Papers
16
Total Citations
270
H-Index
8
About
Guoqiang Fu is a pioneering roboticist whose research spans autonomous navigation, microrobotics, and intelligent perception systems. His most influential work tackles the fundamental challenge of enabling robots to perceive and interact with their environments. In a landmark 2014 paper (55 citations), Fu introduced a deep learning approach for door recognition in visual-based robot navigation, demonstrating how convolutional neural networks could extract robust features for autonomous movement—a significant departure from traditional fixed-model methods. Earlier, he developed an integrated triangulation laser scanner for miniature mobile robots (49 citations), addressing the critical spatial and power constraints of small-scale platforms. His work on wireless microrobots (45 citations) showcased innovative ferromagnetic polymer fins for magnetic field-driven swimming robots, advancing the frontier of biomedical microdevices. Fu also contributed to practical surveillance systems with Kinect-based door detection and crossing algorithms (17 citations), and explored modular robotics with a genderless, fail-safe connection system (13 citations). More recently, his research has extended to industrial robot 3D printing optimization and patient care robots using improved YOLOv5 object recognition (10 citations). With over 250 total citations, Fu’s work consistently bridges theoretical innovation and real-world robotic applications, from miniature explorers to healthcare assistants.
Research Focus
Key Achievements
Top Papers
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- 3Wireless drive and control of a swimming microrobot45 citations · 2003
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- 5Detecting, locating and crossing a door for a wide indoor surveillance robot17 citations · 2013
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