使用强化学习调自动驾驶汽车的路径跟踪控制器.
Ana Vilaça Carrasco1, João Silva Sequeira1
1Lisbon University, Instituto Superior Técnico, Lisbon, Portugal.
PeerJ. Computer science
|December 11, 2023
概括
本研究介绍了一种可适应的路径跟踪控制系统,用于使用强化学习 (RL) 的自动驾驶汽车. 该系统有效地减少了车道更改和圈期间的轨迹错误,确保了安全的导航.
科学领域:
- 机器人和控制系统 机器人和控制系统
- 人工智能的人工智能
- 自动驾驶自动驾驶的自动驾驶
背景情况:
- 自动驾驶汽车需要强大的路径跟踪,以确保安全导航.
- 传统的控制系统经常与动态的轨迹变化和复杂的机动作作斗争,比如绕道.
研究的目的:
- 为自动驾驶汽车开发一个可适应的路径跟踪控制系统.
- 为了尽量减少横向和转向轨迹错误,使用一种新的强化学习方法.
- 确保通过各种轨迹,包括车道更改和圈,安全准确的导航.
主要方法:
- 实施四个参数控制器来塑造车辆行为.
- 使用"受过教育"的Q-Learning算法进行控制器调整.
- 使用CARLA (CAR学习行动) 模拟器进行系统培训和测试.
- 整合机器人操作系统 (ROS) 桥梁以实现现实的模拟和硬件在循环中的兼容性.
主要成果:
- 这辆自动驾驶汽车表现出适应各种参考轨迹的适应性.
- 该系统实现了安全导航,并大大减少了跟踪错误.
- ROS桥梁促进了现实的模拟环境,并简化了现实世界的车辆集成.
结论:
- 拟议的基于强化学习的路径跟踪系统为自动驾驶提供了可适应和有效的解决方案.
- "受过教育"的Q-Learning方法成功地减少了轨迹错误,提高了驾驶安全.
- 由可靠性分析支持的系统架构适合模拟和现实世界的部署.
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