Reagan L. Galvez

De La Salle University, Bulacan State University

Papers

Total Citations

333

H-Index

About

Reagan L. Galvez is a robotics and computer vision researcher whose work sits at the critical intersection of autonomous systems and public safety. His primary research areas include object detection using deep learning, multi-robot coordination, and the development of intelligent systems for explosive ordnance disposal (EOD). Galvez’s most influential contribution is his 2018 paper, "Object Detection Using Convolutional Neural Networks," which has garnered over 300 citations and established foundational methods for enabling mobile robots to perceive and interpret their environments for tasks like navigation and surveillance. He further advanced the field of swarm robotics with his work on obstacle avoidance for quadrotor UAVs using artificial potential fields, a key contribution to safe, real-time autonomous flight. More recently, Galvez has focused on the high-stakes domain of EOD robotics, developing threat object detection and analysis systems to identify improvised explosive devices—a notable achievement given the data scarcity challenges in this area. His research directly enhances the capability of robots to operate in dangerous environments, making him a significant figure in applied autonomous systems.