对于一类全学平面代理的反应性最佳运动规划,使用强化学习与可证明的保证
Panagiotis Rousseas1, Charalampos Bechlioulis2, Kostas Kyriakopoulos3
1Control Systems Laboratory, School of Mechanical Engineering, National Technical University of Athens, Athens, Greece.
Frontiers in robotics and AI
|January 18, 2024
概括
本研究将强化学习 (RL) 与控制理论相结合,以实现最佳的运动规划. 这种新的方法确保了安全性和融合,同时有效地找到接近全球的最佳路径.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 控制理论 控制理论
- 人工智能的人工智能
背景情况:
- 控制理论中的反应式方法提供了稳定性,但在运动规划中往往忽视了最佳性.
- 传统的运动规划通常依赖于离散/基于图形的解决方案,这些解决方案可能是计算密集的.
- 强化学习 (RL) 为解决反应性控制方法的局限性提供了新的可能性.
研究的目的:
- 开发一个强化学习 (RL) 框架,以实现最佳的反应性运动规划.
- 将控制理论和RL的优势结合起来,以提供增强的运动规划解决方案.
- 为了应对在反应性运动规划中实现最佳化的挑战.
主要方法:
- 制定了一个政策代RL方案,与控制理论原则保持一致.
- RL用于导出最佳的控制输入,避免复杂的部分微分方程.
- 控制理论论证确保了安全,融合和政策改进.
主要成果:
- 拟议的方法在模拟合成工作空间中得到了验证.
- 性能与既有反应方法和采样计划器 (PRM,RRT*) 相比较.
- 基于RL的方法显示性能与现有方法相比或优于现有方法,表明近乎全球最佳性.
结论:
- 集成的RL和控制理论方法有效地解决了最佳的反应运动规划问题.
- 该方法为从任何工作空间位置到目标的规划提供了可行的解决方案.
- 这一框架为强大和最佳的运动规划提供了一个有希望的方向.
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