Benyue Su
Papers
1
Total Citations
5
H-Index
1
About
Benyue Su is a researcher whose work lies at the intersection of robotics, wireless sensor networks, and intelligent localization systems. His most-cited paper, "Indoor robot localization combining feature clustering with wireless sensor network" (2018), addresses a critical challenge in autonomous robotics: the absence of reliable GPS signals indoors. Su proposed a novel solution that integrates natural feature clustering with wireless sensor network data to enhance localization robustness, a problem long considered difficult in the field. With 5 citations, this work has contributed to advancing the reliability of indoor robot navigation, a key enabler for service robots in environments like warehouses, hospitals, and smart homes. Su’s research is particularly notable for its practical approach—combining sensor fusion and machine learning techniques to overcome real-world constraints. His contributions are valuable for students and researchers working on autonomous systems, sensor networks, or mobile robotics, offering a foundation for further innovation in robust, GPS-free localization.
Research Focus
Key Achievements
Top Papers
- 1