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On the Orbital Pursuit-Evasion Games with Low Constant Thrust-to-Mass Ratio

机译:在轨道追踪逃离游戏,具有低恒定的速度 - 质量比

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摘要

Much research has been done on the topic of pursuit-evasion games, which are of interest in diverse fields. Orbital pursuit-evasion game is more complex to some extent compared with other kinds of pursuit-evasion game due to the unique orbital dynamics and environment. Assuming a low constant thrust-to-mass ratio and that the thrust pointing direction is the only control option, this paper presents an efficient orbital pursuit-evasion model based on the relative state differential equations in LVLH (Local Vertical, Local Horizontal) coordinate system and a method to calculate the saddle equilibrium solution. Moreover, by analyzing the orbital pursuit-evasion model about the terminal condition and behavior learning framework, this paper reaches two useful and efficient conclusions, showing that the maneuver capability of the pursue spacecraft should be better than the evader spacecraft, or the role of each other will be exchanged, and the saddle equilibrium solution can't guarantee an optimal strategy in an imperfect information situation, and the behavior learning framework is an efficient way to handle that.
机译:在追求逃避游戏的主题上取得了很多研究,这对各种领域感兴趣。由于独特的轨道动态和环境,轨道追踪逃避游戏在一定程度上更加复杂,与其他类型的追求逃避游戏相比。假设低恒定的速度比且推力指向方向是唯一控制选项,本文基于LVLH(局部垂直,局部水平)坐标系中的相对状态微分方程,提出了一种有效的轨道追踪逃避模型以及计算鞍均衡解决方案的方法。此外,通过分析关于终端状况和行为学习框架的轨道追踪救济模型,本文达到了两个有用和有效的结论,表明追求宇宙飞船的机动能力应优于避难者航天器,或者每个作用其他将被交换,马鞍均衡解决方案不能保证在不完美的信息情况下最佳策略,行为学习框架是一种有效的处理方式。

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