R. Barzamini

About

R. Barzamini is a robotics researcher whose work centers on the control and navigation of wheeled mobile robots, with a particular emphasis on adaptive tracking control and fuzzy-logic-based path planning. His major contributions include the development of novel adaptive algorithms for robust trajectory tracking in wheeled mobile robots, as detailed in his most-cited paper, "Model Reference Adaptive Path Following for Wheeled Mobile Robots" (12 citations), and its companion work, "A New Adaptive Tracking Control For Wheeled Mobile Robot" (11 citations). These papers address the challenge of maintaining precise motion control despite external disturbances, advancing the field of autonomous navigation. Additionally, Barzamini pioneered the use of fuzzy inference systems for path planning in unknown environments, notably for Automated Guided Vehicles (AGVs), as seen in "AGV Path Planning in Unknown Environment Using Fuzzy Inference Systems" (8 citations) and its multi-robot extension (6 citations). His research on multi-robot coordination, such as in "A New Fuzzy Path Planning For Multiple Robots" (6 citations), further demonstrates his impact on scalable, intelligent robotic systems. With a cumulative citation count exceeding 40 across his key works, Barzamini’s contributions remain foundational for students and researchers exploring adaptive control and fuzzy logic in mobile robotics.

Research Focus

Key Achievements

Top Papers

1. 1
   Model Reference Adaptive Path Following for Wheeled Mobile Robots

   12 citations · 2006
2. 2
   A New Adaptive Tracking Control For Wheeled Mobile Robot

   11 citations · 2006
3. 3
   AGV Path Planning in Unknown Environment Using Fuzzy Inference Systems

   8 citations · 2006
4. 4
   A New Fuzzy Path Planning For Multiple Robots

   6 citations · 2006
5. 5
   Multi AGV path planning in unknown environment using fuzzy inference systems

   6 citations · 2008

Key Collaborators

Contact & Links