Emergence of flocking behaviors transferring previously evolved alignment robot controllers

Abstract Flocking is a crucial collective behavior in swarm robotics. Reynolds introduced the boids model as a means to imitate flocking behaviors in artificial agents. This model relies on three fundamental local rules: separation, cohesion, and alignment. This paper examines the development of flocking behaviors only through the evolution of the alignment rule. Initially, we employ a genetic algorithm to develop the alignment behavior inside a group of stationary robots. The advanced alignment robot controller is a continuous-time recurrent neural network (CTRNN). Afterwards, we include the developed controller into a three-layered subsumption architecture in order to accomplish flocking behavior. Aside from the advanced alignment behavior, the architecture also incorporates a rudimentary manually designed obstacle avoidance behavior and a subroutine for moving forward. The initial experiment centers on the progression of alignment among the robots. Advanced communication techniques result in a scalable and precise alignment, where both the message content and its related context are very pertinent. The second experiment investigates the development of flocking behavior. The results indicate that the suggested subsumption architecture is capable of achieving efficient flocking behaviors. In addition, the robot swarm has the ability to navigate around barriers and continue to exhibit flocking behavior once the impediments have been bypassed. Our research indicates that the formation of a cohesive group can occur by implementing a single developed rule, complemented with well designed actions for avoiding obstacles and navigating the environment.