Emergence of Collective Behaviors for the Swarm Robotics Through Visual Attention-Based Selective Interaction

Abstract

Plenty of local interaction mechanisms have been proposed to achieve collective behaviors in swarm robotics. However, these mechanisms require robots to explicitly obtain the velocity of their neighbors as the sensory input to make motion decisions. This further poses great challenges in real-world applications of swarm robotics. In this letter, inspired by the chasing behavior in large-scale migrating locusts, we propose a visual attention-based swarm model to achieve collective behaviors with positional interaction. Through numerical simulations, we find the emergence of three typical collective behaviors: flocking, milling and swarming. To gain deep insights into the new proposed model, we investigate the impact of group size and sensory blindness on the emergence of collective behaviors. Moreover, by using the mean field analysis framework, we present the theoretical proof of the emergence of flocking and milling behavior. Furthermore, to validate the feasibility of our proposed model, we reproduce the flocking and milling behavior with up to 50 physical robots. Robotic experiments demonstrate the promising ability of the new proposed model to achieve collective behaviors with the absence of velocity alignment.