Recursive Target Body Approach for Low-Thrust Multiple Gravity-Assist Sequence Optimization
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866917018589986816 |
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| author | Cowan, Sean Noomen, Ron |
| author_facet | Cowan, Sean Noomen, Ron |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_14402 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Recursive Target Body Approach for Low-Thrust Multiple Gravity-Assist Sequence Optimization Cowan, Sean Noomen, Ron Optimization and Control Earth and Planetary Astrophysics 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. |
| title | Recursive Target Body Approach for Low-Thrust Multiple Gravity-Assist Sequence Optimization |
| topic | Optimization and Control Earth and Planetary Astrophysics |
| url | https://arxiv.org/abs/2510.14402 |