Optimal trajectory planning for multiple asteroid tour mission by means of an incremental bio-inspired tree search algorithm

Aram Vroom; Marilena Di Carlo; Juan Manuel Romero Martin; Massimiliano Vasile

doi:10.1109/SSCI.2016.7850108

Optimal trajectory planning for multiple asteroid tour mission by means of an incremental bio-inspired tree search algorithm

Aram Vroom, Marilena Di Carlo, Juan Manuel Romero Martin, Massimiliano Vasile

Aerospace Engineering

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

3 Citations (Scopus)

Abstract

In this paper, a combinatorial optimisation algorithm inspired by the Physarum Polycephalum mould is presented and applied to the optimal trajectory planning of a multiple asteroid tour mission. The Automatic Incremental Decision Making And Planning (AIDMAP) algorithm is capable of solving complex discrete decision making problems with the use of the growth and exploration of the decision network. The stochastic AIDMAP algorithm has been tested on two discrete astrodynamic decision making problems of increased complexity and compared in terms of accuracy and computational cost to its deterministic counterpart. The results obtained for a mission to the Atira asteroids and to the Main Asteroid Belt show that this non-deterministic algorithm is a good alternative to the use of traditional deterministic combinatorial solvers, as the computational cost scales better with the complexity of the problem.

Original language	English
Title of host publication	2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Number of pages	8
ISBN (Electronic)	9781509042401
DOIs	https://doi.org/10.1109/SSCI.2016.7850108
Publication status	Published - 9 Feb 2017
Event	2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016 - Athens, Greece Duration: 6 Dec 2016 → 9 Dec 2016

Conference

Conference	2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016
Country/Territory	Greece
City	Athens
Period	6/12/16 → 9/12/16

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10.1109/SSCI.2016.7850108

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Vroom, A., Di Carlo, M., Martin, J. M. R., & Vasile, M. (2017). Optimal trajectory planning for multiple asteroid tour mission by means of an incremental bio-inspired tree search algorithm. In 2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016 Article 7850108 Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/SSCI.2016.7850108

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title = "Optimal trajectory planning for multiple asteroid tour mission by means of an incremental bio-inspired tree search algorithm",

abstract = "In this paper, a combinatorial optimisation algorithm inspired by the Physarum Polycephalum mould is presented and applied to the optimal trajectory planning of a multiple asteroid tour mission. The Automatic Incremental Decision Making And Planning (AIDMAP) algorithm is capable of solving complex discrete decision making problems with the use of the growth and exploration of the decision network. The stochastic AIDMAP algorithm has been tested on two discrete astrodynamic decision making problems of increased complexity and compared in terms of accuracy and computational cost to its deterministic counterpart. The results obtained for a mission to the Atira asteroids and to the Main Asteroid Belt show that this non-deterministic algorithm is a good alternative to the use of traditional deterministic combinatorial solvers, as the computational cost scales better with the complexity of the problem.",

author = "Aram Vroom and {Di Carlo}, Marilena and Martin, {Juan Manuel Romero} and Massimiliano Vasile",

year = "2017",

month = feb,

day = "9",

doi = "10.1109/SSCI.2016.7850108",

language = "English",

booktitle = "2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016",

publisher = "Institute of Electrical and Electronics Engineers (IEEE)",

address = "United States",

note = "2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016 ; Conference date: 06-12-2016 Through 09-12-2016",

}

Vroom, A, Di Carlo, M, Martin, JMR & Vasile, M 2017, Optimal trajectory planning for multiple asteroid tour mission by means of an incremental bio-inspired tree search algorithm. in 2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016., 7850108, Institute of Electrical and Electronics Engineers (IEEE), 2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016, Athens, Greece, 6/12/16. https://doi.org/10.1109/SSCI.2016.7850108

Optimal trajectory planning for multiple asteroid tour mission by means of an incremental bio-inspired tree search algorithm. / Vroom, Aram; Di Carlo, Marilena; Martin, Juan Manuel Romero et al.
2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016. Institute of Electrical and Electronics Engineers (IEEE), 2017. 7850108.

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

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AB - In this paper, a combinatorial optimisation algorithm inspired by the Physarum Polycephalum mould is presented and applied to the optimal trajectory planning of a multiple asteroid tour mission. The Automatic Incremental Decision Making And Planning (AIDMAP) algorithm is capable of solving complex discrete decision making problems with the use of the growth and exploration of the decision network. The stochastic AIDMAP algorithm has been tested on two discrete astrodynamic decision making problems of increased complexity and compared in terms of accuracy and computational cost to its deterministic counterpart. The results obtained for a mission to the Atira asteroids and to the Main Asteroid Belt show that this non-deterministic algorithm is a good alternative to the use of traditional deterministic combinatorial solvers, as the computational cost scales better with the complexity of the problem.

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BT - 2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016

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Optimal trajectory planning for multiple asteroid tour mission by means of an incremental bio-inspired tree search algorithm

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