Papers
24
Total Citations
203
H-Index
8
About
Quanjun Song is a pioneering researcher in robotics and sensor technology, with key contributions spanning multisensor fusion, force sensing, and human-robot interaction. His most cited work, "Fast and Robust Data Association Using Posterior Based Approximate Joint Compatibility Test" (2013, 54 citations), revolutionized data association in multisensor fusion and localization by overcoming the computational expense and linearization errors of traditional joint compatibility tests. Song also advanced underwater robotics with a novel 4-D fingertip force sensor (2010, 23 citations) for manipulators, enabling precise interaction force measurement. His innovative application of neural networks to nonlinear static decoupling of wrist force sensors (2006, 17 citations) significantly improved measurement precision. Song's research extends to power assist robots, where he developed neural network ensembles for sit-to-stand motion phase recognition (2007, 11 citations), and to human motion recognition via surface EMG signals in arm wrestling robots (2006, 10 citations). He has also explored multi-agent role allocation in robotic soccer (2005, 9 citations) and gait phase recognition for exoskeletons (2022, 8 citations). With over 150 citations across his work, Song's contributions are foundational to intelligent robotics, sensor design, and human-machine collaboration.
Research Focus
Key Achievements
Top Papers
- 1
- 2A Potential 4-D Fingertip Force Sensor for an Underwater Robot Manipulator23 citations · 2010
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- 9Sensors for Robotics8 citations · 2013
- 10Sensors for Robotics 20157 citations · 2015