Fault-Aware MPC for Robotic Fleet Communications Scheduling
Carlo Schreiber, Duncan Eddy, Mykel J. Kochenderfer

TL;DR
This paper introduces IMM-MPC, a fault-aware scheduling framework for robotic fleets that optimizes communication and fault diagnosis using probabilistic fault mode beliefs and information gain.
Contribution
It develops a receding-horizon control method that distinguishes fault modes under observational aliasing, improving fault recovery in satellite communication scheduling.
Findings
IMM-MPC recovers 59.8% of lethal faults in spacecraft.
Outperforms binary-MPC and bipartite graph methods in fault recovery.
Maintains similar solve times and healthy satellite acquisition across trials.
Abstract
Operating a fleet of remote robotic systems with intermittent communications requires scheduling limited contact opportunities to maintain fleet health awareness, complete mission objectives, and intervene on faulted assets before their permanent loss. This scheduling problem is complicated by observational ambiguity: when an asset fails to check in, the operator cannot distinguish between a lethal hardware fault and a benign communications failure. If the system's failure modes are structured through a fault model, a scheduler can exploit mode-specific lethality, timing, and recoverability properties to prioritize correctly - but only if it can distinguish between modes that produce identical observations under standard actions. We present Interacting Multiple Model Model Predictive Control (IMM-MPC), a receding-horizon framework that maintains a probabilistic belief over discrete…
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