虚拟的停车路径规划在狭窄的道路,基于模糊的纯追求算法
Qingyi Men1, Yongwei Wang1, Guangwei Cheng1,2
1College of Intelligent Vehicle Engineering, Luoyang Institute of Science and Technology, Luoyang, China.
PloS one
|December 29, 2025
概括
这项研究引入了用于自动停车的 Fuzzy Pure Pursuit (FPP) 算法,提高了路径跟踪精度和适应性. FPP通过根据车辆速度和道路曲率调整前距离来增强纯追求 (PP) 算法.
科学领域:
- 机器人和控制系统 机器人和控制系统
- 汽车工程 汽车工程
- 人工智能的人工智能
背景情况:
- 传统的纯追求 (PP) 算法由于固定的前距离,在停车时难以适应和准确.
- 停车机动需要精确的路径跟踪和方向角控制,这对标准算法来说是个挑战.
研究的目的:
- 开发一种自适应式自动停车路径跟踪控制算法.
- 为了提高纯追求 (PP) 算法在停车场景中的适应性和跟踪精度.
主要方法:
- 提出了一个 Fuzzy Pure Pursuit (FPP) 算法,集成了一个模糊的控制器.
- 模糊控制器通过速度和曲率的比例系数自适应地调整前景距离.
- 使用Prescan/CarSim/Simulink模拟和阿波罗高级平台验证了算法.
主要成果:
- 与传统的PP算法相比,FPP在垂直停车模拟中表现出优越的跟踪性能.
- 实现了4.8%的跟踪误差降低和7.3%的方向角度误差降低.
- 在各种初始条件下,阿波罗平台上确认了成功的路径跟踪和无碰撞停车.
结论:
- 迷糊纯追求 (FPP) 算法显著提高了自动停车的路径跟踪精度和适应性.
- FPP表现出极好的环境适应性,使其适合于现实世界的停车场应用.
- 开发的算法提供了一个强大的解决方案,用于在停车操作期间精确控制车辆.
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