Outputs

In the Computational Robotics Laboratory (CRL/ComRob), our research has delivered practical algorithmic and system-level results that push autonomy from controlled demos to real deployments. We have contributed new methods for mission planning and routing in multi-goal data-collection tasks (including TSP/TSPN-style formulations and robust variants), enabling autonomous robots to gather information efficiently under real-world constraints. We have also produced learning-driven autonomy results—e.g., unsupervised/self-organizing representations that adapt online to prioritize sensing targets and scale to complex environments—supporting long-duration operations and adaptive decision-making. In parallel, CRL advances resilient navigation and control, including work spanning GNSS-denied operation, sensor fusion/SLAM-oriented autonomy themes, and agile control for aerial and legged systems—bridging theory with field-ready robotic platforms used across the lab.