Evolving Solutions to TSP Variants for Active Space Debris Removal

D. Izzo, I. Getzner, D. Hennes, L.F. Simões

Research output: Chapter in Book / Report / Conference proceedingConference contributionAcademicpeer-review

Abstract

The space close to our planet is getting more and more polluted. Orbiting debris are posing an increasing threat to operational orbits and the cascading effect, known as Kessler syndrome, may result in a future where the risk of orbiting our planet at some altitudes will be unacceptable. Many argue that the debris density at the Low Earth Orbit (LEO) has already reached a level sufficient to trigger such a cascading effect. An obvious consequence is that we may soon have to actively clean space from debris. Such a space mission will involve a complex combinatorial decision as to choose which debris to remove and in what order. In this paper, we find that this part of the design of an active debris removal mission (ADR) can be mapped into increasingly complex variants to the classic Travelling Salesman Problem (TSP) and that they can be solved by the Inver-over algorithm improving the current state-of-the-art in ADR mission design. We define static and dynamic cases, according to whether we consider the debris orbits as fixed in time or subject to orbital perturbations. We are able, for the first time, to select optimally objects from debris clouds of considerable size: hundreds debris pieces considered while previous works stopped at tens.
Original languageEnglish
Title of host publicationProceedings of the 17th annual conference on Genetic and evolutionary computation (GECCO 2015)
EditorsS. Silva
PublisherACM Press
Pages1207-1214
ISBN (Print)9781450334723
DOIs
Publication statusPublished - 2015
EventGenetic and Evolutionary Computation Conference (GECCO) -
Duration: 11 Jul 201515 Jul 2015

Conference

ConferenceGenetic and Evolutionary Computation Conference (GECCO)
Period11/07/1515/07/15

Bibliographical note

Nominated for Best Paper Award at GECCO 2015 (Track: Real World Applications)

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