Decentralized Observation Allocation for a Large-Scale Constellation

Abstract

Increased space sensing enables new measurements of a wide range of Earth science phenomena including volcanism, flooding, wildfires, and weather. Large-scale observation constellations of hundreds of assets have already been deployed (for example, Planet Labs’s Dove satellites), and several constellations of tens of thousands of assets are planned. New challenges exist to rapidly assimilate available data and to optimize measurements by directing spacecraft assets to best observe complex Earth science phenomena. Centralized approaches to managing request allocation in these large constellations are constrained by 1) the need to assign/elect a central node to assign requests to spacecraft and 2) reliance on a single agent communicating with potentially thousands of dependent agents. On the other hand, entirely decentralized approaches to request allocation and observation are prone to oversatisfaction of some requests and undersatisfaction of others due to a lack of communication among agents. In large constellations, an intermediary method is necessary to solve the request allocation problem in a distributed manner. We present distributed artificial intelligence/multiagent methods that leverage existing work on distributed constraint optimization to allocate observations in a satellite constellation. We compare their performance to centralized and highly decentralized approaches using realistic orbits and observation request distributions. Our distributed algorithms can find approximate solutions to the large-scale constellation request allocation problem with low data volume for agent coordination and extend to continuous planning problems with varying request sets and availability of spacecraft agents.