Bahar Ahmadi
Papers
4
Total Citations
71
H-Index
3
About
Bahar Ahmadi is a robotics and control systems researcher whose work centers on the intersection of computer vision, force control, and robust control theory for industrial robotic systems. Her most significant contribution lies in developing cascade vision/force control architectures that enable industrial robots to interact intelligently with unknown workpieces under model uncertainties — a challenge of fundamental importance in flexible manufacturing environments. Her 2021 paper introducing a continuous integral sliding-mode control method for cascade vision/force control has garnered 51 citations, establishing her as a notable voice in robust robot control. Building on this foundation, Ahmadi has advanced optimal image-based task-sequence and path planning strategies for eye-in-hand robotic systems, addressing the complex multi-task operation problem with elegant hybrid control solutions. Her more recent work tackles workspace expansion challenges for eye-in-hand configurations, broadening the practical applicability of vision-guided robots. Earlier in her career, Ahmadi explored adaptive H∞ control using GA-hybrid wavelet radial basis function networks for robot arm tracking, demonstrating her deep grounding in intelligent control theory. Across her body of work, she has consistently sought to bridge theoretical robustness guarantees with real-world industrial robotics challenges.
Research Focus
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Top Papers
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