Umar Shabaz Khan

National University of Sciences and Technology

Papers

Total Citations

H-Index

About

Umar Shabaz Khan’s research lies at the intersection of robotics, computer vision, and cyber-physical systems, with a focus on enhancing industrial automation through intelligent sensing. His most cited work, “Vision-Based Hybrid Detection For Pick And Place Application In Robotic Manipulators” (2023, 11 citations), addresses a critical challenge in collaborative robotics: reducing positional uncertainty in object detection. By integrating vision sensing with a UR5 cobot, Khan developed a hybrid detection framework that enables more reliable and autonomous decision-making in pick-and-place tasks—a cornerstone application in modern manufacturing. This contribution demonstrates his ability to bridge theoretical sensing algorithms with practical robotic manipulation, improving efficiency and adaptability in cyber-physical environments. Khan’s work is particularly notable for its direct relevance to Industry 4.0, where smarter, vision-guided cobots are essential for decreasing human intervention and increasing precision. With a growing citation impact, his research continues to influence the design of robust, real-time robotic systems. For students and researchers exploring the fusion of vision and robotics, Khan’s approach offers a compelling model for creating more responsive and autonomous industrial robots.