| 黄家博,陈春梅,龚渔民,刘桂华,徐敏.连续障碍物环境中安全高效的A路径规划算法[J].电子测量与仪器学报,2026,40(2):252-266 |
| 连续障碍物环境中安全高效的A路径规划算法 |
| Safe and efficient A* path planning algorithm forcontinuous obstacle environments |
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| DOI： |
| 中文关键词: 路径规划 A*算法 移动机器人 小邻域搜索 连续障碍物环境 |
| 英文关键词:path planning A* algorithm mobile robots small neighborhood search continuous obstacle environments |
| 基金项目: |
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| Author | Institution |
| Huang Jiabo | 1.School of Information and Control Engineering, Southwest University of Science and Technology, Mianyang 621010, China;
2.Robot Technology Used for Special Environment Key Laboratory of Sichuan Province, Mianyang 621010, China |
| Chen Chunmei | 1.School of Information and Control Engineering, Southwest University of Science and Technology, Mianyang 621010, China;
2.Robot Technology Used for Special Environment Key Laboratory of Sichuan Province, Mianyang 621010, China |
| Gong Yumin | 1.School of Information and Control Engineering, Southwest University of Science and Technology, Mianyang 621010, China;
2.Robot Technology Used for Special Environment Key Laboratory of Sichuan Province, Mianyang 621010, China |
| Liu Guihua | 1.School of Information and Control Engineering, Southwest University of Science and Technology, Mianyang 621010, China;
2.Robot Technology Used for Special Environment Key Laboratory of Sichuan Province, Mianyang 621010, China |
| Xu Min | 1.School of Information and Control Engineering, Southwest University of Science and Technology, Mianyang 621010, China;
2.Robot Technology Used for Special Environment Key Laboratory of Sichuan Province, Mianyang 621010, China |
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| 摘要点击次数: 331 |
| 全文下载次数: 211 |
| 中文摘要: |
| 针对A*算法在大范围连续障碍物环境中存在时间效率低、遍历节点数过多、安全性低及路径不平滑的问题,提出一种改进的A*算法。首先,提出8种双层5领域来提高时间效率和路径平滑度,并设计边界拓展和满障碍物拓展的方法来解决使用小邻域搜索陷入死锁的问题;其次,提出一种启发函数分层策略,该策略将搜索区域分层并根据分层阈值赋予不同层启发函数不同权重,从而减少遍历节点数及进一步提高时间效率;最后,提出一种安全探测的方法来使路径与障碍物保持安全距离。与不同环境下的5种算法相比,仿真实验表明改进A*算法的运行时间平均减少了30.9%,路径安全性平均提高了14.7%。此外,改进A*算法的路径平滑度较高,综合性能优于其他5种算法。所提出的改进A*算法不仅能在大范围连续障碍物环境中满足移动机器人安全高效的路径规划需求,还在不同环境中表现出更强的鲁棒性。在不对路径进行二次规划的情况下改进A*算法的路径也能具有较高的平滑度。 |
| 英文摘要: |
| To address the problems of low computational efficiency, excessive node exploration, poor safety, and non-smooth path in large-scale continuous obstacle environments, this paper proposes an improved A* algorithm. Firstly, eight types of two-layer 5-neighborhood are proposed to improve computational efficiency and enhance path smoothness, and boundary expansion and full-obstacle expansion methods are designed to resolve the deadlock caused by small neighborhood search. Secondly, a hierarchical heuristic function strategy is proposed. The strategy divides the search space into multiple layers and assigns different weights to the heuristic functions of each layer based on predefined thresholds, thereby reducing the number of explored nodes and further improving computational efficiency. Finally, a safety detection method is proposed to ensure that the generated paths maintain a safe distance from obstacles. Compared with five algorithms in different environments, simulation experiments demonstrate that the improved A* algorithm reduces running time by an average of 30.9%, increases path safety by an average of 14.7%. In addition, the improved A* algorithm generates paths with moderate smoothness and achieves better overall performance than the five compared algorithms. The proposed improved A* algorithm not only satisfies the requirements for safe and efficient path planning for mobile robots in large-scale continuous obstacle environments but also shows greater robustness in different environments. Even without secondary path optimization, the improved A* algorithm generates paths with relatively high smoothness. |
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