Abstract
This paper presents a novel and robust two-stage pursuit strategy for the incomplete-information impulsive space pursuit-evasion missions considering the J2 perturbation. The strategy firstly models the impulsive pursuit-evasion game problem into a far-distance rendezvous stage and a close-distance game stage according to the perception range of the evader. For the far-distance rendezvous stage, it is transformed into a rendezvous trajectory optimization problem and a new objective function is proposed to obtain the pursuit trajectory with the optimal terminal pursuit capability. For the close-distance game stage, a closed-loop pursuit approach is proposed using one of the reinforcement learning algorithms, i.e., the deep deterministic policy gradient algorithm, to solve and update the pursuit trajectory for the incomplete-information impulsive pursuit-evasion missions. The feasibility of this novel strategy and its robustness to different initial states of the pursuer and evader and to the evasion strategies are demonstrated for the sun-synchronous orbit pursuit-evasion game scenarios. The results of the Monte Carlo tests show that the successful pursuit ratio of the proposed method is over 91% for all the given scenarios.
Highlights
The space pursuit-evasion (PE) game is a typical zero-sum game [1,2], where the goals of both confrontation sides are completely opposite and irreconcilable
Wang [27] developed the improved branching deep Q networks and the fuzzy actor-critic learning algorithm, respectively. These previous researches usually restricted the initial distance between the two spacecraft to reduce the PE game duration and used a simplified dynamical model to improve the computational efficiency. To remove this limitation and Aerospace 2021, 8, 299 consider realistic space PE game problems, in this paper, a novel two-stage pursuit strategy is developed to find a robust solution for incomplete-information impulsive space pursuitevasion missions considering J2 perturbation
The required velocity increment for the close-distance PE game is the minimum if the evader does not perform any evasive maneuvers, which is equal to the sum of the Δv that were planned in far-distance rendezvous stage (FRS) but not executed increment is large
Summary
The space pursuit-evasion (PE) game is a typical zero-sum game [1,2], where the goals of both confrontation sides are completely opposite and irreconcilable. It is challenging to develop a feedback closed-loop control method with high efficiency for the impulsive space PE game missions considering the perturbations of the dynamics. Wang [27] developed the improved branching deep Q networks and the fuzzy actor-critic learning algorithm, respectively These previous researches usually restricted the initial distance between the two spacecraft to reduce the PE game duration and used a simplified dynamical model to improve the computational efficiency. To remove this limitation and Aerospace 2021, 8, 299 consider realistic space PE game problems, in this paper, a novel two-stage pursuit strategy is developed to find a robust solution for incomplete-information impulsive space pursuitevasion missions considering J2 perturbation. The proposed method is applied to the scenarios of spacecraft games in the sun-synchronous orbit, which demonstrates outstanding advantages in robustness to various initial states of the pursuer and the evader and to the different evasion strategies
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