Thèse de doctorat
Résumé : In this thesis, we investigate the problem of path formation and prey retrieval in a swarm of robots. We present two swarm intelligence control mechanisms used for distributed robot path formation. In the first, the robots form linear chains. We study three variants of robot chains, which vary in the degree of motion allowed

to the chain structure. The second mechanism is called vectorfield. In this case,

the robots form a pattern that globally indicates the direction towards a goal or

home location. Both algorithms were designed following the swarm robotics control

principles: simplicity of control, locality of sensing and communication, homogeneity

and distributedness.

We test each controller on a task that consists in forming a path between two

objects—the prey and the nest—and to retrieve the prey to the nest. The difficulty

of the task is given by four constraints. First, the prey requires concurrent, physical

handling by multiple robots to be moved. Second, each robot’s perceptual range

is small when compared to the distance between the nest and the prey; moreover,

perception is unreliable. Third, no robot has any explicit knowledge about the

environment beyond its perceptual range. Fourth, communication among robots is

unreliable and limited to a small set of simple signals that are locally broadcast.

In simulation experiments we test our controllers under a wide range of conditions,

changing the distance between nest and prey, varying the number of robots

used, and introducing different obstacle configurations in the environment. Furthermore,

we tested the controllers for robustness by adding noise to the different sensors,

and for fault tolerance by completely removing a sensor or actuator. We validate the

chain controller in experiments with up to twelve physical robots. We believe that

these experiments are among the most sophisticated examples of self-organisation

in robotics to date.