach the goal. To avoid obstacle’s cavity, we develop our technique “virtual-obstacle”, and for local minima we modify the existing artificial goal-technique to ensure robot reaches the goal. Our algorithm renders several solutions amongst which we choose the shortest path. We consider both static and dynamic obstacles with static and moving targets and demonstrate the effectiveness of our algorithm in several simulations including narrow passages which is a difficult case. The proposed method, by considering physical and environmental constraints, is an improvement to existing path planning algorithms and is of practical use for implementation in real environments.