Yu-hang Zhang

Papers

Total Citations

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About

Dr. Yu-hang Zhang is a researcher focused on intelligent robotics and optimization algorithms, with a particular emphasis on automated maintenance systems. Their most-cited work, "Path Optimization of Gluing Robot Based on Improved Genetic Algorithm" (2021, 8 citations), addresses a critical challenge in industrial robotics: the rapid, autonomous repair of microdamage on worn conveyor belt surfaces. By reformulating the gluing robot’s path planning as a novel variant of the Traveling Salesman Problem (TSP), Zhang introduced an enhanced genetic algorithm that optimizes traversal efficiency, enabling timely and precise maintenance. This contribution bridges theoretical optimization with practical automation, offering a scalable solution for reducing downtime in manufacturing environments. Zhang’s research demonstrates a keen ability to identify real-world industrial pain points—such as the acceleration-induced wear on belt surfaces—and translate them into computationally tractable problems. While their citation count reflects an emerging career, the work’s direct applicability to robotics and maintenance automation signals growing influence. For students and researchers, Zhang’s approach exemplifies how classic algorithms can be innovatively adapted to solve pressing engineering challenges, making their profile a compelling study in applied optimization and robotic intelligence.