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About

Nanmu Hui is a rising researcher in robotics and autonomous systems, with a focus on underwater and wall-climbing robots, as well as robotic manipulator control. Their work centers on advancing autonomous vehicle navigation, trajectory planning, and dynamic modeling to enhance precision and safety in complex environments. Hui’s most cited paper, “System Identification and Controller Design of a Novel Autonomous Underwater Vehicle” (2021, 21 citations), contributes to the reliable operation of underwater structures by improving vehicle stability and accuracy. They further developed an improved Fast Marching Tree algorithm for robotic arm obstacle avoidance and singularity prevention (2024, 10 citations), addressing critical challenges in manipulation tasks. More recently, Hui introduced a hybrid Broad Learning System for vibration suppression in manipulator dynamics (2025, 3 citations) and a multi-sensor fusion localization method for wall-climbing robots (2025, 2 citations), showcasing their versatility across robotic domains. Their work bridges theoretical modeling with practical applications, offering solutions for industrial inspection and automation. With a growing citation record and innovative approaches to sensor fusion and control, Hui is establishing a reputation for tackling real-world robotic challenges.