Swarm Self Clustering for Communication denied Environments without Global Positioning
This addresses swarm robotics in GPS-denied and communication-constrained environments, offering a scalable and robust solution for applications like search-and-rescue or military operations, though it appears incremental in method.
The paper tackles the problem of enabling robot swarms to autonomously form spatially coherent clusters using only local sensing, without external commands, GPS, or communication, and demonstrates improved performance over baselines in simulations and real-robot experiments.
In this work, we investigate swarm self-clustering, where robots autonomously organize into spatially coherent groups using only local sensing and decision-making, without external commands, global positioning, or inter-robot communication. Each robot forms and maintains clusters by responding to relative distances from nearby neighbors detected through onboard range sensors with limited fields of view. The method is suited for GPS-denied and communication-constrained environments and requires no prior knowledge of cluster size, number, or membership. A mechanism enables robots to alternate between consensus-based and random goal assignment based on local neighborhood size, ensuring robustness, scalability, and untraceable clustering independent of initial conditions. Extensive simulations and real-robot experiments demonstrate empirical convergence, adaptability to dynamic additions, and improved performance over local-only baselines across standard cluster quality metrics.