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Simulated and Real-World Evolution of Predator Robots

机译:捕食者机器人的模拟和真实世界演变

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This paper addresses the problem of designing behavioural strategies for a group of robots with a specific task, capturing another robot. Our proposed approach is to employ a "smart" prey with a pre-programmed strategy based on a novel Gaussian model of danger zones and use an evolutionary algorithm (EA) to optimize the predators’ behavior. The EA is applied in two stages: first in simulation, then in hardware on the real robots. The best evolved robot controllers are then further inspected and compared by their robustness, i.e., performance under different conditions. The results show that our approach is successful, combining simulations, real-world evolution, and robustness analysis it is possible to develop good solutions for the predator-prey problem.
机译:本文解决了为一组具有特定任务的机器人设计行为策略并捕获另一个机器人的问题。我们提出的方法是将“智能”猎物与基于新型危险区高斯模型的预编程策略结合使用,并使用进化算法(EA)来优化捕食者的行为。 EA分两个阶段应用:首先是仿真,然后是真实机器人的硬件。然后进一步检查发展最快的机器人控制器,并通过其鲁棒性(即在不同条件下的性能)进行比较。结果表明,我们的方法是成功的,结合了模拟,真实世界的演化和鲁棒性分析,有可能为捕食者-猎物问题开发良好的解决方案。

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