Papers
12
Total Citations
69
H-Index
6
About
Anamaria Dogar is a researcher specializing in robotics, computer vision, and intelligent manufacturing systems, with particular expertise in motion planning, visual guidance, and fault-tolerant robotic platforms. Her work bridges theoretical algorithmic development and practical industrial implementation, making significant contributions to the automation and flexibility of modern manufacturing environments. Among her most notable contributions is her development of vision-based methods for robot guidance in conveyor tracking, where stationary camera systems enable precise real-time localization of moving objects — a technically demanding problem with direct industrial relevance. Her research on constrained motion planning for redundant 7-DOF mechanisms reflects a sustained commitment to solving complex real-time robotics challenges, particularly in the context of 3D laser scanning and reverse engineering applications. Dogar has also made meaningful advances in holonic fault-tolerant manufacturing architectures, exploring how networked multi-robot systems can adapt dynamically to changing production conditions and technological requirements. Her work on collision avoidance further demonstrates a focus on robust, safe robot operation in real-world settings. With a cumulative citation count exceeding 65 across her most recognized publications, her research has informed both academic understanding and applied robotics practice, offering valuable insights for students and engineers working in automation, industrial robotics, and intelligent manufacturing systems.
Research Focus
Key Achievements
Top Papers
- 1Visual robot guidance in conveyor tracking with belt variables14 citations · 2010
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- 4A HOLONIC FAULT TOLERANT MANUFACTURING PLATFORM WITH MULTIPLE ROBOTS7 citations · 2006
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- 6REMOTE MONITORING AND CONTROL OF A ROBOTIZED FAULT TOLERANT WORKCELL6 citations · 2006
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