Rodrigo Zelir Azzolin
Universidade Federal do Rio Grande, University of Rio Grande and Rio Grande Community College
Papers
9
Total Citations
57
H-Index
5
About
Rodrigo Zelir Azzolin is a robotics and automation researcher whose work focuses on advancing industrial welding, pipe inspection, and control systems. His primary contributions lie in developing intelligent, sensor-driven solutions for manufacturing and infrastructure maintenance. Azzolin’s most impactful work, "Online Sound Based Arc-Welding Defect Detection Using Artificial Neural Networks" (13 citations), pioneers the use of acoustic signals and neural networks for real-time quality control in heavy steel industries, reducing waste and human oversight. He also advanced automated seam tracking with passive monocular vision (12 citations), enabling robots to perform precise linear welding in hazardous environments like shipbuilding. In pipeline inspection, Azzolin proposed sensor data fusion using Kalman Filters (8 citations) to improve robot navigation and defect detection. His research extends to motor control optimization, backlash compensation in robotic systems, and mathematical modeling for rehabilitation. With a total of over 55 citations across his published works, Azzolin demonstrates a sustained commitment to bridging theoretical control methods with practical automation challenges, making him a notable figure in industrial robotics and sensor integration.
Research Focus
Key Achievements
Top Papers
- 1
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- 3Sensors data fusion to navigate inside pipe using Kalman Filter8 citations · 2016
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- 5Backlash Robotic Systems Compensation by Inverse Model-Based PID Control6 citations · 2019
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- 9Inside Pipe Inspection: A Review Considering the Locomotion Systems2 citations · 2016