ACTA AERONAUTICAET ASTRONAUTICA SINICA >
Space target rendezvous sequence planning via pointer networks
Received date: 2023-04-11
Revised date: 2023-04-22
Accepted date: 2023-05-06
Online published: 2023-05-12
Supported by
National Natural Science Foundation of China(12125207)
Traversal rendezvous mission planning of multiple space targets for a single spacecraft is a mixed-integer programming problem with high complexity， which involves the combinatorial optimization of the top-level rendezvous sequence and the continuous optimization of the base-level flight trajectories. Existing methods that integrally optimize all discrete and continuous variables are inefficient and difficult to achieve the optimum. We propose a learning-based method that can efficiently obtain the near-optimal sequence mainly using the pointer networks. First， the neural network model for multiple-space-target traversal rendezvous planning is constructed as the decision agent of sequencing. Second， an unsupervised learning method based on the asynchronous advantage actor-critic algorithm is proposed to avoid the expensive computational cost in obtaining training labels. Finally， an estimation method to rapidly approximate the actual transfer cost is embedded in the training process to improve the efficiency of calculating rewards. Case studies show that the proposed training method performs efficiently， and the well-trained agent can rapidly predict the optimal sequence with a probability more than 88.7%.
Jiacheng ZHANG , Yuehe ZHU , Yazhong LUO . Space target rendezvous sequence planning via pointer networks[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2023 , 44(15) : 528698 -528698 . DOI: 10.7527/S1000-6893.2023.28698
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