Papers
7
Total Citations
99
H-Index
5
About
Nigora Gafur is a robotics and control systems researcher whose work sits at the intersection of motion planning, optimization, and intelligent automation for robotic manipulators. Her research primarily addresses the challenge of enabling multiple robotic arms to operate safely, flexibly, and efficiently in shared and dynamic environments — a critical frontier in modern manufacturing and human-robot collaboration. Gafur's most impactful contributions center on applying model predictive control (MPC) to real-time trajectory planning, with her 2020 paper on cooperative robot scheduling garnering 33 citations and her 2022 work on dynamic collision and deadlock avoidance accumulating 24 citations. Together, these works establish her as a leading voice in multi-robot coordination. Her research on flatness-based control using secondary encoders (23 citations) demonstrates additional depth in precision industrial robot control. Beyond classical optimization, Gafur has pioneered the integration of reinforcement learning for job-shop scheduling and developed agent-based frameworks for human-robot collaboration, reflecting a forward-looking approach to autonomous production systems. Her growing body of work — spanning hierarchical control, distributed MPC, and skill-based multi-agent architectures — positions her as an influential contributor to the future of flexible, intelligent manufacturing robotics.
Research Focus
Key Achievements
Top Papers
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- 2Dynamic Collision and Deadlock Avoidance for Multiple Robotic Manipulators24 citations · 2022
- 3Flatness Based Control of an Industrial Robot Joint Using Secondary Encoders23 citations · 2020
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