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Research Article | Open Access

Trajectory Optimization Approach for Adaptive Multiple-Pass Aeroassisted Orbital Maneuver

Rui Teng1Hongwei Han1,2( )Jilin Chen1Bo Pang1,3
School of Aerospace Engineering, Beijing Institute of Technology, 100081 Beijing, China
Beijing Institute of Technology Chongqing Innovation Center, 401120 Chongqing, China
Department of Mathematics, Polytechnic University of Catalonia, Barcelona, Spain
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Abstract

Aeroassisted orbital maneuver changes the energy and orbit of a vehicle by traversing the planetary atmosphere. It is an essential method for achieving low-energy orbit transfer, and multipass aeroassisted orbital maneuver has been the most common type of practical application due to lower vehicle protection needs and safer atmospheric flight. The deorbit continuous thrust maneuver, multipass atmospheric flight, and insertion thrust maneuver are all simultaneously taken into account by the segmented hierarchical trajectory optimization method presented in this study. A unified optimization model for the deorbit and insertion maneuver optimization problems is constructed, and sequential convex optimization is used to solve them. In order to implement the autonomous design of the multipass atmospheric flight trajectory, the number of atmospheric flight passes and the path constraint boundaries are created into a nonlinear mapping relationship. To enable consistent modeling and effective optimization of the multipass aeroassisted orbital maneuver trajectories, a 2-level segmented step-by-step optimization framework is established. Additionally, the algorithm ensures the generality of any number of passes and constraint settings, which provides the algorithm’s robustness and migrability. As a result, it can be applied as a decision-making open loop of multipass aeroassisted orbital maneuver guidance.

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Space: Science & Technology
Article number: 0240

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Cite this article:
Teng R, Han H, Chen J, et al. Trajectory Optimization Approach for Adaptive Multiple-Pass Aeroassisted Orbital Maneuver. Space: Science & Technology, 2025, 5: 0240. https://doi.org/10.34133/space.0240

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Received: 17 August 2024
Revised: 15 November 2024
Accepted: 05 December 2024
Published: 14 August 2025
© 2025 Rui Teng et al. Exclusive licensee Beijing Institute of Technology Press. No claim to original U.S. Government Works.

Distributed under a Creative Commons Attribution License (CC BY 4.0).