Jiongkun Yang

Papers

Total Citations

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About

Jiongkun Yang is a researcher specializing in intelligent robotics and embedded AI systems, with a focus on autonomous navigation, computer vision, and human-robot interaction. His work bridges the gap between theoretical neural network models and practical robotic applications. In his most cited paper (2021, 3 citations), Yang developed a monocular vision manipulator positioning system using an EdgeBoard embedded AI card and the Paddle-Lite framework, enabling real-time target detection and spatial positioning—a critical contribution to low-cost, high-efficiency robotic vision. His second major work (2022, 2 citations) presents an intelligent disinfection robot that leverages the Robot Operating System (ROS) and Gmapping algorithm for autonomous map-building and voice-activated area disinfection, demonstrating a practical solution for public health automation. Though his citation numbers are modest, Yang’s contributions are notable for their applied engineering focus: integrating open-source architectures with edge computing to create deployable robotic systems. His research is particularly relevant for students and engineers interested in embedded AI, ROS-based robotics, and cost-effective automation solutions for real-world challenges.