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在线基于树的规划,用于积极的航天器故障估计和避免碰撞
James Ragan1, Benjamin Riviere1, Fred Y Hadaegh1
1Division of Engineering and Applied Science, California Institute of Technology, Pasadena, CA 91125, USA.
Science robotics
|August 28, 2024
概括
自主机器人现在可以通过主动传感树搜索 (s-FEAST) 使用安全故障估计来识别故障. 这种方法确保了在不确定的环境中安全和最佳性能,这对于危险的任务至关重要.
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:
- 自主机器人需要在不确定的环境中进行强大的故障识别.
- 结合故障模式和状态不确定性对现有方法构成挑战.
- 时间最佳的故障隔离对于安全受限制的操作至关重要.
研究的目的:
- 为自主机器人开发一种方法,在安全限制下识别和隔离有缺陷的部件.
- 为了应对与机器人状态估计交织在一起的模两可的故障的挑战.
- 为了使动作计划能够区分同时执行器和传感器故障.
主要方法:
- 结合信念空间树搜索,边缘化过和集中不平等.
- 通过主动传感树搜索 (s-FEAST) 计划器开发了安全故障估计.
- 利用主动传感来获得信息观测,同时执行概率状态约束.
主要成果:
- 从理论上证明了s-FEAST的趋同到最佳政策.
- 在机器人航天器模拟器中实验验证了s-FEAST,成功执行了故障估计.
- 成功导航了与模型彗星的碰撞轨道,同时识别了断层.
结论:
- s-FEAST 能够在自动驾驶系统中安全有效地进行故障估计.
- 该方法即使在合故障模式和状态不确定性的情况下也有效.
- 通过模拟和机器人航天器场景验证,显示强大的性能.
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