Shadi Najjar
Papers
1
Total Citations
7
H-Index
1
About
Shadi Najjar is a researcher whose work centers on advancing the precision and reliability of robotic systems, with a particular focus on kinematic calibration, Bayesian inference, and uncertainty quantification in serial manipulators. In their most-cited work, “Kinematic calibration of serial manipulators using Bayesian inference” (2018, 7 citations), Najjar introduced a novel calibration method that fuses prior parameter knowledge with artifact measurement data through Bayesian inference. This approach not only updates kinematic parameters but also provides confidence bounds, offering a richer, more robust framework for robot accuracy than traditional deterministic methods. This contribution is especially significant for applications in manufacturing, surgical robotics, and autonomous systems where precise motion is critical. By integrating probabilistic reasoning into calibration, Najjar’s work bridges the gap between theoretical modeling and real-world performance, enabling more adaptive and trustworthy robotic platforms. Their research continues to influence the growing field of robot metrology, making them a notable voice in the pursuit of smarter, more reliable automation.
Research Focus
Key Achievements
Top Papers
- 1Kinematic calibration of serial manipulators using Bayesian inference7 citations · 2018