Large-Scale Continual Scheduling and Execution for Dynamic Distributed Satellite Constellation Observation Allocation

TL;DR

This paper introduces new online algorithms for large-scale, dynamic satellite scheduling that improve solution quality and efficiency, demonstrated through simulations and the NASA FAME mission.

Contribution

It develops the first formulation of the DCOSP problem, proposes the D-NSS algorithm, and demonstrates its effectiveness in large-scale satellite constellations.

Findings

D-NSS converges to near-optimal solutions

Outperforms baseline algorithms in solution quality

Reduces computation time and message volume

Abstract

The size and capabilities of Earth-observing satellite constellations are rapidly increasing. Leveraging distributed onboard control, we can enable novel time-sensitive measurements and responses. However, deploying autonomy to large multiagent satellite systems necessitates algorithms with efficient computation and communication. We tackle this challenge and propose new, online algorithms for large-scale dynamic distributed constraint optimization problems (DDCOP). We present the Dynamic Multi-Satellite Constellation Observation Scheduling Problem (DCOSP), a new formulation of DDCOPs that models integrated scheduling and execution. We construct an omniscient offline algorithm to compute the novel optimality condition of DCOSP and present the Dynamic Incremental Neighborhood Stochastic Search (D-NSS) algorithm, an incomplete online decomposition-based DDCOP approach. We show through simulation that D-NSS converges to near-optimal solutions and outperforms DDCOP baselines in terms of solution quality, computation time, and message volume. Our work forms the foundation of the largest in-space demonstration of distributed multiagent AI to date: the NASA FAME mission.