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基于改进的APF和SMC的自动驾驶汽车的路径规划和路径跟踪控制研究
Yong Zhang1, Kangting Liu1, Feng Gao1
1College of Automobile and Traffic Engineering, Nanjing Forestry University, Nanjing 210037, China.
Sensors (Basel, Switzerland)
|September 28, 2023
概括
这项研究使用改进的人工潜力场 (APF) 算法增强了自动驾驶汽车路径规划,并优化了跟踪控制,以实现更安全,更顺的导航. 新方法确保了更好的路径坚持和避免碰撞.
科学领域:
- 机器人技术和自主系统
- 控制工程 控制工程 控制工程
- 人工智能的人工智能
背景情况:
- 路线规划和跟踪控制对自动驾驶汽车至关重要.
- 人工潜力场 (APF) 算法提供了完整性,但存在局部最小值,不可达到的目标和安全问题.
- 现有的方法需要改进,以解决现实应用的这些局限性.
研究的目的:
- 为自动驾驶汽车路径规划提出一个改进的人工潜力场 (APF) 算法.
- 开发一个立方B-spline路径优化方法.
- 设计一个先进的滑动模式控制器,用于准确的路径跟踪.
主要方法:
- 一个改进的APF算法结合了障碍物速度,道路限制,虚拟子目标和速度排斥场.
- 一个立方B-spline方法用于路径优化.
- 一个滑动模式控制器,集成横向和方向错误与增强的滑动模式功能.
主要成果:
- 改进的APF算法产生更小,连续的方向角和曲率的路径,考虑到车辆动力学.
- 路径跟踪实验显示横向误差在0.06m以内,曲折角度误差在0.3rad以内,在各种速度下.
- 提出的方法证明了有效的路径规划和高精度跟踪控制.
结论:
- 增强的APF算法有效地解决了传统APF方法的局限性.
- 集成路线规划和跟踪控制系统确保安全,顺和准确的自动驾驶车辆导航.
- 该研究在模拟平台上验证了拟议的方法,显示了性能的显著改善.
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