Papers
2
Total Citations
6
H-Index
2
About
Zhuqi Li is a researcher focused on industrial robotics and intelligent transportation systems, with key contributions in kinematic calibration and traffic safety analytics. Their work on "Kinematic Parameters Identification and Compensation of an Industrial Robot" (2019, 4 citations) addresses a critical challenge in manufacturing precision by developing methods to identify and correct robotic arm inaccuracies, enhancing automation reliability. More recently, Li has advanced into traffic safety with "Online Traffic Crash Risk Inference Method Using Detection Transformer and Support Vector Machine Optimized by Biomimetic Algorithm" (2024, 2 citations), which tackles the persistent challenge of estimating crash risks in urban environments. This innovative approach combines deep learning (Detection Transformer) with biomimetic optimization to enable real-time risk assessment, offering a novel framework to mitigate life-threatening and economic costs of traffic incidents. Li’s work bridges mechanical engineering and AI-driven safety solutions, demonstrating versatility in applying computational methods to both industrial and societal problems. Their research trajectory highlights a commitment to practical, high-impact applications, from improving robotic precision to enhancing urban traffic safety through cutting-edge machine learning techniques.
Research Focus
Key Achievements
Top Papers
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- 2