Papers
8
Total Citations
109
H-Index
6
About
Xiaohong Liang is a leading researcher in robotics and computer vision, whose work bridges the critical gap between perception and physical interaction. Liang’s primary research areas include multi-robot localization, robotic manipulation, and 3D object recognition. A standout contribution is the development of a UWB-VIO fusion framework for relative localization in round robotic teams, which achieved 29 citations by significantly improving accuracy and robustness in infrastructure-free environments—a fundamental challenge for multi-robot systems. In robotic assembly, Liang pioneered a contact force estimation method compensated by convolutional neural networks (23 citations), enabling precise peg-in-hole tasks without expensive force sensors. Liang’s early work on 3D object recognition and pose determination (18 citations) laid groundwork for automated model synthesis from single-camera images, while a vision-based online motion planning system (12 citations) advanced real-time obstacle avoidance for manipulators. Notable achievements include a disturbance observer-based force-tracking control method (8 citations) and nonlinear dynamics modeling using phase space reconstruction (10 citations), both enhancing robot autonomy. With applications spanning construction equipment visualization to industrial assembly, Liang’s research consistently pushes the boundaries of sensor-less, vision-driven robotics, making complex tasks more accessible and reliable.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3Robotic vision: 3D object recognition and pose determination18 citations · 2002
- 4A vision based online motion planning of robot manipulators12 citations · 2002
- 5
- 6
- 7
- 8Robot vision: model synthesis for 3D objects4 citations · 2002