Collective Random Walks of Flocking Agents Through Emergent Implicit Leadership

This paper presents a novel method to achieve collective exploration with a mobile robot swarm. The method enables collective random walks: Swarms of simple individuals using only local interactions flock together while navigating in a random direction. In this paper, we introduce swarm leaders that emerge through a mechanism based on local sensing. Upon emerging, leaders combine social interactions with a goal direction, that is generated using Lévy or Correlated Random Walks processes. Key findings highlight that the aggregated swarm behaviours are random-walk-like, and tend towards Lévy-like dynamics increasing swarm size, irrespective of the emergent leader process. Furthermore, we explore how leader distribution affects the trajectory of the swarm, revealing the potential for optimized movement strategies through the manipulation of leadership roles. The introduction of emergent implicit leadership offers a novel perspective on achieving complex navigational behaviors in swarm robotics, presenting significant implications for applications requiring adaptive, decentralized exploration strategies.