Autonomous robots operating in uncertain or hazardous environments subject to state safety constraints must be able to identify and isolate faulty components in a time-optimal manner. When the underlying fault is ambiguous and intertwined with the robot’s state estimation, motion plans that discriminate between simultaneous actuator and sensor faults are necessary. However, the coupled fault mode and physical state uncertainty creates a constrained optimization problem that is challenging to solve with existing methods. We combined belief-space tree search, marginalized filtering, and concentration inequalities in our method, safe fault estimation via active sensing tree search (s-FEAST), a planner that actively diagnoses system faults by selecting actions that give the most informative observations while simultaneously enforcing probabilistic state constraints. We justify this approach with theoretical analysis showing s-FEAST’s convergence to optimal policies. Using our robotic spacecraft simulator, we experimentally validated s-FEAST by safely and successfully performing fault estimation while on a collision course with a model comet. These results were further validated through extensive numerical simulations demonstrating s-FEAST’s performance.

中文翻译：

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基于树的在线规划，用于主动航天器故障估计和避免碰撞


在受国家安全约束的不确定或危险环境中运行的自主机器人必须能够以时间最优的方式识别和隔离故障组件。当潜在故障不明确并且与机器人的状态估计交织在一起时，需要区分同时发生的执行器和传感器故障的运动计划。然而，耦合的故障模式和物理状态不确定性产生了约束优化问题，用现有方法很难解决。我们在我们的方法中结合了置信空间树搜索、边缘化过滤和集中不等式，通过主动感知树搜索（s-FEAST）进行安全故障估计，这是一种规划器，通过选择提供最丰富信息的观察结果的同时主动诊断系统故障强制执行概率状态约束。我们通过理论分析证明了这种方法的合理性，显示 s-FEAST 收敛于最优策略。使用我们的机器人航天器模拟器，我们在与模型彗星的碰撞过程中安全、成功地执行故障估计，从而对 s-FEAST 进行了实验验证。通过广泛的数值模拟证明 s-FEAST 的性能，这些结果得到了进一步验证。