dorsal/arxiv
View SchemaLarge-Scale Continual Scheduling and Execution for Dynamic Distributed Satellite Constellation Observation Allocation
| Authors | Itai Zilberstein, Steve Chien |
|---|---|
| Categories | |
| ArXiv ID | 2601.06188vv1 |
| URL | https://arxiv.org/abs/2601.06188 |
| License | http://creativecommons.org/licenses/by-nc-nd/4.0/ |
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 satellites requires efficient computation and communication. This work tackles the challenge of efficiently scheduling observations for hundreds of satellites in a dynamic, large-scale problem with millions of variables. We present the Dynamic Multi-Satellite Constellation Observation Scheduling Problem (DCOSP), a new formulation of Dynamic Distributed Constraint Optimization Problems (DDCOP) that models integrated scheduling and execution. DCOSP has a novel optimality condition for which we construct an omniscient offline algorithm for its computation. We also present the Dynamic Incremental Neighborhood Stochastic Search algorithm (D-NSS), an incomplete online decomposition-based DDCOP algorithm that repairs and solves sub-problems when problem dynamics occur. 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. As part of the NASA FAME mission, DCOSP and D-NSS will be the foundation of the largest in-space demonstration of distributed multi-agent AI to date.
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"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 satellites requires efficient computation and communication. This work tackles the challenge of efficiently scheduling observations for hundreds of satellites in a dynamic, large-scale problem with millions of variables. We present the Dynamic Multi-Satellite Constellation Observation Scheduling Problem (DCOSP), a new formulation of Dynamic Distributed Constraint Optimization Problems (DDCOP) that models integrated scheduling and execution. DCOSP has a novel optimality condition for which we construct an omniscient offline algorithm for its computation. We also present the Dynamic Incremental Neighborhood Stochastic Search algorithm (D-NSS), an incomplete online decomposition-based DDCOP algorithm that repairs and solves sub-problems when problem dynamics occur. 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. As part of the NASA FAME mission, DCOSP and D-NSS will be the foundation of the largest in-space demonstration of distributed multi-agent AI to date.",
"arxiv_id": "2601.06188",
"authors": [
"Itai Zilberstein",
"Steve Chien"
],
"categories": [
"cs.AI"
],
"license": "http://creativecommons.org/licenses/by-nc-nd/4.0/",
"title": "Large-Scale Continual Scheduling and Execution for Dynamic Distributed Satellite Constellation Observation Allocation",
"url": "https://arxiv.org/abs/2601.06188",
"version": "v1"
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