Carlos Redondo
Papers
2
Total Citations
38
H-Index
2
About
Carlos Redondo is a robotics researcher specializing in autonomous exploration and hazard detection for unmanned aerial vehicles (UAVs). His primary contributions lie in developing efficient frontier-based exploration algorithms for resource-constrained aerial robots, particularly in unstructured environments. His most-cited work, "Optimal Frontier-Based Autonomous Exploration in Unconstructed Environment Using RGB-D Sensor" (2020, 33 citations), addresses the critical challenge of enabling UAVs to autonomously navigate and map unknown areas despite limited onboard payload and computing power—a key requirement for search and rescue missions. More recently, Redondo has advanced the field of safety-critical robotics through "Sensing Anomalies as Potential Hazards: Datasets and Benchmarks" (2022), providing foundational resources for detecting environmental anomalies that could pose risks during autonomous operations. His research bridges the gap between theoretical exploration algorithms and practical deployment constraints, making aerial robots more reliable in real-world emergency scenarios. By tackling both exploration efficiency and hazard awareness, Redondo's work is shaping the next generation of autonomous systems capable of operating safely in complex, unpredictable environments.
Research Focus
Key Achievements
Top Papers
- 1
- 2Sensing Anomalies as Potential Hazards: Datasets and Benchmarks5 citations · 2022