基于模型的预测控制和强化学习用于规划垂直停车场的车辆停车轨迹
Junren Shi1, Kexin Li1, Changhao Piao1
1School of Automation, Chongqing University of Posts and Telecommunications, Chongqing 400065, China.
Sensors (Basel, Switzerland)
|August 26, 2023
概括
本研究引入了一种使用模型预测控制和强化学习的新车辆停车方法. 这种方法显著减少了培训时间,并改善了自主停车系统的融合.
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:
- 自动停车系统经常面临长时间的轨迹规划时间和趋同困难的挑战.
- 现有的方法可能需要广泛的培训,并与有效的路径生成作斗争.
研究的目的:
- 开发一种有效的自动停车路径规划方法.
- 解决长时间规划和自主停车缓慢融合的问题.
主要方法:
- 一种两阶段的方法,将模型预测控制 (MPC) 结合起来,用于初始轨迹跟踪和近距离政策优化 (PPO),用于基于强化学习的停车.
- 使用四维奖励函数来指导神经网络参数的调整,并最大限度地减少无效的操作.
主要成果:
- 该MPC控制器展示了准确的轨迹跟踪与顺,连续的车辆运动.
- 基于PPO的方法实现了显著缩短的学习时间 (30-37.5%的DPG/TD3) 和更快的融合 (75%和68%的代比DPG/TD3).
结论:
- 提出的方法有效地克服了停车轨道规划中的缓慢融合和长时间的培训时间.
- 这种方法提高了自主停车操作的及时性和效率.
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