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

机译:低恒推力质量比的轨道追逃游戏

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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:关于追逃游戏的研究已经很多,这在各个领域都引起了人们的兴趣。由于独特的轨道动力学和环境,与其他类型的追逃游戏相比,轨道追逃游戏在某种程度上更加复杂。假定恒定的推力质量比低,并且推力指向是唯一的控制选项,则本文基于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避模型以及计算鞍座平衡解的方法。此外,通过分析关于终端条件和行为学习框架的轨道追逃模型,得出两个有用而有效的结论,表明追击航天器的机动能力应优于逃避航天器,或两者的作用交换其他信息,鞍式均衡解决方案不能保证信息不完善情况下的最佳策略,而行为学习框架则是一种有效的解决方案。

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