Evolving distributed resource sharing for cubesat constellations

Authors:
Adrian Agogino;Chris HolmesParker;Kagan Tumer
Affiliations:
University of California, Santa Cruz, Santa Cruz, CA, USA;Oregon State University, Corvallis, OR, USA;Oregon State University, Corvallis, OR, USA
Venue:
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Year:
2012

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Abstract

Advances in miniaturization will allow for the commoditization of large numbers of tiny satellites, known as "CubeSats." However, current algorithms made for small tightly-managed space missions are ill-designed to take advantage of the huge amount of resources available in a decentralized collection of these CubeSats. We believe that multiagent evolutionary algorithms are ideally suited to exploit the distributed nature of this new problem. This paper presents a solution where a customer in need of satellite observations can reliably obtain these observations at low cost, through the help of a multiagent system as an intermediary. Each agent in this system is assigned to a single CubeSat. Given a set of the customer's observational needs, and models of the CubeSats' salient properties, the agents evolve policies that attempt to purchase an appropriate set of observations at a low price. This system is especially flexible as it demands no centralized resource broker, contracts or commitments of resources. We perform a series of experiments on an Earth-observition domain. The results show that the evolutionary methods combined with multiagent techniques have three times the performance of a simple hand-coded allocation algorithm, and twice the performance of simple evolving agents.