The emerging behaviors of swarms have fascinated scientists and gathered significant interest in the field of robotics. Traditionally, swarms are viewed as egalitarian, with robots sharing identical roles and capabilities. However, recent findings highlight the importance of hierarchy for deploying robot swarms more effectively in diverse scenarios. Despite nature's preference for hierarchies, the robotics field has clung to the egalitarian model, partly due to a lack of empirical evidence for the conditions favoring hierarchies. Our research demonstrates that while egalitarian swarms excel in environments proportionate to their collective sensing abilities, they struggle in larger or more complex settings. Hierarchical swarms, conversely, extend their sensing reach efficiently, proving successful in larger, more unstructured environments with fewer resources. We validated these concepts through simulations and physical robot experiments, using a complex radiation cleanup task. This study paves the way for developing adaptable, hierarchical swarm systems applicable in areas like planetary exploration and autonomous vehicles. Moreover, these insights could deepen our understanding of hierarchical structures in biological organisms.

翻译：集群涌现行为令科学家着迷，并在机器人领域引发了广泛关注。传统上，集群被视为平等主义系统，其中机器人共享相同的角色与能力。然而，最新研究强调了层级结构对于在不同场景中更有效部署机器人集群的重要性。尽管自然界偏好层级结构，但机器人领域仍固守平等主义模型，部分原因是缺乏支持层级结构优势条件的实证证据。我们的研究表明，平等主义集群在与其集体感知能力相匹配的环境中表现优异，但在更大型或更复杂的场景中则存在困难。相反，层级结构集群能高效扩展其感知范围，在资源更少但更大型、更无结构的环境中证明其有效性。我们通过复杂辐射清理任务的仿真和物理机器人实验验证了这些概念。这项研究为开发适用于行星探索和自动驾驶汽车等领域的自适应层级集群系统铺平了道路。此外，这些见解可能加深我们对生物有机体中层级结构的理解。