Replication Data for: Multi-Agent Swarm Coordination for Distributed Mapping of Non-Euclidean Infrastructure Topologies

This research establishes a rigorous mathematical framework for multi-agent swarm coordination to solve the challenges of mapping complex, non-Euclidean infrastructure topologies, such as urban utility networks or subterranean conduits. By formalizing these environments as connected graphs, the study develops decentralized communication protocols and decision-making algorithms that prioritize unexplored edges and leverage local information fusion. Through extensive discrete-event simulations in Python using both synthetic and real-world-inspired graph datasets, the research demonstrates that swarm intelligence provides a significant 5.8 to 18.3-fold speed advantage over traditional single-agent approaches. These findings underscore the efficiency and resilience of collective robotics for critical infrastructure assessment, providing a scalable solution for rapid topological discovery in GPS-denied and highly connected urban \"city veins\".