在动态和不断变化的环境中在线和强大的间歇运动规划.
IEEE transactions on neural networks and learning systems
|August 28, 2023
概括
本研究介绍了RRT-,一个动作规划框架,用于动态环境中的机器人. 它确保稳定性和最佳控制,尽管机器人动态和干扰是未知的.
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
- 控制理论 控制理论
背景情况:
- 动态环境对机器人运动规划构成挑战.
- 未知的机器人动态和外部干扰使控制变得复杂.
- 在线和间歇性规划对于适应性至关重要.
研究的目的:
- 提出RRT-,一个在线和间歇性动力学运动规划框架.
- 为了应对未知的机器人动态和干扰的挑战.
- 在动态环境中确保闭环稳定性.
主要方法:
- 利用快速探索的随机树 (RRT) 进行全球路径规划和重新规划.
- 作为一个边界值问题 (BVPs) 的序列,制定途径点生成.
- 使用强大的间歇式Q学习控制器,使用轻松的持续激发技术.
主要成果:
- 拟议的框架有效地规划了动态环境中的运动.
- 在Q学习控制器汇聚到最佳的控制,尽管未知的动态.
- 基于Lyapunov的证明证实了闭环稳定性.
结论:
- 在不确定的动态环境中,RRT-为动态运动规划提供了强大的解决方案.
- 整合RRT和Q学习提供了适应性和稳定的机器人控制.
- 数字实验验证了框架的有效性.
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