Papers
43
Total Citations
1,079
H-Index
19
About
Juliang Xiao is a prominent robotics researcher whose work centers on hybrid robot systems, human-robot collaboration, and advanced control strategies for precision manufacturing. His research has made significant contributions to improving the accuracy, safety, and flexibility of robotic systems across industrial applications. Xiao's most influential work addresses two critical challenges in modern robotics: precision motion control and sensorless interaction. His studies on hybrid robots—including the TriMule and 5-DOF platforms—have advanced pose error compensation, dynamic modeling, and feedrate scheduling, with his 2022 paper on pose error prediction alone accumulating 71 citations. Equally impactful is his pioneering research on force/torque sensorless control strategies, enabling compliant robot assembly and human-robot collaboration without costly external sensors, work that has collectively garnered over 150 citations across multiple publications. His contributions extend to specialized manufacturing domains, including dual-robot mirror milling for thin-walled aerospace components and friction-aware collision detection for safe human-robot interaction. With a portfolio spanning fuzzy-PID control, sliding mode controllers, and particle swarm optimization-based scheduling, Xiao's cumulative citation impact exceeds 560, reflecting his broad influence on the robotics research community and practical manufacturing automation.
Research Focus
Key Achievements
Top Papers
- 1Pose error prediction and real-time compensation of a 5-DOF hybrid robot71 citations · 2022
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- 5Research on the collaborative machining method for dual-robot mirror milling60 citations · 2018
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- 8Dynamic modeling and design of a 5-DOF hybrid robot for machining47 citations · 2021
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