Improved A* algorithm for path planning in dynamic environments

Abstract

To improve the global path planning capabilities of mobile robots and achieve real-time obstacle avoidance, a robot path planning algorithm that improves the traditional A* algorithm is proposed. The A* algorithm is utilized for the design of global path planning, incorporating weight coefficients into the heuristic function to bolster search efficiency. Path smoothing is performed by improving the Floyd algorithm, aiming to reduce inflection points and increase the path smoothness. For local path planning, the artificial potential field method is adopted to address the real-time obstacle avoidance limitations of the A* algorithm. Simultaneously, local corrections are applied to mitigate potential issues associated with local minima in the artificial potential field method. Additionally, attempts are made to navigate around obstacles by fine-tuning the turning angle. Simulation results validate that the improved A* algorithm can effectively construct reasonable paths in the map environment with better search mechanism and flexibility. The improved artificial potential field algorithm successfully achieves real-time obstacle avoidance, surpassing local optimal points.