基于A*算法的路径规划自适应性搜索算法
Shiwei Lin1, Xiangxi Fan2, Zhixuan Xie2
1School of Computer Engineering, Jimei University, Xiamen, 361000, Fujian, China. Shiwei.Lin@jmu.edu.cn.
Scientific reports
|December 29, 2025
概括
基于障碍密度的动态指数A* (ODDEA*) 算法通过调整基于障碍密度的启发式权重来改善机器人路径规划. 与传统的A*方法相比,这种新的方法显著减少了规划时间和搜索空间.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 人工智能的人工智能
- 计算机科学 计算机科学
背景情况:
- A*算法对于机器人全球路径规划至关重要.
- 现有的A*方法与冗余节点和大搜索空间作斗争.
研究的目的:
- 引入基于障碍密度的动态指数A* (ODDEA*) 算法.
- 通过解决A*限制,提高机器人路径规划效率.
主要方法:
- ODDEA*算法根据周围的障碍物密度动态调整启发式函数权重.
- 整合了局部动态惩罚来引导机器人进入阻碍物密度较低的区域.
- 计算实验将ODDEA*与Theta*,A*和BA*在各种网格地图大小上进行比较.
主要成果:
- 与Theta*,A*和BA*相比,ODDEA*显著减少了扩展节点和规划时间.
- 在固定网格地图上,与A*相比,ODDEA*实现了46.96%的规划时间和20.33%的搜索空间.
- 实验包括小型 (20x20),中型 (40x40),大型 (60x60) 网格地图和50个随机的中型地图.
结论:
- 在机器人路径规划中,ODDEA*算法提供了卓越的性能.
- 通过优化搜索空间和减少计划时间,ODDEA*有效地将计算资源降到最低.
- 这种算法为复杂环境中高效的机器人导航提供了有希望的进步.
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