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About

David Morilla-Cabello is a robotics researcher focused on advancing autonomous systems for critical applications, particularly in search and rescue (SAR) and disaster response. His work centers on three interconnected areas: multimodal perception, efficient path planning, and semantic mapping for robotic teams. Morilla-Cabello’s most significant contribution is the **UMA-SAR dataset** (27 citations), a comprehensive multimodal collection from real-world outdoor SAR exercises, providing the research community with invaluable raw sensor data to develop and benchmark perception algorithms for emergency response. He further addresses the operational constraints of aerial robots with **Sweep-Your-Map** (15 citations), introducing a hierarchical coverage planning algorithm that dramatically improves efficiency for multi-rotor teams in large-scale environments. His work on **Robust Fusion for Bayesian Semantic Mapping** (10 citations) tackles the challenge of integrating noisy neural network observations into coherent environmental models, while his latest research on **Perceptual Factors for Environmental Modeling** (6 citations) explores how robots can intelligently assess the value of new sensor data for active perception. Morilla-Cabello’s research directly bridges the gap between theoretical robotics and practical, life-saving applications.