Auction-based and Distributed Optimization Approaches for Scheduling Observations in Satellite Constellations with Exclusive Orbit Portions

TL;DR

This paper introduces auction-based and distributed optimization methods for scheduling satellite observations, focusing on coordinating exclusive orbit portions among multiple users and a central planner, with experimental validation on realistic scenarios.

Contribution

It presents a novel formulation of the Earth Observation Satellite Constellation Scheduling Problem (EOSCSP) and proposes market-based and distributed constraint optimization solutions.

Findings

Distributed methods effectively coordinate requests without schedule sharing

Market-based techniques outperform baseline approaches in conflict resolution

Experimental results demonstrate scalability on large, realistic instances

Abstract

We investigate the use of multi-agent allocation techniques on problems related to Earth observation scenarios with multiple users and satellites. We focus on the problem of coordinating users having reserved exclusive orbit portions and one central planner having several requests that may use some intervals of these exclusives. We define this problem as Earth Observation Satellite Constellation Scheduling Problem (EOSCSP) and map it to a Mixed Integer Linear Program. As to solve EOSCSP, we propose market-based techniques and a distributed problem solving technique based on Distributed Constraint Optimization (DCOP), where agents cooperate to allocate requests without sharing their own schedules. These contributions are experimentally evaluated on randomly generated EOSCSP instances based on real large-scale or highly conflicting observation order books.