Collective intelligence and autonomy of robot swarms can be improved by enabling the individual robots to become aware they are the constituent units of a larger whole and what is their role. In this study, we present an algorithm to enable positional self-awareness in a swarm of minimalistic error-prone robots which can only locally broadcast messages and estimate the distance from their neighbours. Despite being unable to measure the bearing of incoming messages, the robots running our algorithm can calculate their position within a swarm deployed in a regular formation. We show through experiments with up to 200 Kilobot robots that such positional self-awareness can be employed by the robots to create a shared coordinate system and dynamically self-assign location-dependent tasks. Our solution has fewer requirements than state-of-the-art algorithms and contains collective noise-filtering mechanisms. Therefore, it has an extended range of robotic platforms on which it can run. All robots are interchangeable, run the same code, and do not need any prior knowledge. Through our algorithm, robots reach collective synchronisation, and can autonomously become self-aware of the swarm's spatial configuration and their position within it.
翻译:机器人群的集体智能和自主性可以通过使个体机器人能够意识到它们是一个大整体的组成单位和他们的作用来提高机器人群群的集体智能和自主性。 在这项研究中,我们展示了一种算法,使在微小易出错的机器人群中能够进行定位自我意识,这种机器人群中只能用本地广播信息并估计与邻居的距离。尽管无法测量收到的信息的承载,运行我们的算法的机器人可以计算出其在正常结构中部署的群落中的位置。我们通过与多达200个Kilobot机器人的实验显示,机器人可以使用这种定位自我意识来创建一个共享的坐标系统,并动态地自标自标位置依赖任务。我们的解决方案要求比最先进的算法要少,并且包含集体的噪音过滤机制。 因此,它可以运行的机器人平台范围很广。 所有机器人都是可互换的,运行相同的代码,并且不需要任何先前的知识。 通过我们的算法,机器人可以实现集体同步,并且可以自主地成为其空间配置和空间位置的自我觉悟。</s>