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About

Navid Asadi Khomami is a robotics researcher whose work sits at the intersection of parallel robotics, deep learning, and autonomous manipulation. His primary research focuses on integrating computer vision and deep neural networks with Delta parallel robots to perform complex, real-world tasks with high precision. Among his most notable contributions is an experimental study on automated chessboard setup using a Delta robot guided by deep learning, which demonstrates how vision-based systems can replace manual labor in structured environments. He has also advanced laboratory automation by developing a calibration method for microorganism culturing patterns using a Delta robot and a cam-in-hand approach, addressing reproducibility issues in manual lab work. His recent work on autonomous robotic assembly employs YOLOv8 for real-time object detection and sequence planning, enabling robots to observe, plan, and execute assembly tasks autonomously. With over 20 citations across his published works, Asadi Khomami’s research is gaining traction for its practical applications in manufacturing, catering, and laboratory automation. His pioneering study on automated catering package assembly using a 3-DOF Delta robot and deep learning further underscores his commitment to bridging the gap between robotic precision and everyday tasks.