Rapid trajectory optimization for multiple entry vehicles based on decoupled sequential convex programming

Abstract This paper proposes a decoupled sequential convex programming (SCP) method for the time-coordinated trajectory optimization of multiple hypersonic entry vehicles (HEVs). The proposed method introduces the reference trajectory to decouple the collision avoidance constraints, allowing for independent optimization of each vehicle while maintaining overall system safety. Additionally, a lower bound on time coordination is employed to ensure that the vehicles reach their destinations simultaneously. These approaches break down the complex multi-vehicle optimization problem into simpler subproblems, thereby effectively reducing both the number of variables and the associated constraints. Numerical simulations confirm the reliability and convergence of the proposed method, demonstrating significant improvement in computational efficiency.

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Publication Details

Journal
Journal of Physics Conference Series
Published
2025-03-01
DOI
https://doi.org/10.1088/1742-6596/2977/1/012085
Citations
1
Primary Topic
Robotic Path Planning Algorithms
Type
article
Field-Weighted Citation Impact
1.28
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article

Rapid trajectory optimization for multiple entry vehicles based on decoupled sequential convex programming

Binfeng Pan, Qi Chen, Chao Ou
1 citations
Journal of Physics Conference Series
Robotic Path Planning Algorithms
1.28
article

Rapid trajectory optimization for multiple entry vehicles based on decoupled sequential convex programming

Binfeng Pan, Qi Chen, Chao Ou
article en
1 citations

Abstract

Abstract This paper proposes a decoupled sequential convex programming (SCP) method for the time-coordinated trajectory optimization of multiple hypersonic entry vehicles (HEVs). The proposed method introduces the reference trajectory to decouple the collision avoidance constraints, allowing for independent optimization of each vehicle while maintaining overall system safety. Additionally, a lower bound on time coordination is employed to ensure that the vehicles reach their destinations simultaneously. These approaches break down the complex multi-vehicle optimization problem into simpler subproblems, thereby effectively reducing both the number of variables and the associated constraints. Numerical simulations confirm the reliability and convergence of the proposed method, demonstrating significant improvement in computational efficiency.

Journal of Physics Conference SeriesVol. 2977(1)
Northwestern Polytechnical University (CN), China Aerodynamics Research and Development Center (CN), Huazhong University of Science and Technology (CN)
Openalex Percentile: Top 22%
Robotic Path Planning Algorithms
1.28
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