Zhonghua Huang

About

Dr. Zhonghua Huang is a pioneering researcher at the intersection of robotics, artificial intelligence, and sustainable manufacturing. His primary research areas include mobile robot path planning, deep learning for autonomous systems, and industrial robotics for green development. Dr. Huang’s most influential contribution is his work on path planning for mobile robots using improved genetic algorithms, which addresses critical challenges such as slow convergence and local optima—his 2020 paper on this topic has garnered 53 citations, reflecting its significant impact on the field. He has also advanced Lidar detection in degraded visual environments (e.g., smoke and fog) using deep learning, a vital contribution to autonomous vehicle safety. More recently, Dr. Huang has explored the role of industrial robots in promoting environmental sustainability, with his 2025 study on green development in China’s manufacturing sector already attracting attention. His additional work includes optimizing flexspline structures for harmonic drives, demonstrating versatility across mechanical design. With a growing citation record and a focus on practical, real-world applications, Dr. Huang is shaping the future of intelligent robotics and eco-friendly automation.

Research Focus

Key Achievements

Top Papers

1. 1
   Path planning of mobile robot based on improved genetic algorithm

   53 citations · 2020
2. 2
   Deep Learning Method on Target Echo Signal Recognition for Obscurant Penetrating Lidar Detection in Degraded Visual Environments

   17 citations · 2020
3. 3
   Harnessing the power of industrial robots for green development: Evidence from China's manufacturing industry

   9 citations · 2025
4. 4
   Research status of mobile robot path planning based on genetic algorithm

   6 citations · 2020
5. 5
   Optimal design of flexspline structure based on approximation model

   5 citations · 2021

Key Collaborators

Contact & Links