Papers
1
Total Citations
5
H-Index
1
About
Yong Wan is a robotics researcher whose work focuses on advancing autonomous navigation in challenging environments. His primary research areas include mobile robot localization, semantic mapping, and adaptive algorithms for large-scale, visually ambiguous settings. Wan’s major contribution lies in addressing the critical limitations of the adaptive Monte Carlo localization (AMCL) algorithm, which often fails in large scenes or environments with repetitive features. By integrating semantic information—such as object recognition and contextual cues—into the localization pipeline, he developed a robust method that significantly improves robot positioning accuracy and reliability in such difficult conditions. His most cited paper, “Research on rapid location method of mobile robot based on semantic grid map in large scene similar environment” (2022), has garnered 5 citations, reflecting its relevance to ongoing challenges in field robotics. This work demonstrates his ability to bridge theoretical algorithms with practical, real-world applications, making autonomous systems more dependable for tasks like warehouse logistics, search-and-rescue, and autonomous driving. Wan’s research is particularly valuable for students and engineers seeking to enhance robot autonomy in complex, unstructured spaces.
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Top Papers
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