| 王军晓*,黄美琴*,冯建涵*,汪显博**.基于改进A*算法和动态窗口法的移动机器人路径规划[J].高技术通讯(中文),2026,36(2):123~136 |
| 基于改进A*算法和动态窗口法的移动机器人路径规划 |
| Path planning of mobile robot based on improved A* algorithm and dynamic window approach algorithm |
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| DOI：10. 3772 / j. issn. 1002 - 0470. 2026. 02. 002 |
| 中文关键词: 移动机器人； 路径规划； 改进A*算法； 改进动态窗口法； 栅格地图 |
| 英文关键词: mobile robot, path planning, improved A* algorithm, improved dynamic window approach, grid map |
| 基金项目: |
| 作者 | 单位 | | 王军晓* | （*浙江工业大学信息工程学院杭州 310023）
（**浙江大学海南研究院三亚 572025） | | 黄美琴* | | | 冯建涵* | | | 汪显博** | |
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| 中文摘要: |
| 在大型场景中，针对传统A*算法存在的内存开销大、搜索时间长以及动态窗口法(dynamic window approach，DWA)容易陷入局部最优、找到的最终路径不是全局最优路径等问题，提出了一种基于融合改进A*算法和DWA的混合路径规划方法。首先，将传统A*算法的24邻域扩展减少为10邻域扩展。其次，引入同步双向搜索策略，并在此基础上提出同步直连的方法，即检查2个当前动态定义的目标节点之间是否存在障碍物，若无障碍物，则直接生成最终路径。然后，将改进A*算法生成的全局路径中提取出的关键路径点作为DWA的局部目标点，并改进DWA的评价函数。仿真和实验结果表明，相比于传统A*算法，改进的A*算法有效地将遍历节点数量减少51.42%、搜索时间降低63.32%；改进的DWA可以完美避开凹形障碍物并找到全局最优路径。 |
| 英文摘要: |
| In large scenarios, a hybrid path planning method based on the fusion of the improved A* algorithm and the dynamic window approach (DWA) is proposed in response to the problems of the traditional A* algorithm, such as the large memory overhead, the long search time, and the ease of DWA to fall into the local optimum, and the final path found is not a globally optimal path. Firstly, the 24-neighbourhood expansion of the traditional A* algorithm is reduced to 10-neighbourhood expansion. Secondly, a synchronous bidirectional search strategy is introduced, and based on this, a synchronous direct connection method is proposed to check whether there is an obstacle between two current dynamically defined target nodes, and if there is no obstacle, the final path is generated directly. Then, the critical path points extracted from the global path generated by the improved A* algorithm are used as the local target points of DWA, and the evaluation function of DWA is improved. Finally, the results of simulation and experiment show that compared with the traditional A* algorithm, the improved A* algorithm effectively reduces the number of traversed nodes by 51.42% and the search time by 63.32%; and the improved DWA can perfectly avoid concave obstacles and find the globally optimal path. |
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