Haiqing Hong
Papers
1
Total Citations
4
H-Index
1
About
Haiqing Hong is a researcher at the forefront of intelligent robotics and embedded AI systems, with a primary focus on real-time visual grasping control and FPGA-based deep learning acceleration. His most-cited work, "FPGA-based Deep Learning Acceleration for Visual Grasping Control of Manipulator" (2021), addresses a critical challenge in mobile robotics: achieving fast, low-power, and high-precision visual recognition for manipulator control. By leveraging FPGA architectures to accelerate deep neural networks, Hong’s research enables efficient, real-time object detection and grasping in resource-constrained environments—a vital contribution for industrial automation and autonomous systems. With 4 citations to date, this work has laid groundwork for integrating hardware acceleration with vision-based robotic control. Hong’s achievements highlight his expertise in bridging algorithm design and hardware implementation, offering practical solutions for intelligent production lines. His research is particularly valuable for students and engineers exploring the intersection of deep learning, embedded systems, and robotics, demonstrating how optimized hardware can unlock new capabilities in real-world robotic applications.
Research Focus
Key Achievements
Top Papers
- 1