Deli Zhang

Nanjing University of Aeronautics and Astronautics

Papers

Total Citations

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About

Deli Zhang is a robotics researcher whose work focuses on enhancing the performance and reliability of robotic systems through advanced computation and deep learning. Her key research areas include visual servoing, motion prediction, and hardware-accelerated robotics. In her most cited work (2024, 10 citations), Zhang investigates image-based visual servoing systems, integrating deep learning for object detection and time series prediction to improve target capture and anticipate robotic arm motion trajectories. This research directly addresses critical challenges in real-time robotic control and system responsiveness. Additionally, Zhang has made notable contributions to computational efficiency in robotics through her 2023 study on solving robot kinematic equations using FPGA-based parallel processing. This work tackles the problem of CPU overload in complex motion control, proposing a method that significantly improves processing speed and system responsiveness. Her research bridges the gap between theoretical robotics and practical implementation, offering solutions that enhance both the intelligence and computational efficiency of robotic systems. Zhang’s work is particularly valuable for researchers and engineers developing high-performance robotic applications requiring real-time adaptability and precision.