Donghun Shin

Papers

Total Citations

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About

Donghun Shin’s research focuses on advancing autonomous mobile robotics, with key contributions in object detection and kinematic modeling. His work bridges deep learning and mechanical design to enhance robot perception and motion control in dynamic environments. In his highly cited 2022 paper, “Environment-Adaptive Object Detection Framework for Autonomous Mobile Robots,” Shin introduced a robust detection system that adapts to varying conditions—critical for real-world deployment. This work, with 5 citations, demonstrates his ability to integrate convolutional neural networks with practical robotics challenges, improving mission efficiency for mobile platforms. Earlier, his 2002 paper, “Generalized Kinematics Modeling of Wheeled Mobile Robots,” provided a systematic framework for analyzing wheeled robot motion, moving beyond ad-hoc methods to a consistent, transformation-based approach. With 3 citations, this foundational work has influenced subsequent research in robot kinematics. Shin’s achievements reflect a career dedicated to both theoretical rigor and applied innovation, making his research essential reading for students and engineers working on autonomous navigation, perception systems, and mobile robot design.