An Efficient Improved Artificial Potential Field Based Regression Search Method for Robot Path Planning


Path planning field for autonomous mobile robot is an optimization problem that involves computing a collision- free path between initial location and goal location. In this paper, we present an improved artificial potential field based regression search (Improved APF-based RS) method which can obtain a global sub-optimal/optimal path efficiently without local minima and oscillations in complete known environment information. We redefine potential functions to eliminate non-reachable and local minima problems, and utilize virtual local target for robot to escape oscillations. Due to the planned path by improved APF is not the shortest/approximate shortest trajectory, we develop a regression search (RS) method to optimize the planned path. The optimization path is calculated by connecting the sequential points which produced by improved APF. Amount of simulations demonstrate that the improved APF method very easily escape from local minima and oscillatory movements. Moreover, the simulation results confirm that our proposed path planning approach could always calculate a more global optimal/near- optimal, collision-free and safety path to its destination compare with general APF. That proves our improved APF-based RS method very feasibility and efficiency to solve path planning which is a NP-hard problem for autonomous mobile robot.

Proceedings of the 2012 IEEE International Conference on Mechatronics and Automation, pp.1227-1232