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Parametric Analysis for Modeling and Simulation of Stochastic Behavior in the Predator-Prey Pursuit Domain

机译:捕食者-食饵追踪域随机行为建模与仿真的参数分析。

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摘要

Modeling and simulation are very useful tools when analyzing complex systems. Multipredator, multi-prey pursuit-evasion games, with stochastic vision, constitute an example of such a complex system. We present a careful parametric analysis for the multipredator-multiprey domain. Previous work on the predator-prey domain has mainly been focused on the strategies to intercept all prey, leaving out the analysis about how their parameters, which describe the capabilities of the predator-prey pursuit, would affect the time to intercept all prey. In this paper we use a fixed strategy proven to be effective in previous studies. In most predator-prey studies, most of the capabilities and parameters that describe the pursuit have been fixed (e.g. the number of predators and prey in the arena, their velocities and detection zones, among others). Assuming that some of the capabilities and parameters describing the prey are given, and that the parameters and capabilities of the predators could be designed, many questions arise. How would the different capabilities of the mobile robotic predator affect the probability to intercept all prey? How many mobile robotic predators would be required to guarantee a probability of intercept within a finite, and perhaps tactically useful, time period within a given region? It is proposed that stochastic modeling of predator-prey scenarios lend insight into such problems. A probabilistic approach to the predator-prey domain is thus shown. Parametric analysis for the pursuit-evasion game, Monte Carlo simulations and a significance test are presented. In the following simulations, the prey and predators have only local information provided by their detection and observance zones. These zones are modeled mathematically via a probabilistic model, and each predator and prey in the scenario has a probability of being detected based on distance. As a prey is geometrically closer to a predator, its probability of intercept increases.
机译:在分析复杂系统时,建模和仿真是非常有用的工具。具有随机视野的多捕食者,多猎物逃避游戏构成了这种复杂系统的一个例子。我们为多重捕食者-多重猎物域提出了仔细的参数分析。先前在捕食者-被捕食域上的工作主要集中在拦截所有猎物的策略上,而没有分析它们的参数如何描述捕食者-被捕食者的能力如何影响拦截所有猎物的时间。在本文中,我们使用一种在先前的研究中证明有效的固定策略。在大多数捕食者-猎物研究中,描述追踪的大多数能力和参数都是固定的(例如,竞技场中的捕食者和猎物的数量,它们的速度和检测区域等)。假设已经给出了描述猎物的一些能力和参数,并且可以设计出捕食者的参数和能力,那么就会出现许多问题。移动机器人捕食者的不同能力将如何影响拦截所有猎物的概率?在给定区域内的有限时间内,也许在战术上有用的时间内,需要多少移动机器人捕食者来保证截获概率?有人提出,捕食者—猎物场景的随机建模有助于深入了解此类问题。因此显示了一种针对捕食者-被捕食域的概率方法。提出了追逃游戏的参数分析,蒙特卡洛模拟和重要性检验。在下面的模拟中,被捕食者和被捕食者仅具有由其检测区和观察区提供的本地信息。这些区域是通过概率模型进行数学建模的,场景中的每个捕食者和被捕食者都有基于距离被检测到的可能性。由于猎物在几何上更接近捕食者,因此其被拦截的可能性增加。

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