Recursive Target Body Approach for Low-Thrust Multiple Gravity-Assist Sequence Optimization

TL;DR

Addressing the combinatorial complexity of MGA sequencing for interplanetary missions with low-thrust propulsion, the paper proposes the Recursive Target Body Approach (RTBA). RTBA couples hodographic-shaping LTTO with a nested-loop optimization and a greedy-recursive tree-search, enhanced by a Generalized Island Model (GIM) to enable parallel evaluation. It introduces sequence-level and target-body fitness metrics (ξ and χ) to balance minimum and mean performance across islands, and demonstrates the approach on an Earth–Jupiter transfer with up to three gravity assists, achieving a minimum $ΔV$ of about $15.4$ km s$^{-1}$ under practical runtimes. The results show a robust high-fit sequence group and scalable parallel performance, indicating that RTBA provides automated, reliable preliminary optimization for low-thrust MGA trajectories.

Abstract

This work aims to automate the design of Multiple Gravity-Assist (MGA) transfers between planets using low-thrust propulsion. In particular, during the preliminary design phase of space missions, the combinatorial complexity of MGA sequencing is very large, and current optimization approaches require extensive experience and can take many days to simulate. Therefore, a novel optimization approach is developed here -- called the Recursive Target Body Approach (RTBA) -- that uses the hodographic-shaping low-thrust trajectory representation together with a unique combination of tree-search methods to automate the optimization of MGA sequences. The approach gradually constructs the optimal MGA sequence by recursively evaluating the optimality of subsequent gravity-assist targets. Another significant contribution to the novelty of this work is the use of parallelization in an original way involving the Generalized Island Model (GIM) that enables the use of new figures of merit to further increase the robustness and accelerate the convergence. An Earth-Jupiter transfer with a maximum of three gravity assists is considered as a reference problem. The RTBA takes 21.5 hours to find an EMJ transfer with 15.4 km/s $ΔV$ to be the optimum. Extensive tuning improved the quality of the MGA trajectories substantially, and as a result a robust low-thrust trajectory optimization could be ensured. A distinct group of highly fit MGA sequences is consistently found that can be passed on to a higher-fidelity method. In conclusion, the RTBA can automatically and reliably be used for the preliminary optimization of low-thrust MGA trajectories.

Figures (6)

• Figure 1: Grid search of departure dates from 61400-62860 MJD with and without additional local optimization step.
• Figure 2: A three-level tree for an Earth-Jupiter transfer. 'Y' is Mercury, and other planets are denoted by the first letter of their names.
• Figure 3: MGA sequences sorted by $f_s$, for $q = 0.5$ for three different seeds and four combinations of $\xi$ and $\chi$. Red sequences represent those present in fan2021fast.
• Figure 4: Most optimal MGA sequences sorted by $f_s$, for $q = 0.5$ for three different seeds and four combinations of $\xi$ and $\chi$. Red sequences represent those present in fan2021fast.
• Figure 5: TNW frame.