Mingxing Yuan
Papers
12
Total Citations
133
H-Index
6
About
Mingxing Yuan is a robotics researcher whose work spans trajectory planning, teleoperation, cable-driven systems, and autonomous robotic control. With a career arc stretching from foundational algorithmic contributions to cutting-edge applied systems, Yuan has established a distinctive research identity at the intersection of motion planning and intelligent robot control. Yuan's most influential contribution, a real-time acceleration-continuous trajectory planning algorithm (2020, 48 citations), introduced an elegant tradeoff mechanism between cruise and time-optimal motions, addressing a longstanding challenge in high-precision robotics and CNC machining. This built on earlier theoretical groundwork in time-optimal trajectory planning published in 2014 and 2015, demonstrating a sustained commitment to advancing motion efficiency under physical constraints. His 2018 work on modular master-slave teleoperation systems (29 citations) reflects a practical engineering sensibility, delivering a flexible ROS-based platform applicable across diverse hardware configurations. More recently, Yuan has expanded into model-free robust adaptive control of cable-driven robots, reinforcement learning for pursuit-evasion scenarios, dynamic motion planning in cluttered environments, and force-controlled manipulation in uncertain settings. His 2025 contributions to autonomous robotic ultrasound imaging signal an exciting translation of his control expertise into medical robotics, underscoring the breadth and growing real-world relevance of his research portfolio.
Research Focus
Key Achievements
Top Papers
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- 3A New Model-free Robust Adaptive Control of Cable-driven Robots15 citations · 2021
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- 6A Novel Algorithm for Time Optimal Trajectory Planning6 citations · 2014
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