自动驾驶车辆导航的路径规划方案,集成BJA*和改进的DWA算法
Kai Xin1, Guoxu Zhou2, Huacai Lu1
1Anhui Key Laboratory of Detection Technology and Energy Saving Devices, Anhui Polytechnic University, Wuhu 241000, China.
Sensors (Basel, Switzerland)
|November 27, 2025
概括
本研究介绍了一种融合导航系统,将双向跳转点A* (BJA*) 和动态窗口方法 (DWA) 结合起来,用于自动驾驶汽车. 这种新方法可以提高路径效率,并在动态环境中实时避免障碍物.
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
- 自主系统 自主系统
背景情况:
- 传统的A*和动态窗口方法 (DWA) 算法在动态环境,路径流性和实时障碍回避方面存在局限性.
- A*与动态环境和路径质量扎,而DWA可能会卡在局部最佳状态中,需要广泛的参数调整.
研究的目的:
- 通过融合双向跳转点A* (BJA*) 和改进的DWA,提出一种新的自动驾驶车辆导航路径规划方案.
- 提高自动驾驶汽车的全球路线规划效率和局部避障强度.
主要方法:
- 拟议的方案将BJA*与改进的DWA融合在一起,通过24个社区的搜索和双向跳转点策略来提高A*的全球效率.
- 通过优化评估功能和整合全球路径信息,提高了DWA的本地稳定性.
- 使用MATLAB (2022b) 模拟来进行比较分析.
主要成果:
- 与改进的A*方法相比,BJA*在路径长度,穿越时间和转次数方面表现出显著的改善.
- 集成的局部避障系统在动态场景中为车辆提供了更稳定的速度控制.
- 废弃实验证实了BJA*中特定改善的贡献.
结论:
- 合并的BJA*-DWA导航方案有效地解决了自动驾驶传统算法的局限性.
- 拟议的方法提供了增强的全球路径规划和强大的局部障碍回避,从而实现更稳定和更有效的车辆导航.
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