Abstract
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.
Abstract (translated)
群体行为的演变引起了科学家的浓厚兴趣，并在机器人领域引起了显著的关注。传统上，群体被视为平等的，机器人共享相同的角色和能力。然而，最近的研究强调分层对于在多样场景中更有效地部署机器人集群的重要性。尽管自然倾向于分层，但机器人领域仍然坚持平等模式，部分原因是缺乏支持分层的有实验证据。我们的研究证明，尽管平等的群体在与其 collective sensing abilities相适应的环境中表现优秀，但在更大或更复杂的设置中，它们的表现并不理想。相反，分层群扩展了其感知范围，在更大、更不规则的环境中表现出色，同时有更少的资源。我们通过仿真和实体机器人实验验证了这些概念，并使用一个复杂的放射性污染任务来验证这些概念。本研究为开发适用于行星探索和自动驾驶等领域的可适应分层群系统铺平了道路。此外，这些见解还有可能加深我们对生物有机体中层次结构的理解。
URL
https://arxiv.org/abs/2405.02417
