Controlling Robot Swarm Aggregation through a Minority of Informed Robots

Antoine Sion, Andreagiovanni Reina, Mauro Birattari, Elio Tuci

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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.
Original languageEnglish
Title of host publicationANTS 2022: 13th International Conference on Swarm Intelligence
Number of pages14
Publication statusAccepted/In press - 2022


  • swarm robotics
  • self-organised aggregation
  • heterogeneous swarm
  • finite-state machine


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