Active Debris Removal Mission Planning Method Based on Machine Learning

Yingjie Xu, Xiaolu Liu (), Renjie He, Yuehe Zhu, Yahui Zuo and Lei He
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Yingjie Xu: College of System Engineering, National University of Defense Technology, Changsha 410073, China
Xiaolu Liu: College of System Engineering, National University of Defense Technology, Changsha 410073, China
Renjie He: College of System Engineering, National University of Defense Technology, Changsha 410073, China
Yuehe Zhu: College of Aerospace Science and Engineering, National University of Defense Technology, Changsha 410073, China
Yahui Zuo: College of System Engineering, National University of Defense Technology, Changsha 410073, China
Lei He: College of System Engineering, National University of Defense Technology, Changsha 410073, China

Mathematics, 2023, vol. 11, issue 6, 1-24

Abstract: To prevent the proliferation of space debris and stabilize the space environment, active debris removal (ADR) has increasingly gained public concern. Considering the complexity of space operations and the viability of ADR missions, it would be necessary to schedule the ADR process in order to remove as much debris as possible. This paper presents an active debris removal mission planning problem, devoted to generate an optimal debris removal plan to guide the mission process. According to the problem characteristics, a two-layer time-dependent traveling salesman problem(TSP) mathematical model is established, involving the debris removal sequence planning and the transfer trajectory planning. Subsequently, two main novel methods based on machine learning are proposed for the ADR mission planning problem, including a deep neural networks(DNN)-based estimation method for approximating the optimal velocity increments of perturbed multiple-impulse rendezvous and an reinforcement learning(RL)-based method for optimizing the sequence of debris removal and rendezvous time. Experimental results of different simulation scenarios have verified the effectiveness and superiority of the proposed method, indicating the good performance for solving the active debris removal mission planning problem.

Keywords: active space debris removal; mission planning; time-dependent TSP; transfer estimation; sequence planning; deep neural networks; pointer network; reinforcement learning (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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