Chapter 74 Self-organized Flocking with Conflicting Goal Directions

In flocking, a large number of individuals move cohesively in a common direction. Many examples can be found in nature: from simple organisms such as crickets and locusts to more complex ones such as birds, fish and quadrupeds. Reynolds was the first to propose a computational model of flocking [1]. The be-havior of each individual is made of three parts: separation, cohesion, alignment. Separation means that the individual moves away from its neighbors. Cohesion means that the individual stays close to its neighbors. Alignment means that the individual matches the velocity of its neighbors. This paper studies flocking in the robotics setting. One of the earliest attempt to realize flocking in robotics was done by Matariç [2]. She created a set of “basic behaviors”: safe-wandering, aggregation, dispersion and homing. Turgut et al. [3] implemented flocking on real robots using two behaviors: proximal control and alignment control. Proximal control combines the separation and cohesion compo-nents and was realized using the framework of artificial physics as done by Spears et al. [4]. Alignment control is realized through a novel sensing system which the