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

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

2 Citations (Scopus)
65 Downloads (Pure)

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 languageEnglish
Title of host publication2016 IEEE Symposium Series on Computational Intelligence (SSCI)
Place of PublicationPiscataway, NJ
PublisherIEEE
Number of pages8
ISBN (Electronic)9781509042401
DOIs
Publication statusPublished - 13 Feb 2017
Event2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016 - Royal Olympic Hotel, Athens, Greece
Duration: 6 Dec 20169 Dec 2016
http://ssci2016.cs.surrey.ac.uk/

Conference

Conference2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016
Abbreviated titleSSCI 2016
CountryGreece
CityAthens
Period6/12/169/12/16
Internet address

Fingerprint

Asteroids
Trajectory Planning
Optimal Trajectory
Trees (mathematics)
Tree Algorithms
Search Algorithm
Decision making
Decision Making
Trajectories
Planning
Computational Cost
Combinatorial Algorithms
Combinatorial optimization
Combinatorial Optimization
Space flight
Costs
Optimization Algorithm
Incremental
Trajectory
Alternatives

Keywords

  • veins
  • mathematical model
  • decision making
  • planning
  • trajectory
  • complexity theory
  • phase change random access memory
  • computational cost
  • optimal trajectory planning
  • multiple asteroid tour mission
  • incremental bio-inspired tree search algorithm
  • physarum polycephalum mould

Cite this

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) [7850108] Piscataway, NJ: IEEE. https://doi.org/10.1109/SSCI.2016.7850108
Vroom, Aram ; Di Carlo, Marilena ; Martin, Juan Manuel Romero ; Vasile, Massimiliano. / Optimal trajectory planning for multiple asteroid tour mission by means of an incremental bio-inspired tree search algorithm. 2016 IEEE Symposium Series on Computational Intelligence (SSCI). Piscataway, NJ : IEEE, 2017.
@inproceedings{579a2fecfb9d48bb824032d5f4bfc8b8,
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.",
keywords = "veins, mathematical model, decision making, planning, trajectory, complexity theory, phase change random access memory, computational cost, optimal trajectory planning, multiple asteroid tour mission, incremental bio-inspired tree search algorithm, physarum polycephalum mould",
author = "Aram Vroom and {Di Carlo}, Marilena and Martin, {Juan Manuel Romero} and Massimiliano Vasile",
note = "{\circledC} 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.",
year = "2017",
month = "2",
day = "13",
doi = "10.1109/SSCI.2016.7850108",
language = "English",
booktitle = "2016 IEEE Symposium Series on Computational Intelligence (SSCI)",
publisher = "IEEE",

}

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)., 7850108, IEEE, Piscataway, NJ, 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; Vasile, Massimiliano.

2016 IEEE Symposium Series on Computational Intelligence (SSCI). Piscataway, NJ : IEEE, 2017. 7850108.

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

TY - GEN

T1 - Optimal trajectory planning for multiple asteroid tour mission by means of an incremental bio-inspired tree search algorithm

AU - Vroom, Aram

AU - Di Carlo, Marilena

AU - Martin, Juan Manuel Romero

AU - Vasile, Massimiliano

N1 - © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

PY - 2017/2/13

Y1 - 2017/2/13

N2 - 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.

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.

KW - veins

KW - mathematical model

KW - decision making

KW - planning

KW - trajectory

KW - complexity theory

KW - phase change random access memory

KW - computational cost

KW - optimal trajectory planning

KW - multiple asteroid tour mission

KW - incremental bio-inspired tree search algorithm

KW - physarum polycephalum mould

UR - http://www.scopus.com/inward/record.url?scp=85016082863&partnerID=8YFLogxK

UR - http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=7840087

U2 - 10.1109/SSCI.2016.7850108

DO - 10.1109/SSCI.2016.7850108

M3 - Conference contribution book

BT - 2016 IEEE Symposium Series on Computational Intelligence (SSCI)

PB - IEEE

CY - Piscataway, NJ

ER -

Vroom A, Di Carlo M, Martin JMR, Vasile M. 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). Piscataway, NJ: IEEE. 2017. 7850108 https://doi.org/10.1109/SSCI.2016.7850108