Beeclust: A Swarm Algorithm Derived from Honeybees Derivation of the Algorithm, Analysis by Mathematical Models and Implementation on a Robot Swarm
Beeclust: A Swarm Algorithm Derived from Honeybees Derivation of the Algorithm, Analysis by Mathematical Models and Implementation on a Robot Swarm
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Beeclust：一种源自蜜蜂的群体算法 算法推导、数学模型分析及在机器人群体上的实现
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发表时间：
2011
期刊：
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通讯作者：
Heiko Hamann
中科院分区：
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作者：
T. Schmickl;Heiko Hamann
通过对蜜蜂群体行为的观察，我们给出了一个强大而简单，以及健壮和灵活的群体机器人系统算法。我们展示了如何将自然系统中的这种观察转化为在小型自主机器人的传感器-参与者世界中工作的行为(算法)的抽象表示。通过建立几个不同复杂性的数学模型，研究了群体系统的全局特征。这些模型支持我们解释观察到的群体行为的最终原因，并允许我们预测群体在新的环境条件下的行为。反过来，这些预测为自然系统(蜜蜂和其他群居昆虫)以及机器群建立新的实验设置提供了灵感。这样，就可以更深入地理解在蜜蜂和机器人中发生的集体算法的复杂属性。
We demonstrate the derivation of a powerful and simple, as well as robust and flexible algorithm for a swarm robotic system derived from observations of honeybees’ collective behavior. We show how such observations made in a natural system can be translated into an abstract representation of behavior (algorithm) working in the sensor-actor world of small autonomous robots. By developing several mathematical models of varying complexity, the global features of the swarm system are investigated. These models support us in interpreting the ultimate reasons of the observed collective swarm behavior and they allow us to predict the swarm’s behavior in novel environmental conditions. In turn these predictions serve as inspiration for new experimental setups with both, the natural system (honeybees and other social insects) as well as the robotic swarm. This way, a deeper understanding of the complex properties of the collective algorithm, taking place in the bees and in the robots, is achieved.