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About

Adrian Burlacu is a robotics and control systems researcher whose work spans visual servoing, advanced control theory, computer vision, and autonomous aerial systems. His most influential contribution — an orthogonal dual tensor method for solving the AX=XB sensor calibration problem (2016, 81 citations) — established a robust mathematical framework widely adopted in robotic calibration. Burlacu has made substantial advances in visual servoing for robot manipulators, pioneering the application of model predictive control and fractional-order control strategies to eye-in-hand robotic configurations, with multiple papers in this area accumulating over 130 combined citations. His research demonstrates a consistent drive to tame the inherent nonlinearity and coupling complexity of vision-guided robotic systems. Beyond manipulation, Burlacu has expanded into applied machine learning, developing an industry-deployed defect detection system for porcelain manufacturing using deep learning (2017, 27 citations), bridging academic research with real industrial impact. His later work explores autonomous drone systems, including evaluation of the Crazyflie quadcopter platform and optimal indoor goods delivery using micro aerial vehicles. Through rigorous theoretical contributions and practical implementations, Burlacu has built a diverse research portfolio that continues to influence robotics, automation, and intelligent systems communities worldwide.