基于强化学习方法的道路适应精确路径跟踪
1School of Information Science and Engineering, Fudan University, Shanghai 200433, China.
Sensors (Basel, Switzerland)
|August 14, 2025
概括
本研究介绍了SACPP控制器,这是一个新的速度适应自动驾驶框架. 它通过整合软演员-关键 (SAC) 和纯追求 (PP) 方法来实现有效的路径跟踪和障碍回避,以实现实时速度优化和控制.
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
- 控制系统 控制系统
背景情况:
- 自动驾驶系统需要强大的路径跟踪能力.
- 实时适应车辆动态和能源效率至关重要.
- 为了安全,避免障碍物必须与路径遵循相结合.
研究的目的:
- 提出一个名为SACPP控制器的速度适应自主驾驶路径跟踪框架.
- 为了提高路径跟踪的准确性,避开障碍物和提高能源效率.
- 为了减少实时应用程序的计算负载.
主要方法:
- 使用混合A*算法进行无障碍路径规划.
- 采用软演员-关键 (SAC) 来实现实时最佳速度和预览点预测.
- 实现基于预测预览点的前轮角度控制纯追逐 (PP).
- 设计一个专门的评估功能,以在不确定性下进行稳健的跟踪.
主要成果:
- SACPP控制器有效地跟踪路径,同时避免障碍物.
- 该框架优化了车辆的速度,以实现高电机能效.
- 轻量级的网络结构和基于几何的控制减少了计算负载.
- 控制框架在i7 CPU上实现了超过100 Hz的控制周期.
结论:
- SACPP控制器为适应速度的自动驾驶提供了一个计算高效和有效的解决方案.
- 集成SAC和PP方法可以提高性能和能源效率.
- 该框架展示了实时自主导航的实际可行性.
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