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About

Lorenzo Cano is a robotics researcher focused on autonomous navigation in extreme underground environments. His work addresses the unique challenges posed by subterranean spaces—long, featureless corridors, loose and slippery soils, poor illumination, and the absence of global localization signals. Cano’s key contribution is a topological-based navigation system that enables robots to navigate underground using simple, robust representations of the environment, bypassing the need for complex mapping or GPS. This approach, detailed in his most-cited paper (9 citations), offers a practical solution for real-world deployment in mines, tunnels, and caves. To support the development and testing of such systems, Cano has also pioneered procedural generation techniques for creating realistic tunnel networks and underground environments in simulation. His work on generating training data for unsupervised machine learning and on building Gazebo-compatible underground worlds (each with 2 citations) provides essential tools for the robotics community. By combining novel navigation algorithms with accessible simulation frameworks, Cano is advancing the reliability and scalability of autonomous systems in one of the most challenging operational domains.