轻量级道路适应性路径跟踪基于软行为者-关键RL方法
1Beijing-Dublin International College Electronic Information Engineering, Beijing University of Technology, Beijing 100124, China.
Sensors (Basel, Switzerland)
|October 16, 2025
概括
本研究介绍了一种速度适应型机器人路径跟踪系统,使用适应软演员-关键 (ASAC) 和斯坦利方法. 该框架通过根据道路和车辆条件动态调整速度和转向来实现准确的路径.
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:
- 准确的机器人路径跟踪对于自动驾驶系统至关重要.
- 现有的方法往往难以适应动态速度和不同的道路条件.
研究的目的:
- 开发一个适应速度的机器人路径跟踪框架,以提高准确性.
- 整合Lidar-Inertial Odometry同时定位和映射 (LIO-SLAM) 进行精确的定位.
- 为了利用自适应软演员-关键 (ASAC) 来进行动态控制调整.
主要方法:
- 使用LIO-SLAM进行100Hz的机器人姿势估计.
- 快速探索随机树 (RRT) 用于全球路径规划和A*用于避免当地的障碍.
- 集成的U-Net用于路面分类和ASAC通过斯坦利方法进行自适应速度和转向控制.
主要成果:
- 拟议的STANLY_ASAC框架在各种场景中展示了准确的路径.
- 适应性控制调整了车辆的加速和侧向偏差,有效地获得.
- 路面分类通过补偿系数错误提高了跟踪精度.
结论:
- 适应速度的框架提供了强大而准确的机器人路径跟踪.
- 基于道路和车辆状态的自适应控制是性能的关键.
- 整合LIO-SLAM,RRT,A*,U-Net,ASAC和Stanley方法提供了一个全面的解决方案.
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