Mingzhi Chen

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About

Mingzhi Chen is a leading researcher in autonomous underwater vehicle (AUV) systems and intelligent robotics, with a focus on real-time navigation, task allocation, and bio-inspired control algorithms. His most influential work, "Real-time path planning for a robot to track a fast moving target based on improved Glasius bio-inspired neural networks" (2019), has garnered 18 citations, demonstrating its impact on dynamic target tracking and neural network-based path planning. Chen's research addresses critical challenges in multi-AUV coordination, as seen in his study "Multi-AUV SOM Task Allocation Considering Initial Orientation of AUV" (2018), which introduces self-organizing map techniques to optimize mission efficiency. His contributions span computational fluid dynamics, variable structure systems, and position control, advancing the autonomy and reliability of underwater robotic fleets. Chen's work is particularly notable for integrating bio-inspired approaches with practical engineering constraints, offering scalable solutions for marine exploration and defense applications. His research continues to influence the development of robust, real-time control systems for complex underwater environments.