Hongzhong Tang
Papers
2
Total Citations
9
H-Index
2
About
Hongzhong Tang is a researcher focused on advancing the intelligence of autonomous cleaning robots, particularly through innovative decision-making systems. His work centers on developing algorithms that enable robots to perceive their environment and adapt their cleaning strategies in real time. Tang’s major contributions include pioneering the use of curriculum learning strategies and feedback networks to optimize multi-mode operations, allowing robots to learn progressively and improve efficiency. His most-cited paper, "The multi-mode operation decision of cleaning robot based on curriculum learning strategy and feedback network" (2022, 7 citations), demonstrates how robots can autonomously select cleaning modes based on task complexity. More recently, his 2024 paper introduces a lightweight operation mode decision method driven by garbage attributes perception, achieving enhanced cleaning performance with reduced computational overhead. This work has been recognized as a promising solution for practical deployment. With a growing citation impact, Tang’s research bridges machine learning and robotics, offering scalable, intelligent solutions for household and industrial cleaning applications.
Research Focus
Key Achievements
Top Papers
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