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About

Delu Zeng is a researcher whose work bridges the frontiers of computer vision, robotics, and intelligent control systems. His primary research areas include underwater image enhancement, robotic hand-eye calibration, and adaptive control for robotic manipulators. Zeng's most impactful contribution is his pioneering work on underwater image processing, particularly his 2020 paper on "Multi-scale enhancement fusion for underwater sea cucumber images based on human visual system modelling," which has garnered 36 citations—a testament to its significance in marine robotics and aquaculture applications. This work demonstrates his ability to integrate biological visual principles with computational methods to solve real-world challenges. In robotics, Zeng has made notable advances in calibration techniques, introducing a novel deep reinforcement learning approach for hand-to-eye calibration in his 2023 paper, which addresses the critical industrial need for precise robotic manipulation. He has also contributed to the theoretical foundations of tracking control for robotic systems, developing zeroing-gradient dynamics methods that enhance the stability and accuracy of industrial robots. His work on continuous self-adaptive calibration further showcases his commitment to creating robust, real-time solutions for evolving robotic environments. Through these contributions, Zeng has established himself as a versatile researcher advancing both the theoretical and applied aspects of intelligent robotic systems.