Scalable Model-Based Discrete Mode Estimation for a Lunar Rover Power System
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Abstract
Reliable autonomous operation of planetary rovers requires robust onboard monitoring and diagnosis of power systems under uncertain and dynamic conditions. This work presents a model-based mode estimation framework for a simplified rover power system that combines physics-based modeling with probabilistic reasoning to estimate system and environmental state, including anomalous behavior. The approach adapts the Miniature Mode Estimation (Mini-ME) architecture to perform real-time belief tracking over discrete component modes based on noisy measurement signals including voltage, current, temperature, and state of charge, and command inputs into the system. Mini-ME is a system health management tool that monitors components, diagnoses faults in real-time, and supports downstream system recovery. We simulate the physics of the simplified power system under varying loads using a lumped parameter equivalent circuit model that captures the electrical and thermal behavior of the battery. A compiled automaton is used to encode admissible probabilistic state transitions and observation likelihoods derived from sensor models. By compiling out the combinatorial search and integrating these representations within a Bayesian update process, the system efficiently detects, isolates, and diagnoses anomalies arising from faults or unexpected operating regimes. The simulation results demonstrate accurate recovery of hidden system modes during charge-discharge cycles, illustrating how model-based reasoning supports autonomous fault diagnosis and system health management in autonomous missions. This of the paper provides a thorough set of computational experiments to evaluate the performance of compiled mode estimation, versus uncompiled mode estimation, on space relevant benchmarks.
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Model-Based Diagnosis, Mode Estimation, Autonomous Systems, System Health Management, Compiled Inference, Probabilistic Reasoning
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