Effective improved artificial potential field-based regression search method for autonomous mobile robot path planning

摘要

This paper presents an effective improved artificial potential field-based regression search (improved APF-based RS) method that can obtain a better and shorter path efficiently without local minima and oscillations in an environment including known, partially known or unknown, static, and dynamic environments. We redefine potential functions to eliminate oscillations and local minima problems, and use improved wall-following methods for the robots to escape non-reachable target problems. Meanwhile, we develop a regression search method to optimise the planned path. The optimisation path is calculated by connecting the sequential points produced by improved APF. The simulations demonstrate that the improved APF method easily escapes from local minima, oscillations, and non-reachable target problems. Moreover, the simulation results confirm that our proposed path planning approach can calculate a shorter or more nearly optimal than the general APF can. Results prove our improved APF-based RS method's feasibility and efficiency for solving path planning.