Meiyuan Zou
Papers
3
Total Citations
10
H-Index
2
About
Meiyuan Zou is a robotics researcher focused on advancing autonomous systems for construction, exploration, and service applications. Their key research areas include multi-sensor fusion for safety-critical environments, autonomous mobile robot navigation, and robotic manipulation for healthcare logistics. Zou’s most cited work, “Active Pedestrian Detection for Excavator Robots based on Multi-Sensor Fusion” (2022, 6 citations), addresses a vital safety challenge by integrating sensor data to detect workers near heavy machinery, directly improving construction site safety. In “A Generalized Voronoi Diagram based Robot Exploration Method for Mobile Robots” (2022, 2 citations), Zou tackles the trap space problem in autonomous exploration, offering a more efficient frontier detection approach using Voronoi diagrams to guide RRT-based navigation through narrow corridors. Additionally, “An Efficient Medicine Identification and Delivery System based on Mobile Manipulation Robot” (2022, 2 citations) demonstrates practical impact by combining computer vision and robotic arms for automated pharmacy tasks. Though early in their career, Zou’s work bridges theoretical robotics with real-world deployment, earning recognition for enhancing both industrial safety and assistive technology. Their contributions are particularly relevant for researchers developing autonomous systems that must operate reliably in complex, human-shared environments.
Research Focus
Key Achievements
Top Papers
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