Improved Potential Field Method for Robot Path Planning with Path Pruning

Abstract

Path planning is vital for a robot deployed in a mission in a challenging environment with obstacles around. The robot needs to ensure that the mission is accomplished without colliding with any obstacles and find an optimal path to reach the goal. Three important criteria, i.e., path length, computational complexity, and completeness, need to be taken into account when designing a path planning method. Artificial Potential Field (APF) is one of the best methods for path planning as it is fast, simple, and elegant. However, the APF has a major problem called local minima, which will cause the robot fails to reach the goal. This paper proposed an Improved Potential Field method to solve the APF limitation. Despite that, the path length produced by the Improved APF is not optimal. Therefore, a path pruning technique is proposed in order to shorten the path generated by the Improved APF. This paper also compares the performance on the path length and computational time of the Improved APF with and without path pruning. Through simulation, it is proven that the proposed technique could overcome the local minima problem and produces a relatively shorter path with fast computation time.