Global trajectory optimization of multi-spacecraft successive rendezvous using multi-tree search

This paper investigates the global optimization of multi-spacecraft successive rendezvous trajectories, which is divided here into three subproblems: target assignment, sequence optimization, and rendezvous time optimization. A method consisting of two novel algorithms is proposed to solve these subproblems. First, a multi-tree search framework is developed to assign multiple targets to each spacecraft and simultaneously optimize the rendezvous sequence for every single spacecraft. Specifically, a novel algorithm of local search combined with beam search is proposed. Second, this paper converts the rendezvous time optimization problem into a multi-stage decision problem. Based on a critical rendezvous-epoch-dependent characteristic found in this subproblem, the number of state variables is thereby reduced. A novel dual dynamic programming algorithm is proposed and combined with dynamic programming to solve for the globally optimal rendezvous epochs efficiently. The global optimality is guaranteed by Bellman’s principle of optimality, which is the first time in such problem to our knowledge. The proposed method achieves state-of-the-art performance in several typical fuel-optimal scenarios of active debris removal. This open-sourced method is non-database-dependent and contains only one design stage, which is expected to be adopted in other successive rendezvous missions.

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Recommended citation: Zhong Zhang, Nan Zhang, Zherui Chen, Fanghua Jiang, and Junfeng Li. “Global trajectory optimization of multi-spacecraft successive rendezvous using multi-tree search.” Journal of Guidance, Control, and Dynamics, 2023. (Accepted)