Controlling Robot Swarm Aggregation through a Minority of Informed Robots

Abstract

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.

Robot Swarms Break Decision Deadlocks in Collective Perception Through Cross-Inhibition

“…For example, honeybees make consensus decisions on the site where to build their nest among several alternative locations [26], ants are able to collectively select the shortest path from their nest to a profitable food source [10], and flocks of birds on the move select the same direction of motion in a decentralised way [3]. These natural systems have inspired the development of many different types of algorithms to enable robot swarms to make consensus decisions, such as selecting the aggregation site [27], selecting the direction of motion [7,17,9], selecting the predominant environmental feature [33], or selecting the shortest path for transporting items efficiently [29]. These algorithms need to be simpleto run on simple robots-and, at the same time, robust to robot malfunctions and flexible to changing environments-to work in real-life applications.…”