Papers
10
Total Citations
55
H-Index
5
About
Xiem HoangVan is an emerging robotics and intelligent systems researcher whose work spans autonomous navigation, control theory, computer vision, and human-robot interaction. His research addresses some of the most pressing challenges in modern robotics, from designing robust controllers for unstable platforms to enabling robots to perceive and navigate complex real-world environments. Among his most recognized contributions is his development of an adaptive nonlinear PD controller for two-wheeled self-balancing robots, which has garnered 13 citations since its 2024 publication, demonstrating strong early impact in the control systems community. His multisensor data fusion framework for reliable obstacle avoidance, combining LiDAR and depth cameras, reflects his commitment to building perception systems resilient enough for practical deployment. HoangVan has also advanced UAV autonomy through object-oriented semantic mapping and contributed to socially aware mobile robot navigation by integrating motion prediction with trajectory planning. His work extends into industrial machine vision, where he has developed innovative monocular 3D object localization methods using deep learning, and into underwater robotics, exploring image enhancement for depth estimation in challenging aquatic environments. Collectively accumulating over 55 citations, HoangVan's portfolio positions him as a versatile and productive voice in next-generation autonomous systems research.
Research Focus
Key Achievements
Top Papers
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- 2Multisensor Data Fusion for Reliable Obstacle Avoidance8 citations · 2022
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- 4Object-Oriented Semantic Mapping for Reliable UAVs Navigation7 citations · 2023
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