Saul Nieto Bastida

National Taiwan University of Science and Technology

Papers

Total Citations

H-Index

About

Dr. Saul Nieto Bastida is a leading researcher in autonomous robotics and intelligent manufacturing, whose work bridges the critical gap between aerial and industrial automation. His primary research areas include deep learning for unmanned aerial vehicle (UAV) autonomy, robotic trajectory planning, and surface treatment automation. Dr. Nieto Bastida’s most impactful contribution addresses a fundamental barrier to civilian drone integration: safe, automated landing. His 2020 paper, "Landing Area Recognition using Deep Learning for Unmanned Aerial Vehicles" (20 citations), pioneered a vision-based system that enables UAVs to autonomously identify and localize safe landing zones in populated areas, a key step toward commercial logistics and urban air mobility. In parallel, his 2023 work, "Autonomous Trajectory Planning for Spray Painting on Complex Surfaces Based on a Point Cloud Model" (18 citations), revolutionizes industrial robotics by replacing cumbersome manual programming with an autonomous system that generates optimal tool paths directly from 3D point cloud data. This innovation dramatically improves efficiency and quality in coating complex geometries. With a growing citation record reflecting the practical urgency of his solutions, Dr. Nieto Bastida is recognized for transforming both aerial safety and manufacturing precision, making autonomous systems more reliable and accessible for real-world deployment.