Papers
48
Total Citations
1,238
H-Index
15
About
Homayoun Najjaran is a robotics and autonomous systems researcher whose work spans human-robot collaboration, motion planning, aerial vehicles, and intelligent sensing. Best known for his comprehensive survey on robot learning strategies for human-robot collaboration in industrial settings — which has garnered over 328 citations — Najjaran has established himself as a leading voice in applying machine learning to practical robotic challenges. His widely cited review of quadrotor systems (243 citations) reflects deep expertise in underactuated mechanical systems and unmanned aerial vehicles. Early in his career, Najjaran pioneered pipe inspection robotics, developing visual SLAM methods and human-analogous control strategies for robots navigating live water mains — work that bridged nondestructive testing with autonomous navigation. His research portfolio also encompasses flexible strain sensors for human motion monitoring, deep reinforcement learning for multi-agent pathfinding and machine scheduling, and model predictive control for robust manipulation. Across more than a decade of prolific output, Najjaran has consistently pushed the boundaries of intelligent robotics — from low-cost wearable sensors to large-scale autonomous systems — making his contributions essential reading for students and researchers at the intersection of robotics, AI, and industrial automation.
Research Focus
Key Achievements
Top Papers
- 1
- 2A review of quadrotor: An underactuated mechanical system243 citations · 2018
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- 4A review of recent trend in motion planning of industrial robots73 citations · 2023
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- 6Dynamic analysis and human analogous control of a pipe crawling robot44 citations · 2009
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