Automated multigravity assist trajectory planning with a modified ant colony algorithm

Matteo Ceriotti, Massimiliano Vasile

Research output: Contribution to journalArticlepeer-review

29 Citations (Scopus)
66 Downloads (Pure)


The paper presents an approach to transcribe a multigravity assist trajectory
design problem into an integrated planning and scheduling problem. A modified Ant Colony Optimization (ACO) algorithm is then used to generate optimal plans corresponding to optimal sequences of gravity assists and deep space manoeuvers to reach a given destination. The modified Ant Colony Algorithm is based on a hybridization between standard ACO paradigms and a tabu-based heuristic. The scheduling algorithm is integrated into the trajectory model to provide a fast time-allocation of the events along the trajectory. The approach demonstrated to be very
effective on a number of real trajectory design problems.
Original languageEnglish
Pages (from-to)261-293
Number of pages32
JournalJournal of Aerospace Computing, Information, and Communication
Issue number9
Publication statusPublished - 30 Sep 2010


  • multigravity assist trajectory design
  • ant colony optimization algorithm
  • planning


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