A maximal-reward preliminary planning for multi-debris active removal mission in LEO with a greedy heuristic method. (August 2018)

• Record Type: Journal Article
• Authors: Yang, Jianan; Hu, Yu Hen; Liu, Yong; Pan, Quan
• Is Part Of: Acta astronautica. Volume 149(2018)
• Page Start: 123
• Page End: 142
• Publication Date: 2018-08
• Subjects: Multi-debris active removal -- Low earth orbit -- Maximal-reward optimization model -- Greedy heuristic algorithm; Astronautics -- Periodicals; Outer space -- Exploration -- Periodicals; Astronautics; Periodicals; 629.405
• DOI: 10.1016/j.actaastro.2018.05.040
• Languages: English
• ISSNs: 0094-5765

Abstract

Abstract: An active multi-debris removal mission planning algorithm for low Earth orbit (LEO) debris removal is presented. We assume that a space platform carrying multiple debris-deorbiting nano-satellites will be launched to carry out the debris-removal mission. This platform travels from one debris object to the next using a drift-orbit-transfer strategy. Each debris is assigned a reward value reflecting potential collision risk reductions if the debris is deorbited. The objective of mission planning is to determine an ordering of debris to be deorbited that maximizes the total reward due to debris removal subject to the constraints of (a) the total amount of velocity change for orbit transfer, (b) the total duration of the debris removal mission, and (c) the number of deorbiting nano-satellites carried on board the platform. In this work, a new approach is taken to approximate the response curve of drift-orbit transfers between each debris pair using a numerically interpolated continuous curve. An optimal orbit transfer strategy then is adopted to minimizes the sum of relative Δ v consumption and relative transfer duration. The task of multi-debris removal mission planning is formulated as a combinatorial tree search discrete optimization problem using this optimal orbit transfer cost estimate. A greedy heuristic with polynomial time complexity is proposed to select the next debris to deorbit that maximizes the reward-to-cost ratio. The effectiveness of this novel approach is demonstrated using real-world Iridium 33 debris cloud data. It is observed that this algorithm delivers superior performance while consuming a small fraction of computing time compared to state of the art mission planning algorithms. Highlights: Our method does not need to pre-specify the number of debris to be removed. Using reward-based performance can reflect the real benefit of debris removal. The transfer strategy simplifies the algorithm while delivering a good plan. Quality of the derived plan is demonstrated using Iridium 33 debris cloud with RCS.