Picture of aircraft jet engine

Strathclyde research that powers aerospace engineering...

The Strathprints institutional repository is a digital archive of University of Strathclyde's Open Access research outputs. Strathprints provides access to thousands of Open Access research papers by University of Strathclyde researchers, including by Strathclyde researchers involved in aerospace engineering and from the Advanced Space Concepts Laboratory - but also other internationally significant research from within the Department of Mechanical & Aerospace Engineering. Discover why Strathclyde is powering international aerospace research...

Strathprints also exposes world leading research from the Faculties of Science, Engineering, Humanities & Social Sciences, and from the Strathclyde Business School.

Discover more...

Automatic MGA trajectory planning with a modified ant colony optimization algorithm

Ceriotti, M. and Vasile, M. (2009) Automatic MGA trajectory planning with a modified ant colony optimization algorithm. In: 21st International Space Flight Dynamics Symposium, ISSFD 2009, 2009-09-28 - 2009-10-02.

[img]
Preview
PDF
Ceriotti_M_strathprints_Automatic_MGA_trajectory_planning_with_a_modified_Ant_Colony_Optimization_algorithm.pdf - Preprint

Download (378kB) | Preview

Abstract

This paper assesses the problem of designing multiple gravity assist (MGA) trajectories, including the sequence of planetary encounters. The problem is treated as planning and scheduling of events, such that the original mixed combinatorial-continuous problem is discretised and converted into a purely discrete problem with a finite number of states. We propose the use of a two-dimensional trajectory model in which pairs of celestial bodies are connected by transfer arcs containing one deep-space manoeuvre. A modified Ant Colony Optimisation (ACO) algorithm is then used to look for the optimal solutions. This approach was applied to the design of optimal transfers to Saturn and to Mercury, and a comparison against standard genetic algorithm based optimisers shows its effectiveness.