Swarm self-clustering for communication-denied environments without global positioning

In this work, we investigate swarm self-clustering, where robots autonomously organize into spatially coherent groups using only local sensing and decision-making, without relying on external commands, global positioning, or inter-robot communication. This fully decentralized approach enables each robot to form and maintain stable clusters by responding solely to distances from nearby neighbors, as detected through onboard range sensors with limited fields of view. Motivated by real-world scenarios such as autonomous underwater robot retrieval and human gathering during emergency response, the proposed method is designed for GPS-denied and communication-constrained environments. Unlike conventional approaches, it requires no prior knowledge of cluster parameters such as size, number, or member identity, making it highly adaptable to unpredictable settings. A mechanism that enables each robot to adaptively alternate between consensus-based and random goal assignment based on local neighborhood size, ensuring robust, scalable, and untraceable clustering independent of initial configuration. Through extensive simulations and real-robot experiments, we demonstrate the method's scalability, empirical convergence, and robustness under varying initial conditions and dynamic robot additions. The proposed communication-free approach outperforms local-only baselines across standard cluster quality metrics, producing clusters that exhibit untraceability even under identical initial conditions.