Efficient path planning method based on A-STAR

Abstract

Path planning is a crucial aspect of agent (robot) automation, as its efficacy directly affects the quality of tasks performed. This paper proposes an innovative and efficient path planning method by merging the advantages of the A-STAR and the artificial potential field (APF) technique. The proposed method of calculation aims to enhance the traditional A-STAR approach by incorporating an artificial potential field system. Specifically, the estimated cost in the conventional A-STAR approach is ameliorated through the integration of a precisely designed estimated cost gain. The gain is determined by the direction of the force in the artificial potential field., thus enabling the algorithm to focus more accurately on the direction of the target location while avoiding obstacles during path exploration. Through simulation results, the improved algorithm proves its feasibility by maintaining the same path length while reducing the running time by decreasing multiple exploration directions. The improved algorithm, which is more efficient, surpasses the conventional A-STAR and can be utilized for agent trajectory planning in static and complex environments, showcasing its superior performance.