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Multiple Space Debris Collecting Mission: Optimal Mission Planning

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  • Max Cerf

    (AIRBUS Defence and Space)

Abstract

Successive missions must be planned to clean the near-Earth space from the heaviest debris. The problem mixes combinatorial optimization to select and order the debris, and continuous optimization to define the orbital maneuvers. In order to reduce the costs all missions have to be achieved by identical expendable vehicles with a minimum fuel requirement. The solution method proposed consists in three stages. Firstly the orbital transfer problem is solved for all pairs of debris and for discretized dates, considering a generic transfer strategy suited either to a high-thrust or to a low-thrust vehicle. The results are stored in cost matrices defining a response surface. Secondly a simulated annealing algorithm is applied to find the optimal mission planning. The cost is assessed by interpolation on the response surface. The convergence is quite fast, yielding an optimal mission planning. Thirdly the successive missions are re-optimized in terms of transfer maneuvers without changing the debris order. These continuous problems yield a refined performance requirement for designing the removal vehicle. The solution method is illustrated on a representative application case.

Suggested Citation

  • Max Cerf, 2015. "Multiple Space Debris Collecting Mission: Optimal Mission Planning," Journal of Optimization Theory and Applications, Springer, vol. 167(1), pages 195-218, October.
    • Handle: RePEc:spr:joptap:v:167:y:2015:i:1:d:10.1007_s10957-015-0705-0
    DOI: 10.1007/s10957-015-0705-0
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    References listed on IDEAS

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    1. M. Cerf, 2013. "Multiple Space Debris Collecting Mission—Debris Selection and Trajectory Optimization," Journal of Optimization Theory and Applications, Springer, vol. 156(3), pages 761-796, March.
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    Cited by:

    1. Anyi Huang & Shenggang Wu, 2022. "Neural Network-Based Approximation Model for Perturbed Orbit Rendezvous," Mathematics, MDPI, vol. 10(14), pages 1-11, July.

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