Papers
18
Total Citations
260
H-Index
9
About
Qiaokang Liang is a leading researcher at the intersection of robotics, sensor technology, and intelligent perception, with a primary focus on multi-axis force/torque sensing systems. His most significant contributions lie in the development of Fiber Bragg Grating (FBG)-based force sensors for medical and underwater robotics, where he has pioneered miniaturized, self-decoupling designs capable of high-sensitivity 3-D and 4-D force measurement. Liang’s work on multi-component force sensing—reviewed in his highly cited 2018 paper (65 citations)—has established foundational methodologies for calibrating and decoupling robotic force/moment sensors, enabling precise interaction force monitoring in complex environments. His innovations extend to agricultural robotics, where he has applied convolutional neural networks for fruit recognition and tactile sensing for assessing fruit hardness, as well as cost-effective object detection for kitchen waste using active learning. With over 200 total citations, Liang’s research has directly impacted medical robotics, underwater manipulation, and automated harvesting. Notable achievements include his 2010 design of a 4-D fingertip force sensor for underwater robot manipulators and his recent 2024 work on a novel FBG 3-D force sensor for medical robotics, demonstrating continued leadership in advancing sensor miniaturization and decoupling techniques.
Research Focus
Key Achievements
Top Papers
- 1
- 2Calibration and decoupling of multi-axis robotic Force/Moment sensors45 citations · 2017
- 3A Potential 4-D Fingertip Force Sensor for an Underwater Robot Manipulator23 citations · 2010
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- 6Active Learning-DETR: Cost-Effective Object Detection for Kitchen Waste13 citations · 2024
- 7Apple recognition based on Convolutional Neural Network Framework12 citations · 2018
- 8
- 9MCS-ResNet: A Generative Robot Grasping Network Based on RGB-D Fusion9 citations · 2024
- 10