Ragan James, Riviere Benjamin, Hadaegh Fred Y, Chung Soon-Jo
Division of Engineering and Applied Science, California Institute of Technology, Pasadena, CA 91125, USA.
Sci Robot. 2024 Aug 28;9(93):eadn4722. doi: 10.1126/scirobotics.adn4722.
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.
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。当潜在故障不明确且与机器人的状态估计相互交织时，需要能够区分同时发生的执行器和传感器故障的运动计划。然而，耦合故障模式和物理状态不确定性会产生一个约束优化问题，用现有方法解决具有挑战性。我们在方法中结合了信念空间树搜索、边缘化滤波和集中不等式，即通过主动感知树搜索进行安全故障估计（s-FEAST），这是一种规划器，通过选择能给出最具信息性观测结果的动作同时强制执行概率状态约束来主动诊断系统故障。我们通过理论分析证明s-FEAST收敛到最优策略来证明这种方法的合理性。使用我们的机器人航天器模拟器，我们通过在与模型彗星的碰撞航线上安全且成功地执行故障估计，对s-FEAST进行了实验验证。通过广泛的数值模拟进一步验证了这些结果，展示了s-FEAST的性能。
