Zongwei Yao
Papers
2
Total Citations
67
H-Index
2
About
Zongwei Yao is a leading researcher in robotic excavation and intelligent mining systems, with a focus on optimizing heavy machinery operations through computational methods. His work centers on trajectory planning for cable shovels, a critical area for improving efficiency and energy savings in open-pit mining. In his highly cited 2020 paper, Yao introduced a multi-objective genetic algorithm to optimize digging trajectories, enabling robotic excavators to achieve more effective and energy-efficient cycles—a foundational contribution to autonomous mining. This work has garnered 43 citations, reflecting its impact on both robotics and mining engineering. He further advanced the field with a deep learning-based approach to predict piled-up status and payload distribution of bulk materials (24 citations), demonstrating his ability to integrate AI with real-world industrial challenges. Yao’s research bridges the gap between theoretical optimization and practical deployment, offering scalable solutions for the mining industry’s push toward automation. His achievements highlight a commitment to transforming earth-moving equipment into intelligent, adaptive systems, making him a key figure in the evolution of robotic excavation.
Research Focus
Key Achievements
Top Papers
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