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About

Zhoujingzi Qiu is a robotics and control systems researcher whose work centers on visual servoing, model predictive control, and adaptive neural network methodologies for robotic manipulation. With a growing body of influential publications, Qiu has made notable contributions to the challenge of controlling robot manipulators in uncalibrated environments — scenarios where camera parameters and 3D feature positions are unknown, conditions that present significant real-world complexity. Qiu's most cited work, published in 2018 with 32 citations, introduced a pioneering model predictive control framework for constrained image-based visual servoing (IBVS) in uncalibrated settings. This was extended in 2019 (25 citations) to incorporate full robot dynamics while eliminating the need for joint velocity measurements — a meaningful practical advancement. A 2022 study (13 citations) further broadened the approach by integrating adaptive neural networks to handle unknown system dynamics and external disturbances across multiple camera configurations. Beyond manipulation control, Qiu has demonstrated interdisciplinary range through work on a bio-inspired tensegrity spine robot designed for active space debris capture, reflecting an interest in novel structural robotics. Collectively, Qiu's research addresses critical gaps between theoretical control design and deployable robotic systems, making it highly relevant to researchers working in autonomous robotics, space applications, and vision-based control.