Papers
11
Total Citations
367
H-Index
8
About
Guillaume Allibert is a researcher whose work spans robotics, computer vision, and control theory, with particular expertise in visual servoing, model predictive control, and state estimation for robotic systems. He is perhaps best known for his highly influential 2010 paper on predictive control for constrained image-based visual servoing (IBVS), which has accumulated over 220 citations and remains a landmark contribution in the field. By reformulating robot workspace limitations, visibility constraints, and actuator limitations within a nonlinear model predictive control (NMPC) framework, Allibert provided a rigorous and practical solution to one of robotics' most persistent challenges. His earlier work extended these visual predictive control methods to mobile robots and catadioptric camera systems, demonstrating broad applicability across robotic platforms. More recently, his research has evolved toward aerial robotics, tackling attitude estimation, pose estimation using IMU and landmark measurements, and supervisory control of multirotor vehicles in demanding conditions. His 2018 Riccati observer design paper reflects a growing focus on mathematically elegant estimation frameworks grounded in geometric principles. Most recently, he has ventured into forest robotics and deep learning-based scene understanding, underscoring the versatility and continued evolution of his research vision.
Research Focus
Key Achievements
Top Papers
- 1Predictive Control for Constrained Image-Based Visual Servoing222 citations · 2010
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- 3Visual predictive control for manipulators with catadioptric camera30 citations · 2008
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- 7Switching controller for efficient IBVS12 citations · 2012
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