Incremental planning of multi-gravity assist trajectories

Juan Manuel Romero Martin, Luca Masi, Massimiliano Vasile, Edmondo Minisci, Richard Epenoy, Vincent Martinot, Jordi Fontdecaba Baig

Research output: Contribution to conferencePaper

3 Citations (Scopus)
202 Downloads (Pure)


The paper presents a novel algorithm for the automatic planning and scheduling of multi-gravity assist trajectories (MGA). The algorithm translates the design of an MGA transfer into a planning and scheduling process in which each planetary encounter is seen as a scheduled task. All possible transfers form a directional graph that is incrementally built and explored simultaneously forward from the departure planet to the arrival one and backward from the arrival planet to the departure one. Nodes in the graph (or tree) represent tasks (or planetary encounters). Backward and forward generated transfers are then matched during the construction of the tree to improve both convergence and exploration. It can be shown, in fact, that the multi-directional exploration of the tree, allows for better quality solutions for the same computational cost. Unlike branch and prune algorithms that use a set of deterministic branching and pruning heuristics, the algorithm proposed in this paper progressively builds a probabilistic model over all the possible tasks that form a complete trajectory. No branch is pruned but the probability of selecting one particular task increases as the algorithm progresses in the search for a solution. The effectiveness of the algorithm is demonstrated on the design optimization of the trajectory of Marco Polo, JUICE and MESSENGER missions.
Original languageEnglish
Number of pages13
Publication statusPublished - 29 Sept 2014
Event65th International Astronautical Congress (IAC 2014) - Metro Toronto Convention Centre, Toronto, Canada
Duration: 29 Sept 20143 Oct 2014


Conference65th International Astronautical Congress (IAC 2014)


  • trajectory planning
  • multi-gravity assist trajectories
  • transfer trajectories
  • probablistic design optimization


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