Comportamento coletivo de enxames de robôs com restrições de distâncias.

Abstract

In this thesis is addressed the problem of spatially organize behaviors in the context of automatic design of software controllers for swarm robots. Two new specializations are proposed for the automatic design of software controller of each robot: modular and monolithic specialization. The purpose of these new specializations is designing the individual controller of each robot to produce the swarm collective behavior in which specific distances among robots must be maintained. Automatic design methods for software controllers in swarms of robots, previously proposed in the bibliography, do not explicitly consider these distance restrictions in the specialization of the method. The modular method modifies the robot’s low-level controller to allow linear and angular velocity control of the robot. Also, a new individual behavior is included in each robot, called formation. This behavior, designed using the artificial potential theory, allows to include distance constraints in a behavior-based controller. The formation behavior allows the adjustment of the interactions among robots to produce the collective behavior of the swarm. The monolithic method adds the same formation behavior in a neural network architecture without hidden nodes. We carry out experiments in simulation of the main automatic design methods presented in the bibliography that deal with collective tasks and distance restrictions. Also, these methods were compared with the specializations proposed in this thesis: the modular method and the monolithic method. These methods were compared in simulation and real robots in three swarm robotic tasks, using 20 e-puck robots. From the observed results, it can be concluded that the automatic design methods presented in the bibliography, as well as the monolithic method proposed in this thesis, failed to produce relevant collective behaviors and cohesion among robots. However, in the modular method, it is concluded that the introduced constraints produce cohesion and relevant collective behaviors and the performance of the designed controllers is significantly better than those obtained with methods present in the bibliography.