Trajectory Optimization for Multi-target Rendezvous Considering State Uncertainty

Rui Hou; Zhenyu Li; Xingyu Zhou; Xiangyu Li

doi:10.1007/978-981-95-3025-0_13

Trajectory Optimization for Multi-target Rendezvous Considering State Uncertainty

Rui Hou
, Zhenyu Li
, Xingyu Zhou
, Xiangyu Li^*

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

This paper proposes a method for designing Multi-Target Rendezvous (MTR) trajectories, focusing on addressing uncertainties due to observational errors in target spacecraft’s state. By integrating Gaussian Process (GP) surrogate models with Genetic Algorithms (GA), the method predicts spacecraft state evolution post-impulsive maneuvers, reducing computational costs while maintaining prediction accuracy. Applied to MTR missions in geostationary orbit, the GA+GP method’s results closely match real performance, with errors of 0.84% in velocity increment and 0.31% in rendezvous distance. Furthermore, the surrogate-based optimization reduces computational time to one-thousandth compared to pure GA, offering a feasible solution for online trajectory optimization.

Original language	English
Title of host publication	Proceedings of the 2nd Aerospace Frontiers Conference, AFC 2025 - Volume VII
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	184-199
Number of pages	16
ISBN (Print)	9789819530243
DOIs	https://doi.org/10.1007/978-981-95-3025-0_13
Publication status	Published - 2026
Externally published	Yes
Event	2nd Aerospace Frontiers Conference, AFC 2025 - Beijing, China Duration: 11 Apr 2025 → 14 Apr 2025

Publication series

Name	Lecture Notes in Mechanical Engineering
ISSN (Print)	2195-4356
ISSN (Electronic)	2195-4364

Conference

Conference	2nd Aerospace Frontiers Conference, AFC 2025
Country/Territory	China
City	Beijing
Period	11/04/25 → 14/04/25

Keywords

Gaussian process
multi-target rendezvous
surrogate model
trajectory optimization

Access to Document

10.1007/978-981-95-3025-0_13

Cite this

@inproceedings{251abff017c844c1a9ca9c27e490a0ea,

title = "Trajectory Optimization for Multi-target Rendezvous Considering State Uncertainty",

abstract = "This paper proposes a method for designing Multi-Target Rendezvous (MTR) trajectories, focusing on addressing uncertainties due to observational errors in target spacecraft{\textquoteright}s state. By integrating Gaussian Process (GP) surrogate models with Genetic Algorithms (GA), the method predicts spacecraft state evolution post-impulsive maneuvers, reducing computational costs while maintaining prediction accuracy. Applied to MTR missions in geostationary orbit, the GA+GP method{\textquoteright}s results closely match real performance, with errors of 0.84\% in velocity increment and 0.31\% in rendezvous distance. Furthermore, the surrogate-based optimization reduces computational time to one-thousandth compared to pure GA, offering a feasible solution for online trajectory optimization.",

keywords = "Gaussian process, multi-target rendezvous, surrogate model, trajectory optimization",

author = "Rui Hou and Zhenyu Li and Xingyu Zhou and Xiangyu Li",

note = "Publisher Copyright: {\textcopyright} Press of Acta Aeronautica et Astronautica Sinica 2026.; 2nd Aerospace Frontiers Conference, AFC 2025 ; Conference date: 11-04-2025 Through 14-04-2025",

year = "2026",

doi = "10.1007/978-981-95-3025-0\_13",

language = "English",

isbn = "9789819530243",

series = "Lecture Notes in Mechanical Engineering",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "184--199",

booktitle = "Proceedings of the 2nd Aerospace Frontiers Conference, AFC 2025 - Volume VII",

address = "Germany",

}

Hou, R, Li, Z, Zhou, X & Li, X 2026, Trajectory Optimization for Multi-target Rendezvous Considering State Uncertainty. in Proceedings of the 2nd Aerospace Frontiers Conference, AFC 2025 - Volume VII. Lecture Notes in Mechanical Engineering, Springer Science and Business Media Deutschland GmbH, pp. 184-199, 2nd Aerospace Frontiers Conference, AFC 2025, Beijing, China, 11/04/25. https://doi.org/10.1007/978-981-95-3025-0_13

Trajectory Optimization for Multi-target Rendezvous Considering State Uncertainty. / Hou, Rui; Li, Zhenyu; Zhou, Xingyu et al.
Proceedings of the 2nd Aerospace Frontiers Conference, AFC 2025 - Volume VII. Springer Science and Business Media Deutschland GmbH, 2026. p. 184-199 (Lecture Notes in Mechanical Engineering).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Hou, Rui

AU - Li, Zhenyu

AU - Zhou, Xingyu

AU - Li, Xiangyu

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PY - 2026

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N2 - This paper proposes a method for designing Multi-Target Rendezvous (MTR) trajectories, focusing on addressing uncertainties due to observational errors in target spacecraft’s state. By integrating Gaussian Process (GP) surrogate models with Genetic Algorithms (GA), the method predicts spacecraft state evolution post-impulsive maneuvers, reducing computational costs while maintaining prediction accuracy. Applied to MTR missions in geostationary orbit, the GA+GP method’s results closely match real performance, with errors of 0.84% in velocity increment and 0.31% in rendezvous distance. Furthermore, the surrogate-based optimization reduces computational time to one-thousandth compared to pure GA, offering a feasible solution for online trajectory optimization.

AB - This paper proposes a method for designing Multi-Target Rendezvous (MTR) trajectories, focusing on addressing uncertainties due to observational errors in target spacecraft’s state. By integrating Gaussian Process (GP) surrogate models with Genetic Algorithms (GA), the method predicts spacecraft state evolution post-impulsive maneuvers, reducing computational costs while maintaining prediction accuracy. Applied to MTR missions in geostationary orbit, the GA+GP method’s results closely match real performance, with errors of 0.84% in velocity increment and 0.31% in rendezvous distance. Furthermore, the surrogate-based optimization reduces computational time to one-thousandth compared to pure GA, offering a feasible solution for online trajectory optimization.

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