基于强化学习A*和LSTM增强DWA的USV的安全海上航线规划融合算法
Zhenxing Zhang1, Qiujie Wang2, Xiaohui Wang2
1School of Computer Science and Technology, Zhejiang University of Science and Technology, Hangzhou 310023, China.
Sensors (Basel, Switzerland)
|February 13, 2026
概括
本研究介绍了无人地面车辆 (USV) 的混合路径规划方法,使用强化学习和改进的动态窗口方法 (DWA). 这种方法在复杂的海上环境中提高了安全性和轨道平滑性.
科学领域:
- 海上机器人技术 在海上机器人技术
- 人工智能的人工智能
- 路径规划算法 路径规划算法
背景情况:
- 在动态的海上环境中,无人驾驶地面车辆 (USV) 的路径规划带来了重大安全挑战.
- 当前的方法在遇到动态障碍时,难以预测和轨迹平滑.
研究的目的:
- 为USVs开发可靠的混合路径规划方法.
- 在复杂的海洋环境中提高USV轨迹的安全性,可预测性和流性.
主要方法:
- 这是一种混合方法,结合了强化学习增强的A*算法和改进的动态窗口方法 (DWA).
- A*算法增强包括动态邻里搜索,自适应加权和路径后优化.
- 动态障碍预测使用与长短期记忆 (LSTM) 网络集成的卡尔曼波器 (KF).
- 对于符合规定的航行,DWA纳入了防止海上碰撞的国际规则 (COLREGs).
主要成果:
- 改进的A*算法证明了对USV动态模型的更好遵守.
- 改进的DWA显著降低了模拟中的碰撞风险.
- 混合方法导致了更短的路径长度和更光滑的轨迹.
结论:
- 拟议的混合路径规划方法提高了USV导航的安全性和效率.
- 整合RL,KF-LSTM和COLREGs为动态的海上环境提供了强大的解决方案.
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