On the Preliminary Design of Multiple Gravity-Assist Trajectories

TL;DR

This paper presents a novel global optimization approach for designing complex multiple gravity-assist trajectories, balancing model detail and computational efficiency, demonstrated through challenging case studies.

Contribution

It introduces a hybrid global search method combining evolutionary algorithms with systematic branching for trajectory optimization.

Findings

Effective exploration of complex solution spaces

Successful application to difficult trajectory design cases

Highlights importance of model complexity in solution quality

Abstract

In this paper the preliminary design of multiple gravity-assist trajectories is formulated as a global optimization problem. An analysis of the structure of the solution space reveals a strong multimodality, which is strictly dependent on the complexity of the model. On the other hand it is shown how an oversimplification could prevent finding potentially interesting solutions. A trajectory model, which represents a compromise between model completeness and optimization problem complexity is then presented. The exploration of the resulting solution space is performed through a novel global search approach, which hybridizes an evolutionary based algorithm with a systematic branching strategy. This approach allows an efficient exploration of complex solution domains by automatically balancing local convergence and global search. A number of difficult multiple gravity-assist trajectory design cases demonstrates the effectiveness of the proposed methodology.