Swarm fragmentation, the breakdown of communication and coordination among robots, can critically compromise a swarm's mission. Integrating Augmented Reality support into swarm monitoring-especially through co-located visualisations anchored directly on the robots- may enable human operators to detect early signs of fragmentation and intervene effectively. In this work, we propose three localised visual cues-targeting robot connectivity, dominant decision influences, and movement direction-to make explicit the underlying Perception-Decision-Action (PDA) loop of each robot. Through an immersive Virtual Reality user study, 51 participants were tasked with both anticipating potential fragmentation and selecting the appropriate control to prevent it, while observing swarms exhibiting expansion, densification, flocking, and swarming behaviours. Our results reveal that a visualisation emphasising inter-robot connectivity significantly improves anticipation of fragmentation, though none of the cues consistently enhance control selection over a baseline condition. These findings underscore the potential of co-located AR-enhanced visual feedback to support human-swarm interaction and inform the design of future AR-based supervisory systems for robot swarms. A free copy of this paper and all supplemental materials are available at https://osf.io/49gny.

In the context of robot swarms, fragmentation refers to a breakdown in communication and coordination among the robots. This fragmentation can lead to issues in the swarm self-organisation, especially the loss of efficiency or an inability to perform their tasks. Human operators influencing the swarm could prevent fragmentation. To help them in this task, it is necessary to study the ability of humans to perceive and anticipate fragmentation. This article studies the perception of different types of fragmentation occurring in swarms depending on their behaviour selected amongst swarming, flocking, expansion and densification. Thus, we characterise human perception thanks to two metrics based on the distance separating fragmented groups and the separation speed. The experimentation protocol consists of a binary discrimination task in which participants have to assess the presence of fragmentation. The results show that detecting fragmentation for expansion behaviour and anticipating fragmentation, in general, are challenging. Moreover, they show that humans rely on separation distance and speed to infer the presence or absence of fragmentation. Our study paves the way for new research that will provide information to humans to better anticipate and efficiently prevent the occurrence of swarm fragmentation.