Guanghua Nie

Papers

Total Citations

H-Index

About

Guanghua Nie is a researcher focused on intelligent robotics and automation, with particular expertise in deep learning, machine vision, and control systems for industrial applications. His work centers on enhancing robotic performance in complex environments, especially in coal mining and manufacturing settings. Nie's most cited paper, "Optimization of Sorting Robot Control System Based on Deep Learning and Machine Vision" (2022, 15 citations), introduces a dynamic domain fuzzy self-tuning PID controller to improve the precision of coal gangue dry separation robots—a critical advancement for automating hazardous tasks in coal washing plants. His second notable work, "Motion Route Planning and Obstacle Avoidance Method for Mobile Robot Based on Deep Learning" (2022, 3 citations), explores deep learning-driven path planning and obstacle avoidance, addressing key challenges in mobile robot navigation under real-world constraints. While his citation counts are modest, Nie's contributions are significant for their practical impact on industrial automation, offering scalable solutions that replace manual labor with intelligent robotic systems. His research bridges theoretical deep learning models with tangible engineering outcomes, making him a valuable voice in the advancement of smart manufacturing and autonomous robotics.