Asad Khan
Papers
3
Total Citations
24
H-Index
2
About
Asad Khan is a rising researcher at the forefront of intelligent robotics and smart manufacturing, whose work bridges the physical and digital worlds. His primary research areas include digital twin technology, visual servoing, and robust multi-robot control systems. Khan’s most significant contribution is his pioneering work on a Digital Twin-based Visual Servoing framework, which leverages Extreme Learning Machines and Differential Evolution to dramatically enhance the flexibility and precision of automated assembly and dispensing in smart factories. This work, his most cited paper with 15 citations, demonstrates a practical path toward more adaptive manufacturing. He has also made notable strides in multi-modal perception, developing a multistage deep neural network that fuses visible and thermal images for superior people detection and localization. Addressing modern security challenges, Khan’s research extends to resilient control, where he has designed a fuzzy logic-based hybrid control strategy to achieve robust consensus in leader-follower robotic systems even under sophisticated sensor and actuator attacks. Through these contributions, Asad Khan is establishing himself as a key innovator in creating more intelligent, secure, and efficient autonomous systems for industry.
Research Focus
Key Achievements
Top Papers
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