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：集体智能与机器人群体的自主性可通过使个体机器人意识到自身是更大整体的组成部分及其角色来提升。本研究提出一种算法，使由易出错的简单机器人组成的群体能够实现位置自感知。这些机器人仅能本地广播消息并估算与邻接机器人的距离。尽管无法测量传入消息的方位角，运行本算法的机器人可在部署于规则编队中的群体内计算自身位置。我们通过多达200个Kilobot机器人的实验证明：此类位置自感知可用于创建共享坐标系统，并动态自分配位置相关任务。本方案相比现有最优算法需求更少，并包含集体噪声过滤机制，因此可应用于更广泛的机器人平台。所有机器人可互换、运行相同代码且无需任何先验知识。通过本算法，机器人可达成集体同步，并自主感知群体的空间构型及其在群体中的位置。