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

James Ragan1, Benjamin Riviere1, Fred Y Hadaegh1

  • 1Division of Engineering and Applied Science, California Institute of Technology, Pasadena, CA 91125, USA.

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Summary

Autonomous robots can now identify faults using safe fault estimation via active sensing tree search (s-FEAST). This method ensures safety and optimal performance in uncertain environments, crucial for hazardous missions.

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Control Systems

Background:

  • Autonomous robots require robust fault identification in uncertain environments.
  • Coupled fault modes and state uncertainty pose challenges for existing methods.
  • Time-optimal fault isolation is critical for safety-constrained operations.

Purpose of the Study:

  • To develop a method for autonomous robots to identify and isolate faulty components under safety constraints.
  • To address the challenge of ambiguous faults intertwined with robot state estimation.
  • To enable motion plans that discriminate between simultaneous actuator and sensor faults.

Main Methods:

  • Combined belief-space tree search, marginalized filtering, and concentration inequalities.
  • Developed safe fault estimation via active sensing tree search (s-FEAST) planner.
  • Utilized active sensing to gain informative observations while enforcing probabilistic state constraints.

Main Results:

  • Theoretically demonstrated s-FEAST's convergence to optimal policies.
  • Experimentally validated s-FEAST in a robotic spacecraft simulator, successfully performing fault estimation.
  • Successfully navigated a collision course with a model comet while identifying faults.

Conclusions:

  • s-FEAST enables safe and efficient fault estimation in autonomous systems.
  • The method is effective even with coupled fault modes and state uncertainty.
  • Validated through simulations and a robotic spacecraft scenario, showing robust performance.