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About

Deng Chen is a robotics researcher whose work sits at the intersection of computer vision, adversarial machine learning, and educational robotics. His primary research areas include monocular vision-based robot localization, adversarial attacks on robot vision systems, and industrial robot pose estimation. Chen’s most cited paper, “Robot Target Location Based on the Difference in Monocular Vision Projection” (2022, 7 citations), addresses the challenge of guiding industrial robots to locate complex workpieces with height variations—a practical problem in automated manufacturing. He also made notable contributions to robot security with “Attacking Robot Vision Models Efficiently Based on Improved Fast Gradient Sign Method” (2024, 3 citations), proposing the RMS-FGSM algorithm to expose vulnerabilities in deep learning-based vision models. In “Pose estimation for six-axis industrial robots based on pose distillation” (2022, 2 citations), Chen tackled the efficiency-accuracy trade-off in monitoring industrial robot movements for safety. Beyond technical contributions, he developed a “Design of Educational Robot Platform Based on Graphic Programming” (2020, 2 citations), bridging robotics and STEM education. Though his citation counts are modest, Chen’s work demonstrates a coherent focus on making robot vision systems both more capable and more secure, with practical implications for industrial automation and educational technology.