Multi-Target Spacecraft Mission Design using Convex Optimization and Binary Integer Programming

TL;DR

This paper introduces a nested-loop optimization method combining binary integer programming and sequential convex programming to efficiently design multi-target spacecraft missions, achieving new best solutions in a competitive benchmark.

Contribution

It presents a novel nested-loop approach that separates combinatorial and optimal control problems, improving computational efficiency and solution quality for complex mission design.

Findings

Achieved several new best-known solutions on GTOC 12.

Demonstrated computational efficiency over traditional global search methods.

Validated the approach's effectiveness for multi-target mission planning.

Abstract

The optimal design of multi-target rendezvous and flyby missions is challenging due to the combination of traditional spacecraft trajectory optimization and high-dimensional combinatorial problems. This often requires large-scale global search techniques or simplified approximations that rely on manual tuning to be performant. While global search techniques are typically computationally expensive, limiting their use in time- or cost-constrained scenarios, this work proposes a computationally efficient nested-loop approach. The problem is split into separate combinatorial and optimal control subproblems: the combinatorial problem is solved using Binary Integer Programming (BIP) with a fixed rendezvous time schedule, while the optimal control problem is handled with adaptive-mesh Sequential Convex Programming (SCP), which also optimizes the time schedule. By iterating these processes in a nested-loop structure, the approach can efficiently find high-quality solutions. When, applied to the Global Trajectory Optimization Competition 12 (GTOC 12) problem, this method results in several new best-known solutions.