Dynamic Path Planning for Mobile Robots Based on the Improved A-Star Algorithm

Abstract

Based on the path planning and obstacle avoidance problems of mobile robots in dynamic environments, a planning algorithm combining global path planning and local path planning is proposed. Firstly, four different path planning algorithms for robots in static environments are compared. On the basis of the A_star algorithm, an improved A_Star algorithm is proposed. The search direction is reduced from eight to five to improve the search efficiency; a path smoothing optimization algorithm is designed to improve the smoothness of the path; and the adaptive function is optimized to speed up the convergence speed of the algorithm. Secondly, the path planning problem of robots in unknown dynamic environments is studied, and an optimization algorithm combining the improved A_Star algorithm with the rolling window algorithm is proposed. The algorithm can plan a global optimal path and obtain map information through the scrolling window of Dynamic Window Approach (DWA) to calculate a suitable obstacle avoidance strategy in real time for the obstacles that appear, so as to plan the optimal path after avoiding the obstacles. Finally, the simulation platform is used to verify whether the fusion algorithm has better path planning performance through comparative experiments in a randomly changing environment.