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.

翻译：本文提出一种新颖的生物启发式方法，用于机器人集群的分布式控制，其灵感来源于自然界中鱼群和鸟群等集群的集体行为。假设智能体具有有限的感知能力、无记忆性、同质匿名性，且无需显式通信。尽管存在这些限制，我们证明了通过应用智能体间简单的局部交互规则，即可实现协作探索与任务分配。本文提出了一个包含智能体层、编队层和集群层的综合模型，每层在塑造集群集体行为中执行特定功能，从而促进预期行为的涌现。我们在分布式控制过程设计中融合了四项原则：内聚性、柔性、吸引-排斥效应与蠕动运动。所设计的控制算法具有反应式行为特性，使集群能够保持连接性、适应动态环境、根据智能体数量扩展覆盖区域，并响应各类局部任务需求。我们探索了几种基于广播控制的简单引导方法，可在智能体中诱导产生"匿名临时领导者"，引导集群向存在待执行任务的新区域移动。通过仿真实验验证了算法的有效性，多种场景下的实验表明集群在所提框架下能够实现自组织并高效执行任务。该方法可应用于多智能体系统中需要涌现式协调与控制、且无需高级个体能力或直接通信的领域。