Swarm robotics: a review from the swarm engineering perspective <s> Approaches <s> In this paper, we study aggregation in a swarm of simple robots, called s-bots, having the capability to self-organize and self- assemble to form a robotic system, called a swarm-bot. The aggregation process, observed in many biological systems, is of fundamental impor- tance since it is the prerequisite for other forms of cooperation that in- volve self-organization and self-assembling. We consider the problem of defining the control system for the swarm-bot using artificial evolution. The results obtained in a simulated 3D environment are presented and analyzed. They show that artificial evolution, exploiting the complex in- teractions among s-bots and between s-bots and the environment, is able to produce simple but general solutions to the aggregation problem. <s> BIB001 </s> Swarm robotics: a review from the swarm engineering perspective <s> Approaches <s> In group-living animals, aggregation favours interactions and information exchanges between individuals, and thus allows the emergence of complex collective behaviors. In previous works, a model of a self-enhanced aggregation was deduced from experiments with the cockroach Blattella germanica. In the present work, this model was implemented in micro-robots Alice and successfully reproduced the agregation dynamics observed in a group of cockroaches. We showed that this aggregation process, based on a small set of simple behavioral rules of interaction, can be used by the group of robots to select collectively an aggregation site among two identical or different shelters. Moreover, we showed that the aggregation mechanism allows the robots as a group to “estimate” the size of each shelter during the collective decision-making process, a capacity which is not explicitly coded at the individual level. <s> BIB002
In swarm robotics, aggregation is usually approached in two ways: probabilistic finite state machines (PFSMs) or artificial evolution. The most common approach is based on PFSMs: the robots explore an environment and, when they find other robots, they decide stochastically whether to join or leave the aggregate. In this approach, a stochastic component is often used in order to ensure that eventually only a single aggregate is formed. In the artificial evolution approach, the parameters of a neural network are automatically selected in order to obtain an aggregation behavior. Results BIB002 developed a system in which robots are used to replicate the behavior observed in cockroaches by  . The robots are able to collectively aggregate in a circular arena using a PFSM approach. Another example of an aggregation behavior based on a PFSM was developed by Soysal andŞahin (2005 Soysal andŞahin ( , 2007 ) (see Fig. 4(a) ). In their work, a robot can be in one of three states: the repel state, in which the robot tends to get away from other robots; the approach state, in which the robot tends to get closer to other robots; and the wait state, in which the robot stand still. Soysal andŞahin were able to achieve both moving and static aggregation behaviors by changing the parameters of the system. An example of aggregation obtained with artificial evolution was developed by BIB001 (see Fig. 4(b) ). The authors obtained two sets of parameters for a neural network achieving both moving and static aggregates. presented some rules of thumb for obtaining aggregation behaviors through artificial evolution. Moreover, they proposed a comparison between the probabilistic finite state machine approach by Soysal andŞahin (2005) and the artificial evolution approach by Bahçeci andŞahin (2005) .