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Self-organised aggregation is a well studied behaviour in swarm robotics as it is the pre-condition for the development of more advanced group-level responses. In this paper, we investigate the design of decentralised algorithms for a swarm of heterogeneous robots that self-aggregate over distinct target sites. A previous study has shown that including as part of the swarm a number of informed robots can steer the dynamic of the aggregation process to a desirable distribution of the swarm between the available aggregation sites. We have replicated the results of the previous study using a simplified approach, we removed constraints related to the communication protocol of the robots and simplified the control mechanisms regulating the transitions between states of the probabilistic controller. The results show that the performances obtained with the previous, more complex, controller can be replicated with our simplified approach which offers clear advantages in terms of portability to the physical robots and in terms of flexibility. That is, our simplified approach can generate self-organised aggregation responses in a larger set of operating conditions than what can be achieved with the complex controller.

Self-organised aggregation is one of the basic collective behaviours studied in swarm robotics. In this paper, we investigate an aggregation problem occurring on two different sites. Previous studies have shown that a minority of robots, informed about the site on which they have to aggregate, can control the final distribution of the entire robot swarm on the sites. We reproduce this strategy by adapting the previous probabilistic finite-state machine to a new simulated robotic platform: the Kilobot. Our simulation results highlight that the update time (i.e., the amount of time a robot waits before making a decision on leaving a site) impacts the dynamics of the aggregation process. Namely, a longer update time lowers the number of robots wandering in the arena, but can slow down the dynamics when the target final distribution is far from the one initially formed. To ensure a low number of wandering robots while maintaining a quick convergence towards the target final distribution of the swarm, we introduce the concept of a dynamic update time increasing during the aggregation process.

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