Papers
4
Total Citations
13
H-Index
3
About
Junqi Yu is a researcher advancing autonomous navigation for specialized robots in complex, real-world environments. Their work focuses on two critical domains: airport energy stations and large-scale public building construction sites. Yu’s key contributions lie in developing optimized path planning and SLAM (Simultaneous Localization and Mapping) algorithms tailored to these challenging settings. Notably, their 2023 paper on a “Global path planning for airport energy station inspection robots based on improved grey wolf optimization algorithm” (5 citations) addresses the intricate layout of airport energy stations, where narrow areas and dense equipment demand efficient, collision-free routes. More recently, Yu has tackled the unstructured, uneven terrain of construction sites, proposing an improved A* algorithm for global path planning (3 citations) and evaluating LiDAR SLAM algorithms for construction robots (4 citations). Their 2025 work on LiDAR SLAM for safety inspection robots in large public building sites (1 citation) targets critical issues like point-cloud drift and z-axis errors. Through these studies, Yu is systematically enhancing the reliability and efficiency of autonomous inspection and construction robots, laying essential groundwork for safer, more automated infrastructure management.
Research Focus
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Top Papers
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