Riasat Khan
Papers
6
Total Citations
85
H-Index
4
About
Riasat Khan is an emerging robotics and computer vision researcher whose work sits at the intersection of intelligent automation, environmental sensing, and human-machine interaction. His research primarily focuses on robotic arm systems, autonomous perception, and applied deep learning for real-world industrial and environmental challenges. Khan's most impactful contribution, "Computer Vision-based Robotic Arm for Object Color, Shape, and Size Detection" (2022), has garnered 51 citations, establishing him as a notable voice in vision-guided industrial robotics. This work demonstrates how automated systems can enhance workplace efficiency while reducing human labor and operational risks. His follow-up study on cooperative robots in industrial environments further extends this vision toward multi-agent automation frameworks. Beyond manufacturing, Khan has demonstrated a commitment to socially meaningful applications. His deep learning-based visual pollution detection system leverages Google Street View integration, while his floating waste-cleaning robot and semi-wireless underwater rescue drone address pressing environmental and humanitarian challenges. His gesture-controlled robotic arm research (11 citations) highlights an additional thread in his work — intuitive human-robot interaction. Across his portfolio, Khan consistently bridges cutting-edge sensing technologies with practical deployment, making his research particularly valuable for students and engineers working in robotics, automation, and AI-driven environmental solutions.
Research Focus
Key Achievements
Top Papers
- 1
- 2Gesture-Controlled Robotic Arm11 citations · 2023
- 3
- 4Semi Wireless Underwater Rescue Drone with Robotic Arm6 citations · 2022
- 5
- 6Design of a Cost-Effective Floating Waste Cleaning Robot3 citations · 2023