An Improved Wall Following Method for Escaping from Local Minimum in Artificial Potential Field Based Path Planning

This paper surveys the utilization of the wall following concept for path planning in mobile robotics and proposes an improved wall following method for escaping from local minimum encountered by the artificial potential field (APF) method used in real-time path planning. In the new algorithm, more reliable switching conditions are designed to guarantee the success of escape. Moreover, memory information of key points is incorporated in to enhance the robot's capability of making decision. It can be applied effectively in some more complicated unknown environments in which some previous wall following methods are proved to be inefficient or invalid. Simulation studies have been carried out for analyzing the memory information and verify the validity of the proposed method.