Papers
13
Total Citations
322
H-Index
10
About
Tej Dallej is a leading researcher in the intersection of computer vision and parallel robotics, with a primary focus on the vision-based modeling and control of cable-driven and Gough-Stewart parallel manipulators. His major contributions lie in developing innovative methods that replace or reduce reliance on traditional joint sensors by using visual feedback—a paradigm shift that simplifies robot control and enhances accuracy in large-scale applications. Dallej’s work on 3D pose visual servoing and image-based visual servoing has been foundational, with his most cited paper, "Modeling and vision-based control of large-dimension cable-driven parallel robots using a multiple-camera setup" (2019), accumulating 58 citations. He has also pioneered techniques for kinematic calibration using omnidirectional cameras and has demonstrated control of parallel robots without proprioceptive sensors, as seen in his work on the I4R robot. With over 310 total citations across his top publications, Dallej’s research is highly influential, offering practical solutions for real-world deployment of parallel robots in large-dimension settings. His achievements include advancing generic modeling frameworks for a broad class of parallel mechanisms, making his work essential reading for students and researchers in robotics, control systems, and computer vision.
Research Focus
Key Achievements
Top Papers
- 1
- 2Towards vision-based control of cable-driven parallel robots46 citations · 2011
- 3
- 4
- 53D Pose Visual Servoing Relieves Parallel Robot Control from Joint Sensing27 citations · 2006
- 6Towards vision-based control of cable-driven parallel robots25 citations · 2011
- 7
- 8
- 9
- 10