MGA trajectory planning with an ACO-inspired algorithm

M. Ceriotti, M. Vasile

Research output: Contribution to conferencePaper

1 Citation (Scopus)

Abstract

Given a set of celestial bodies, the problem of finding an optimal sequence of gravity assist manoeuvres, deep space manoeuvres (DSM) and transfer arcs connecting two or more bodies in the set is combinatorial in nature. The number of possible paths grows exponentially with the number of celestial bodies. Therefore, the design of an optimal multiple gravity assist (MGA) trajectory is a NP-hard mixed combinatorial-continuous problem, and its automated solution would greatly improve the assessment of multiple alternative mission options in a shorter time. This work proposes to formulate the complete automated design of a multiple gravity assist trajectory as an autonomous planning and scheduling problem. The resulting scheduled plan will provide the planetary sequence for a multiple gravity assist trajectory and a good estimation of the optimality of the associated trajectories. We propose the use of a two-dimensional trajectory model in which pairs of celestial bodies are connected by transfer arcs containing one DSM. The problem of matching the position of the planet at the time of arrival is solved by varying the pericentre of the preceding swing-by, or the magnitude of the launch excess velocity, for the first arc. By using this model, for each departure date we can generate a full tree of possible transfers from departure to destination. Each leaf of the tree represents a planetary encounter and a possible way to reach that planet. An algorithm inspired by Ant Colony Optimization (ACO) is devised to explore the space of possible plans. The ants explore the tree from departure to destination adding one node at the time: every time an ant is at a node, a probability function is used to select one of the remaining feasible directions. This approach to automatic trajectory planning is applied to the design of optimal transfers to Saturn and among the Galilean moons of Jupiter, and solutions are compared to those found through traditional genetic-algorithm-based techniques.

Conference

Conference60th International Astronautical Congress
CityDaejeon, Korea
Period12/10/0916/10/09

Fingerprint

Ant colony optimization
Gravitation
Trajectories
Planning
Planets
Moon
Genetic algorithms
Scheduling

Keywords

  • gravity assist manoeuvres
  • deep space manoeuvres (DSM)
  • transfer arcs
  • optimal multiple gravity assist (MGA) trajectory
  • automatic trajectory planning

Cite this

Ceriotti, M., & Vasile, M. (2009). MGA trajectory planning with an ACO-inspired algorithm. Paper presented at 60th International Astronautical Congress, Daejeon, Korea, .
Ceriotti, M. ; Vasile, M. / MGA trajectory planning with an ACO-inspired algorithm. Paper presented at 60th International Astronautical Congress, Daejeon, Korea, .
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Ceriotti, M & Vasile, M 2009, 'MGA trajectory planning with an ACO-inspired algorithm' Paper presented at 60th International Astronautical Congress, Daejeon, Korea, 12/10/09 - 16/10/09, .

MGA trajectory planning with an ACO-inspired algorithm. / Ceriotti, M.; Vasile, M.

2009. Paper presented at 60th International Astronautical Congress, Daejeon, Korea, .

Research output: Contribution to conferencePaper

TY - CONF

T1 - MGA trajectory planning with an ACO-inspired algorithm

AU - Ceriotti, M.

AU - Vasile, M.

N1 - Also published in Acta Astronautica, 2010. http://strathprints.strath.ac.uk/26351/ (This is a variant record)

PY - 2009/10/12

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KW - optimal multiple gravity assist (MGA) trajectory

KW - automatic trajectory planning

UR - http://www.iafastro.com/index.html?title=IAC2009_Technical_Programme

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M3 - Paper

ER -

Ceriotti M, Vasile M. MGA trajectory planning with an ACO-inspired algorithm. 2009. Paper presented at 60th International Astronautical Congress, Daejeon, Korea, .