Harold Lucero
Papers
1
Total Citations
2
H-Index
1
About
Harold Lucero is a researcher at the forefront of applied computer vision and autonomous mobile robotics, with a particular focus on enhancing operational efficiency and privacy in service robots. His most cited work, "Restricted Area Sign Detector Using YOLO v5" (2023), demonstrates a practical integration of deep learning and robotics by implementing a YOLO v5 model on a mobile robot to detect restricted area signs during medicine delivery rounds. This contribution addresses two critical challenges: preventing robots from violating privacy by entering off-limits zones, and optimizing delivery times by enabling intelligent navigation. With 2 citations, this paper has already sparked interest in the intersection of object detection and real-world robotic constraints. Lucero’s work is notable for its direct application to healthcare logistics, where autonomous systems must balance speed with strict spatial boundaries. His research bridges the gap between state-of-the-art computer vision algorithms and tangible robotic behaviors, offering a scalable solution for secure, efficient indoor navigation in sensitive environments.
Research Focus
Key Achievements
Top Papers
- 1Restricted Area Sign Detector Using YOLO v52 citations · 2023