Maryam Kouzehgar
Singapore University of Technology and Design, University of Tabriz
Papers
5
Total Citations
121
H-Index
4
About
Maryam Kouzehgar is a robotics researcher whose work spans self-reconfigurable systems, bio-inspired deception, and intelligent cleaning robots. Her most influential contribution, "Self-reconfigurable façade-cleaning robot equipped with deep-learning-based crack detection based on convolutional neural networks" (92 citations), addresses the high-risk, labor-intensive maintenance of glassy high-rise structures by integrating deep learning for autonomous crack detection. This work exemplifies her focus on practical, safety-critical applications of robotics. Kouzehgar is also a pioneer in fusing fuzzy logic with deception theory, as seen in her ant-inspired deceptive robots research (2015, 6 citations), where she modeled uncertainty in artificial deception for the first time. Her Tetris-inspired reconfigurable cleaning robot (2018, 11 citations) applies multi-criteria decision making to optimize tiling path planning for energy efficiency and coverage. Through these contributions, Kouzehgar demonstrates a unique ability to combine theoretical frameworks—such as fuzzy signaling games and meta-heuristics—with real-world robotic systems, advancing both the intelligence and autonomy of robots in challenging environments.
Research Focus
Key Achievements
Top Papers
- 1
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- 4Ant-Inspired Fuzzily Deceptive Robots6 citations · 2015
- 5Fuzzy deception game using ant-inspired meta-heuristics3 citations · 2014