Papers
6
Total Citations
169
H-Index
5
About
Yingjun Li is a leading researcher in intelligent sensing and robotic systems, with a primary focus on piezoelectric six-dimensional force/torque sensors and machine vision for industrial automation. Their most significant contributions lie in advancing the accuracy and reliability of multi-axis force measurement for heavy-load robotic manipulators. Li’s work on static decoupling algorithms using LSSVR fusion and BP neural networks has been pivotal in overcoming the nonlinearity challenges that have long hindered the development of high-performance six-dimensional force sensors in China. Their 2012 study on dynamic characteristics of piezoelectric sensors for large-load robots (51 citations) and the 2018 decoupling algorithm paper (61 citations) are foundational references in the field. Li also introduced a fault-tolerant measurement mechanism for pre-tightened four-point supported sensors (34 citations), enhancing sensor robustness. Beyond force sensing, Li has applied machine vision to automate steel plate surface defect detection and grinding path planning, addressing critical inefficiencies in manufacturing. This work, cited 15 times, demonstrates a commitment to practical, industry-relevant solutions. Through these contributions, Li has established a strong impact in sensor design and intelligent manufacturing.
Research Focus
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