Fault-Aware MPC for Robotic Fleet Communications Scheduling

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 fault modes with time-inhomogeneous dynamics and optimizes a two-term objective coupling acquisition value with information gain. We characterize when observationally aliased fault modes can be disambiguated through scheduled actions and when aliasing is permanently unresolvable. Applied to satellite launch and early orbit communications scheduling, IMM-MPC recovers 59.8% of spacecraft experiencing lethal-faults versus 9.0% for binary-MPC and 2.0% for a bipartite graph-based formulation solved through matching. These results hold across 200 randomized trials, while maintaining identical acquisition of healthy satellites and near-identical solve times.