Mohammad Keshmiri
McGill University, Concordia University, Isfahan University of Technology
Papers
16
Total Citations
393
H-Index
10
About
Mohammad Keshmiri is a robotics researcher whose work centers on visual servoing, robotic manipulation, and autonomous systems. He is best known for his pioneering contributions to image-based visual servoing (IBVS) for six-degrees-of-freedom robotic manipulators, a field in which he has consistently pushed the boundaries of control sophistication and real-world applicability. His most influential work, "Robust On-Line Model Predictive Control for a Constrained Image Based Visual Servoing" (2015, 97 citations), introduced a robust model predictive control framework that elegantly handles system constraints, while his closely related "Augmented Image-Based Visual Servoing Using Acceleration Command" (2014, 96 citations) proposed a novel acceleration-based control paradigm that delivers smoother, more precise robotic motion. Keshmiri further advanced the field through optimized trajectory planning methods that extend IBVS operational workspaces and improve task reliability. His broader research portfolio includes navigation guidance algorithms for intercepting moving objects, stereo vision-based tracking systems, and the design of tracked unmanned ground vehicles for surveillance and rescue applications. With over 350 cumulative citations, Keshmiri's body of work represents a substantial contribution to intelligent robotic control, making his research essential reading for students and practitioners working at the intersection of computer vision and robotic systems.
Research Focus
Key Achievements
Top Papers
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- 3Image-Based Visual Servoing Using an Optimized Trajectory Planning Technique72 citations · 2016
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