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About

Nacereddine Djelal is a robotics researcher whose work bridges computer vision, intelligent control, and medical robotics. His primary research areas include visual servoing, target tracking, force-vision control, and the application of deep learning to robotic systems. Djelal’s early contributions focused on robust target tracking by combining SURF features with image-based visual servoing, enabling precise control of Pan-Tilt systems (18 citations). He later advanced adaptive control strategies by integrating sliding mode control with fuzzy logic for manipulator robots, achieving stable force-vision tracking despite kinematic and dynamic uncertainties (14 citations). In the medical domain, Djelal developed a removable device for measuring axial force and orientation on ultrasound probes, supporting tele-echography and robot-assisted diagnostics (11 citations). More recently, he has pioneered the use of LSTM recurrent neural networks to replace computationally intensive interaction matrix calculations in visual control, opening new possibilities for complex robot interactions (2023). His work on measuring ground reaction forces in quadruped robots further demonstrates his versatility in legged locomotion. With a career spanning foundational visual servoing techniques to cutting-edge neural control, Djelal’s research continues to impact autonomous systems and medical robotics.