Evolution, Self-Organisation and Swarm Robotics
Evolution, Self-Organisation and Swarm Robotics
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进化、自组织和群体机器人
DOI：
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发表时间：
2007
期刊：
影响因子：
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通讯作者：
M. Dorigo
中科院分区：
文献类型：
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作者：
V. Trianni;S. Nolfi;M. Dorigo
摘要社会性昆虫的活动通常基于自组织过程，也就是说，“一个系统的全局水平的模式仅从系统的较低水平组件之间的许多相互作用中出现的过程。(see[4]，第8页）。在一个自组织系统中，例如蚁群，既没有领导者来驱动群体的活动，也没有个体蚂蚁被告知要执行的全局食谱或蓝图。相反，每只蚂蚁都遵循简单的规则自主行动，并与其他蚂蚁进行局部互动。由于个体之间的大量相互作用，在殖民地水平上可以观察到一致的行为。类似的组织结构对于一群自主机器人来说肯定是贝内的。事实上，一个连贯的群体行为，可以获得提供每个机器人单独与简单的个人规则。此外，自组织系统所具有的特征，如分散性、灵活性和鲁棒性，对于一群自主机器人来说也是非常理想的。在设计自组织机器人系统时，必须面对的主要问题是定义导致所需集体行为的个体规则。我们对这个设计问题提出的解决方案依赖于阿尔蒂官方进化作为自组织行为合成的主要工具。在这一章中，我们提供了一个成功的应用程序的进化技术的自组织行为的一组模拟自主机器人的进化的概述。所获得的结果表明，该方法是可行的，它产生的行为是有效的，可扩展的和强大的，足以在现实中的物理机器人平台上进行测试。
Summary. The activities of social insects are often based on a self-organising process, that is, “a process in which pattern at the global level of a system emerges solely from numerous interactions among the lower-level components of the sys-tem.”(see [4], p. 8). In a self-organising system such as an ant colony, there is neither a leader that drives the activities of the group, nor the individual ants are informed of a global recipe or blueprint to be executed. On the contrary, each single ant acts autonomously following simple rules and locally interacting with the other ants. As a consequence of the numerous interactions among individuals, a coherent behaviour can be observed at the colony level. A similar organisational structure is definitely beneficial for a swarm of autonomous robots. In fact, a coherent group behaviour can be obtained providing each robot solely with simple individual rules. Moreover, the features that characterise a self-organising system—such as decentralisation, flexibility and robustness—are highly desirable also for a swarm of autonomous robots. The main problem that has to be faced in the design of a self-organising robotic system is the definition of the individual rules that lead to the desired collective behaviour. The solution we propose to this design problem relies on artificial evolution as the main tool for the synthesis of self-organising behaviours. In this chapter, we provide an overview of successful applications of evolutionary techniques to the evolution of self-organising behaviours for a group of simulated autonomous robots. The obtained results show that the methodology is viable, and that it produces behaviours that are efficient, scalable and robust enough to be tested in reality on a physical robotic platform.