Vision Based Path Planning for Mobile Robot Using Extrapolated Artificial Potential Field and Probabilistic Obstacle Avoidance

Mobile robots are increasingly being used to do tasks in unknown environment. The potential of robots to undertake such tasks lies on their ability to intelligently and efficiently locate and interact with objects in their environment. This paper describes a novel method to plan paths for mobile robots in a partially known environment observed by an overhead camera. The environment consists of dynamic obstacles and targets. A new methodology, Extrapolated Artificial Potential Field is proposed for real time robot path planning. The proposed Extrapolated Artificial Potential Field is capable of navigating robots situated among moving obstacles and target. An algorithm for probabilistic collision detection is introduced. The paper summarizes this approach, and discusses the results of path planning experiments using an Amigobot. The result shows that our method is effective.