Evolving distributed resource sharing for cubesat constellations

Abstract

Advances in miniaturization will allow for the commoditization of tiny satellites, known as "CubeSats." This commoditization in addition to reducing price and increasing the numbers of satellites, will also result in the "democratization" of small space missions where numerous institutions can launch their own satellites. 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 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-observation 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.

In pursuit of autonomous distributed satellite systems

“…For distributed missions, even MAS-based modelling approaches tend to be executed on-ground (Bonnet et al, 2015). Conversely, some simulation experiments have constraint their satellite agents with the communication impairment of orbital trajectories (Bonnet and Tessier, 2008;HolmesParker et al, 2012;Tripp and Palmer, 2010). These planning approaches could certainly be executed on-board, and have the potential to exploit autonomous decision-making like EO-1.…”