Decentralized, Decomposition-Based Observation Scheduling for a Large-Scale Satellite Constellation
Keywords: Multi-satellite observation scheduling, multi-agent scheduling, distributed constraint optimization
Abstract: Deploying multi-satellite constellations for Earth observation requires coordinating potentially hundreds or thousands of spacecraft. Centralized approaches to observation scheduling rely on a single controller planning the actions of each satellite. With increasing on-board capability for autonomy, we can view the constellation as a multi-agent system (MAS) and employ decentralized scheduling solutions. We formulate the problem as a distributed constraint optimization problem (DCOP) and desire limited inter-agent communication. Due to the scale and structure of the problem, existing DCOP algorithms are inadequate for this application. We develop a scheduling approach that employs a well-coordinated heuristic to decompose the global DCOP into sub-problems as to enable the application of DCOP algorithms. Building on previous work, we present the Neighborhood Stochastic Search (NSS) algorithm, a decentralized algorithm to effectively solve the Earth observing multi-satellite constellation scheduling problem. In this paper, we identify the roadblocks of deploying DCOP solvers to a large-scale, real-world problem, propose a decomposition-based scheduling approach that is effective at tackling large scale DCOPs, empirically evaluate the approach against other baselines to demonstrate the effectiveness, and discuss the generality of the approach.
Primary Keywords: Applications, Multi-Agent Planning
Category: Long
Student: No
Submission Number: 57
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