Papers
5
Total Citations
33
H-Index
2
About
Jonathan Fried’s research lies at the intersection of robot control, visual servoing, and trajectory optimization, with a focus on making robotic systems more adaptive and efficient under uncertainty. His major contributions include developing uncalibrated image-based visual servoing (IBVS) methods that enable robot manipulators to track translational trajectories using only visual feedback from a fixed monocular camera, even when camera parameters and robot kinematics are unknown. His most cited work, “Uncalibrated image-based visual servoing approach for translational trajectory tracking with an uncertain robot manipulator” (2022, 23 citations), demonstrates a practical solution to a long-standing challenge in vision-based robot control. Fried has also advanced trajectory optimization for redundant manipulators, introducing a bi-level optimization method for dual-arm systems that minimizes motion time while respecting joint limits. His work on adaptive IBVS with time-varying learning rates further refines real-time control performance. With a growing citation record and contributions that bridge theory and application, Fried’s research is particularly relevant for students and engineers working on autonomous robotics, manufacturing automation, and vision-guided manipulation.
Research Focus
Key Achievements
Top Papers
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