Path planning for mobile robots based on improved A* algorithm

Abstract

In order to improve the dynamic obstacle avoidance ability and operational efficiency in unmanned vehicle path planning, a new algorithm incorporating the improved A* algorithm and DWA (dynamic window approach) is proposed and applied to indoor handling work. First, the valuation function is improved using the Euclidean distance and the heuristic function is dynamically weighted to reduce the expansion points, increase the real-time performance of the planning path, greatly reduce the search time, and improve the flexibility of the algorithm. Then, the path is optimized using Bessel curves to improve the smoothness. Finally, the dynamic window algorithm is integrated, and local path planning is carried out with the global optimal path guaranteed by the A* algorithm, so that it can dynamically avoid obstacles and improve path planning. Simulation experimental results show that the improved A* algorithm reduces the search nodes by 75%, the search time is reduced by 91.9%, the path length is still optimal, and the number of turns is reduced by 25%, while the fusion algorithm does not increase the path length and the search time by more than 3% after adding unknown obstacles. The final experiment verifies that the algorithm has good feasibility in real scenarios.