Ivan Lorencin
Papers
7
Total Citations
186
H-Index
5
About
Ivan Lorencin is a robotics researcher whose work sits at the intersection of evolutionary computation, artificial intelligence, and industrial automation. His primary research areas include path planning optimization for robotic manipulators, inverse kinematics using neural networks, and intelligent fault detection in autonomous manufacturing systems. Lorencin’s most impactful contribution is his pioneering use of evolutionary algorithms—specifically genetic algorithms and memetic algorithms—to minimize joint torques and energy consumption in six-degree-of-freedom robotic arms, a breakthrough that directly improves both robot longevity and operational efficiency. His work on applying multilayer perceptron networks to solve inverse kinematics has garnered 50 citations, while his YOLO-based detection and classification of printed circuit boards (38 citations) demonstrates the practical industrial relevance of his research. Lorencin has also advanced fault diagnosis in robotic systems using symbolic classifiers trained via genetic programming, contributing to safer autonomous production lines. With over 186 total citations across his most-cited papers, Lorencin is recognized for bridging theoretical optimization methods with real-world robotic applications, making him a notable figure in the field of intelligent robotics and manufacturing automation.
Research Focus
Key Achievements
Top Papers
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- 3Detection and Classification of Printed Circuit Boards Using YOLO Algorithm38 citations · 2023
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