Hodjat Rahmati
Papers
1
Total Citations
4
H-Index
1
About
Hodjat Rahmati’s research focuses on advancing robotic manipulation through dynamic modeling and error prediction, with a particular emphasis on improving the accuracy and reliability of robotic systems. His most-cited work, “Improvement of a Robotic Manipulator Model Based on Multivariate Residual Modeling” (2017), introduces a novel method for extending the dynamic model of a six-degree-of-freedom robotic manipulator. By applying non-linear multivariate calibration to input-output training data from typical motion trajectories, Rahmati’s approach predicts systematic output errors in real time, enabling more precise control and performance optimization. This contribution has garnered 4 citations, reflecting its niche but growing impact in the field of robotics and automation. Rahmati’s work is notable for bridging theoretical modeling with practical calibration techniques, offering a pathway to enhance robotic manipulator fidelity in industrial and research settings. His research underscores a commitment to refining autonomous systems, making him a valuable contributor to the ongoing evolution of intelligent robotic technologies.
Research Focus
Key Achievements
Top Papers
- 1