Online tree-based planning for active spacecraft fault estimation and collision avoidance

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 spacec..., This data was generated by our Safe Fault Estimation via Active Sensing Tree Search (s-FEAST) algorithm and baselines, ran in simulation and on physical hardware. The source code is availalbe at https://github.com/treyra/s-FEAST, and a copy of the codebase used to generate this data is included in the data set., , # Online tree-based planning for active spacecraft fault estimation and collision avoidance

[https://doi.org/10.5061/dryad.xgxd254r1](https://doi.org/10.5061/dryad.xgxd254r1)

Data and source code are presented here for our Science Robotics paper: [Online Tree-based Planning for Active Spacecraft Fault Estimation and Collision Avoidance](https://www.science.org/doi/10.1126/scirobotics.adn472). This work develops and demonstrates the Safe Fault Estimation via Active Sensing Tree Search (s-FEAST) algorithm, which takes actions to actively diagnose faults in safety-critical settings for robotic spacecraft.

A video overview which accompanies our paper can be found below. It presents our hardware experiments which demonstrate that the simultaneous fault estimation and constraint evaluation of our algorithm is necessary to maintain safety in time-critical scenarios. The video also provides a visual summary of how our algorithm operates.

[![Full overview video](https://img.youtube.com/vi/ol...