Max Robotics — Global Service Robot Directory + US Certification

About

Xiaofang Yuan is a robotics and control systems researcher whose work spans intelligent control, robot perception, and autonomous manipulation. With a focus on bridging advanced machine learning with practical robotic applications, Yuan has made significant contributions to unmanned aerial manipulators, autonomous mobile robots, and industrial robotic systems. Yuan's most-cited work (31 citations) addresses robust control strategies for unmanned aerial manipulators, tackling complex challenges like model uncertainty and environmental disturbances. Earlier contributions include neural network-based self-learning control for power transmission line deicing robots (21 citations) and extreme learning machine-based predictive control for autonomous mobile robot path-tracking (16 citations), demonstrating a sustained interest in intelligent, adaptive control frameworks. A notable thread running through Yuan's recent research is industrial robotic perception — particularly 6D pose estimation, robotic grasping, and 3D surface measurement for blade manufacturing. Papers on geometric inlier selection for rigid registration, pixel-wise prediction networks for grasping, and depth-adaptive pose estimation (collectively accumulating over 45 citations) reflect Yuan's growing impact in robot vision systems. Additional work on UWB-based localization and grinding fault monitoring underscores a comprehensive approach to smart manufacturing, positioning Yuan as a versatile contributor to modern robotics research.