Picture of person typing on laptop with programming code visible on the laptop screen

World class computing and information science research at Strathclyde...

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 researchers from the Department of Computer & Information Sciences involved in mathematically structured programming, similarity and metric search, computer security, software systems, combinatronics and digital health.

The Department also includes the iSchool Research Group, which performs leading research into socio-technical phenomena and topics such as information retrieval and information seeking behaviour.

Explore

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.