Automated multigravity assist trajectory planning with a modified ant colony algorithm

Ceriotti, Matteo and Vasile, Massimiliano (2010) Automated multigravity assist trajectory planning with a modified ant colony algorithm. Journal of Aerospace Computing, Information, and Communication, 7 (9). pp. 261-293. ISSN 1542-9423 (https://doi.org/10.2514/1.48448)

PDF. Filename: Ceriotti_M_Vasile_M_Pure_Automated_multigravity_assist_trajectory_planning_with_a_modified_ant_colony_algorithm_2010.pdf Accepted Author Manuscript Download (662kB)

Abstract

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.

• Share and Export
  

  RDF+XMLBibTeXRIOXX2 XMLRDF+N-TriplesJSONDublin CoreAtomSimple MetadataReferMETSHTML CitationASCII CitationOpenURL ContextObjectEndNoteOpenURL ContextObject in SpanMODSMPEG-21 DIDLEP3 XMLData Cite XMLRIS (EndNote Online, Zotero, Ref manager, etc)RDF+N3Multiline CSV
• Item metadata

  Item type:	Article
  ID code:	26352
  Dates:	Date Event 30 September 2010 Published
  Subjects:	Technology > Mechanical engineering and machinery Technology > Motor vehicles. Aeronautics. Astronautics
  Department:	Faculty of Engineering > Mechanical and Aerospace Engineering
  Depositing user:	Ms Katrina May
  Date deposited:	21 Jul 2010 09:15
  Last modified:	25 Apr 2024 00:21
  URI:	https://strathprints.strath.ac.uk/id/eprint/26352