Zhiyun Deng

Papers

Total Citations

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About

Zhiyun Deng is a researcher whose work sits at the intersection of robotics, optimization algorithms, and intelligent manufacturing. Their primary research focuses on developing advanced path-planning methods for industrial robots, with a particular emphasis on improving efficiency in automated maintenance and repair systems. Deng’s most cited paper, “Path Optimization of Gluing Robot Based on Improved Genetic Algorithm” (2021, 8 citations), introduces a novel variant of the Traveling Salesman Problem tailored to address the challenge of rapidly repairing microdamage on worn conveyor belts. By leveraging acceleration characteristics of belt surfaces, Deng proposed an enhanced genetic algorithm that significantly reduces repair time and improves automation precision. This contribution is vital for industries relying on continuous material handling, where timely maintenance prevents costly downtime. Deng’s work demonstrates a keen ability to bridge theoretical optimization with practical robotic applications, offering scalable solutions for smart factories. Their research not only advances robotic autonomy but also provides a framework for integrating evolutionary algorithms into real-time industrial tasks. With growing relevance in the era of Industry 4.0, Deng’s innovations continue to inspire further exploration in adaptive robotic systems and maintenance automation.