Mobile Robot Path Planning by Improved A* Algorithm Fused with Improved Dynamic Window Approach
To satisfy the performance requirements for robot path planning,an algorithm integrating improved A* algorithm and improved Dynamic Window Approach(DWA)is proposed,which shortens the path length and improves the searching efficiency and path smoothness.To combat the challenges of the traditional A* algorithm in complex scenarios,a new heuristic function is designed based on Manhattan distance and the diagonal distance.The weights are assigned dynamically,and the global shortest path and the least searching time are obtained.Next,an improved search strategy based on the 8-neighborhood is proposed,which involves dynamically assigning the optimal search direction to the current node,thus improving the searching efficiency and reducing the time consumption compared to the traditional 8-neighborhood 8-direction search method.Subsequently,the Floyd algorithm is employed to remove redundant nodes,reduce the steering times,and shorten the path distance.Additionally,the traditional DWA faces certain challenges;for instance,the path is not globally optimal,the path planning may fail,or the path length may increase.To solve these problems,a keypoint densification strategy is proposed to modify the deflective path.Finally,the proposed improved A* algorithm and fusion algorithm are compared with existing methods.The simulation results show that the improved A* algorithm can generate the shortest global path in complex environments,reducing the average steering time by 16.3％and shortening the average path searching time by 55.66％.For the fused algorithm,the average path length and average runtime shorten by 6.1％and 14.7％in the temporary obstacle environment,respectively,and shorten by 1.6％and 39.8％,respectively,in the moving obstacle environment.
万方数据
维普
