Experience mixed the modified artificial potential field method

Abstract

How to find a safe and collision-free path in unstructured environments is always an important issue in mobile robotics. This paper proposed a new path planning method that exploited past experience for obstacle avoidance with a modified artificial potential field, which could help the robot avoid collisions with obstacles effectively and find the optimal path from the start to the goal. This algorithm uses case-based reasoning to obtain the available prior information of the current environment. By retrieving the past cases and adapting to the changes of the environment to solve the problem. The experiments show that this method greatly improves the performance of the robot in terms of time and distance of the path taken from the start to the target.