Aram Vroom, M. D. Carlo, Juan Manuel Romero Martin, M. Vasile
{"title":"Optimal trajectory planning for multiple asteroid tour mission by means of an incremental bio-inspired tree search algorithm","authors":"Aram Vroom, M. D. Carlo, Juan Manuel Romero Martin, M. Vasile","doi":"10.1109/SSCI.2016.7850108","DOIUrl":null,"url":null,"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.","PeriodicalId":120288,"journal":{"name":"2016 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 IEEE Symposium Series on Computational Intelligence (SSCI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SSCI.2016.7850108","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
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.
本文提出了一种受多头绒泡菌模型启发的组合优化算法,并将其应用于多小行星漫游任务的最优轨迹规划。AIDMAP (Automatic Incremental Decision Making And Planning)算法利用决策网络的生长和探索来解决复杂的离散决策问题。在两个日益复杂的离散天体动力学决策问题上,对随机AIDMAP算法进行了测试,并在精度和计算成本方面与确定性算法进行了比较。对Atira小行星和主小行星带任务的结果表明,该非确定性算法是传统确定性组合求解方法的一个很好的替代方案,因为计算成本与问题的复杂性成比例。