Papers
12
Total Citations
216
H-Index
9
About
Mostafa Bagheri is a robotics and control systems researcher whose work centers on robot manipulator control, trajectory optimization, and autonomous robotic systems. He has made significant contributions to addressing some of the most persistent challenges in robotic manipulation, particularly time delay compensation and energy-efficient motion planning for high-degrees-of-freedom (DOF) systems. Bagheri's most cited work, a 2019 study on feedback linearization-based predictors for time delay control (59 citations), tackles the critical problem of input delays in teleoperation and networked robotic systems — a challenge with direct implications for remote surgery, industrial automation, and assistive robotics. His complementary research on adaptive control using batch least-square identification (29 citations) further advances robust manipulation under model uncertainty. A recurring focus throughout his portfolio is the 7-DOF Baxter manipulator, which he has used as an experimental platform for validating novel trajectory optimization frameworks employing multivariable extremum seeking (25 citations) and deep learning-based 3D path planning (11 citations). His earlier work on humanoid shoulder kinematics (31 citations) reflects a broader interest in bimanual robotic systems and workspace optimization. Collectively accumulating over 200 citations, Bagheri's research bridges theoretical control design with rigorous experimental validation, offering practical solutions for next-generation intelligent robotic systems.
Research Focus
Key Achievements
Top Papers
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- 3Adaptive control of a two-link robot using batch least-square identifier29 citations · 2021
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