Unai Izagirre
Papers
5
Total Citations
93
H-Index
3
About
Unai Izagirre is a researcher at the forefront of industrial robotics, specializing in predictive maintenance, health assessment, and intelligent automation for manufacturing. His work focuses on developing non-intrusive methodologies to monitor and optimize the performance of industrial robots, ensuring reliability and efficiency in assembly lines. Izagirre’s most impactful contribution is a practical, synchronized data acquisition network architecture for predictive maintenance (47 citations), which enables real-time fault detection and reduces downtime. He has also pioneered vision-based data-driven approaches for accuracy degradation assessment (29 citations) and torque signature analysis for joint health assessment (13 citations), providing robust tools for robot condition monitoring. His innovative torque-based methodology for optimizing robot standby poses further enhances energy efficiency and longevity. More recently, Izagirre has explored novel automated interactive reinforcement learning frameworks with constraint-based supervision for procedural tasks (2024), pushing the boundaries of adaptive robotics in unstructured environments. With a growing citation record and a focus on bridging theoretical advances with experimental implementation, Izagirre’s work is shaping the future of smart manufacturing and autonomous robotic systems.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3
- 4
- 5