Qiushi Bi
Papers
6
Total Citations
225
H-Index
6
About
Qiushi Bi is a leading researcher at the intersection of robotic excavation and wall-climbing robotics, making significant strides in both mining automation and high-altitude operations. Bi’s work on cable shovel automation has redefined efficiency in open-pit mining. By developing a multi-objective genetic algorithm for digging trajectory optimization, Bi’s 2020 paper (43 citations) provides a foundational method for achieving energy-saving, effective robotic excavation. Complementing this, Bi’s experimental and DEM simulation studies on digging force and power consumption (19 citations) offer critical insights for equipment reliability. In parallel, Bi has advanced safety technology through wall-climbing robots, with a comprehensive 2022 review (115 citations) serving as a key reference in the field. Bi’s innovative use of CFD and kriging models to optimize impeller design (17 citations) and the development of multi-body dynamics models for crawler robots (7 citations) demonstrate a deep commitment to solving real-world challenges. Further extending this work, Bi’s deep learning approach to predicting bulk material piled-up status (24 citations) showcases a versatile application of AI in heavy machinery. With over 225 total citations, Qiushi Bi’s research is shaping the future of autonomous, efficient, and safe robotic systems in demanding industrial environments.
Research Focus
Key Achievements
Top Papers
- 1Design and Technical Development of Wall-Climbing Robots: A Review115 citations · 2022
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- 6Multi-body dynamics model of crawler wall-climbing robot7 citations · 2022