  This paper introduces a novel bio-mimetic approach for distributed control of
robotic swarms, inspired by the collective behaviors of swarms in nature such
as schools of fish and flocks of birds. The agents are assumed to have limited
sensory perception, lack memory, be Identical, anonymous, and operate without
interagent explicit communication. Despite these limitations, we demonstrate
that collaborative exploration and task allocation can be executed by applying
simple local rules of interactions between the agents. A comprehensive model
comprised of agent, formation, and swarm layers is proposed in this paper,
where each layer performs a specific function in shaping the swarm's collective
behavior, thereby contributing to the emergence of the anticipated behaviors.
We consider four principles combined in the design of the distributed control
process: Cohesiveness, Flexibility, Attraction-Repulsion, and Peristaltic
Motion. We design the control algorithms as reactive behaviour that enables the
swarm to maintain connectivity, adapt to dynamic environments, spread out and
cover a region with a size determined by the number of agents, and respond to
various local task requirements. We explore some simple broadcast control-based
steering methods, that result in inducing "anonymous ad-hoc leaders" among the
agents, capable of guiding the swarm towards yet unexplored regions with
further tasks. Our analysis is complemented by simulations, validating the
efficacy of our algorithms. The experiments with various scenarios showcase the
swarm`s capability to self-organize and perform tasks effectively under the
proposed framework. The possible implementations include domains that
necessitate emergent coordination and control in multi-agent systems, without
the need for advanced individual abilities or direct communication.
