Mykel J. Kochenderfer

Carlo Schreiber, Duncan Eddy, Mykel J. Kochenderfer

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.

Grace Ra Kim, Duncan Eddy, Vedant Srinivas et al.

Rapidly expanding low Earth orbit satellite constellations are placing increasing demands on terrestrial ground networks, motivating the development of more efficient ground station network designs. Current approaches select sites from predefined locations, limiting optimization to existing infrastructure and constraining performance. In contrast, free-placement optimization operates over a continuous spatial domain on Earth, broadening the search space and allowing higher-throughput configurations at the cost of potentially requiring new infrastructure deployment. In this work, we introduce SCORE (Sequential Cyclic Optimization via Refinement & Evaluation), a two-stage free-placement method for ground station design. SCORE combines sequential coordinate selection with cyclic refinement to manage high-dimensionality, non-convexity, and local minima that challenge global optimizers. We benchmark SCORE against one-shot methods such as differential evolution (DE) and integer programming approaches using locations from Kongsberg Satellite Services and the World Teleport Association. Tests across two commercial Earth observation constellations (Capella Space and ICEYE) and one synthetic Walker-Star constellation show that SCORE requires up to 5x fewer function evaluations to converge relative to DE while improving downlink throughput by up to 13%. Compared to fixed-site methods, unconstrained SCORE achieves up to 15% greater total downlink, establishing a strong empirical performance benchmark for flexible placement; infrastructure-constrained SCORE retains over 92% of this gain while restricting placement to within proximity of existing fiber and power infrastructure. We also explore trade-offs between expanding existing stations and deploying new sites, informing future ground network design for operational constellations.