In this paper, we present a novel swarm algorithm, swarm synergy, designed for robots to form communities within a swarm autonomously and anonymously. These communities, characterized as clusters of robots, emerge without any (pre-defined or communicated) specific locations. Each robot operates as a silent agent, having no communication capability, making independent decisions based on local parameters. The proposed algorithm allows silent robots to achieve this self-organized swarm behavior using only sensory inputs from the environment. The robots intend to form a community by sensing the neighbors, creating synergy in a bounded environment. We further infer the behavior of swarm synergy to ensure the anonymity/untraceability of both robots and communities and show the results on dynamicity of various parameters relevant to swarm communities such as community size, community location, number of community, no specific agent structure in the community, etc. The results are further analysed to observe the effect of sensing limitations posed by the onboard sensor's field of view. Simulations and experiments are performed to showcase the algorithm's scalability, robustness, and fast convergence. Compared to the state-of-art with similar objectives, the proposed communication-free swarm synergy shows comparative time to synergize or form communities. The proposed algorithm finds applications in studying crowd dynamics under high-stress scenarios such as fire, attacks, or disasters.

翻译：本文提出了一种新颖的群智能算法——群协同，旨在使机器人在群内自主且匿名地形成社区。这些社区表现为机器人集群，且无需任何（预设或通信约定的）特定位置即可涌现。每个机器人作为静默智能体运行，不具备通信能力，仅基于局部参数做出独立决策。所提算法仅通过环境感知输入，使静默机器人能够实现这种自组织群行为。机器人通过感知邻居在受限环境中形成协同从而创建社区。我们进一步推导了群协同的行为特性，以确保机器人与社区的匿名性/不可追踪性，并展示了与群社区相关各参数的动态性结果（如社区规模、社区位置、社区数量、社区内无特定智能体结构等）。通过分析感知受限场景（机载传感器视场角限制）的影响，我们进一步评估了算法性能。仿真与实验验证了该算法的可扩展性、鲁棒性和快速收敛性。与具有相似目标的最新方法相比，所提无通信群协同在协同或社区形成时间上具有可比性。该算法适用于研究火灾、袭击或灾难等高压场景下的群体动力学。