Adaptive role assignment for self-organized flocking of a real robotic swarm

Self-organized flocking of robotic swarms has been investigated for approximately 20 years. Most studies are based on a computer animation model named Boid. This model reproduces flocking motion by three simple behavioral rules: collision avoidance, velocity matching, and flock centering. However, flocking performance depends on how these rules are configured and no guideline for the configuration exists. This paper investigates real robot flocking where individuals can switch their roles depending on the situations. Robots can move as leaders or followers, and the roles are dynamically allocated using stochastic learning automata. The flocking performance is evaluated, and swarming behavior is analyzed in a scenario where robots consecutively travel between two landmarks. © 2016, ISAROB.