Mohammad Qori Aziz Hakiki

Papers

Total Citations

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About

Mohammad Qori Aziz Hakiki is a robotics researcher specializing in visual servoing and manipulator control, with a focus on integrating deep learning for real-time industrial automation. His most-cited work, "YOLOv7-based Visual Servoing on 2-DOF Manipulator Robot" (2023, 2 citations), addresses the critical challenge of achieving fast, precise feature extraction in robotic systems—a key limitation of conventional control methods. By leveraging the YOLOv7 object detection framework, Hakiki demonstrates how neural networks can enhance visual feedback loops, enabling manipulator robots to handle repetitive tasks like component movement with improved accuracy and responsiveness. This contribution bridges computer vision and robotics, offering a scalable solution for smart manufacturing. Though early in his career, his work highlights a growing trend toward AI-driven automation, where real-time visual data replaces rigid pre-programmed instructions. Hakiki’s research is particularly relevant for students and engineers exploring the intersection of deep learning and mechatronics, as it provides a practical framework for adaptive robot control. His approach underscores the potential of lightweight neural architectures in resource-constrained industrial settings, paving the way for more flexible, intelligent production lines.