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Multi-objective Optimization of Multi-level Models for Controlling Animal Collective Behavior with Robots

机译:机器人控制动物集体行为的多层次模型的多目标优化

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Group-living animals often exhibit complex collective behaviors that emerge through the non-linear dynamics of social interactions between individuals. Previous studies have shown that it is possible to influence the collective decision-making process of groups of insects by integrating them with autonomous multi-robot systems. However, generating robot controller models for this particular task can be challenging. The main difficulties lie in accommodating group collective dynamics (macroscopic level) and agent-based models implemented in every individual robot (microscopic level). In this study, we show how such systems can be appropriately modeled, and how to use them to modulate the collective decision-making of cockroaches in a shelter-selection problem. We address two questions in this paper: first, how to optimize a microscopic model of cockroach behavior to exhibit the same collective behavior as a macroscopic model from the literature, and second, how to optimize the model describing robot behavior to modulate the collective behavior of the group of cockroaches.
机译:群居动物通常表现出复杂的集体行为,这些行为是通过个体之间社交互动的非线性动力而出现的。先前的研究表明,通过将昆虫与自主的多机器人系统集成在一起,可以影响昆虫群体的集体决策过程。但是,为该特定任务生成机器人控制器模型可能具有挑战性。主要困难在于适应团体集体动力学(宏观层面)和在每个机器人中实现的基于代理的模型(微观层面)。在这项研究中,我们展示了如何对此类系统进行适当的建模,以及如何使用它们来在避难所选择问题中调节蟑螂的集体决策。我们在本文中解决了两个问题:首先,如何优化蟑螂行为的微观模型以表现出与文献中的宏观模型相同的集体行为,其次,如何优化描述机器人行为的模型来调节蟑螂的集体行为一群蟑螂。

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