Research on fusing improved A* and adaptive DWA path planning algorithms

Abstract

To satisfy the performance requirements of robot path planning, an algorithm combining the improved A* algorithm and the improved Dynamic Window Approach (DWA) is proposed, which results in shorter path lengths, improved search efficiency, and path smoothness. Aiming at the challenges of the traditional A* algorithm in complex scenarios, a new heuristic function based on Manhattan and diagonal is designed, and then weights are dynamically assigned to obtain the global shortest path and the least search time. Then, an improved search strategy based on 8-neighborhoods is proposed, which improves the search efficiency and reduces the time consumption of the traditional 8-neighborhood 8-direction search method by dynamically assigning the optimal search direction of the current node. On the other hand, the traditional DWA algorithm faces some challenges, such as the paths are not globally optimal, the path planning may fail or path length may increase, the azimuthal coefficient is rigid, and the algorithm is computationally intensive. For these problems, a keypoint densification strategy is proposed to modify the deflected paths, adaptively adjust the azimuth function coefficients, and limit the range of the obstacle distance function. Finally, the proposed improved A* algorithm and fusion algorithm are compared with the existing methods. The simulation results under the ROS system show that the improved A* algorithm can generate the shortest global path in complex environments, the average path length is reduced by 3.95%, and the average path searching time is shortened by 21.62%. For the fused algorithm, the average path length and the average runtime are reduced by 5.95% and 8.7% in the moving obstacles environment.