Van Nguyen Thi Thanh
Papers
5
Total Citations
51
H-Index
4
About
Van Nguyen Thi Thanh is a robotics and autonomous systems researcher whose work sits at the intersection of mobile robotics, deep reinforcement learning, and intelligent navigation. Her research focuses primarily on autonomous robot navigation, simultaneous localization and mapping (SLAM), and sensor fusion, with a growing emphasis on practical deployment in real-world environments. Her most influential contribution, "Autonomous Navigation for Omnidirectional Robot Based on Deep Reinforcement Learning" (2020, 22 citations), demonstrated how learning-based approaches could meaningfully advance self-directed robot movement, while her earlier work on ROS-based omnidirectional robot mapping (2019, 14 citations) established a strong foundation in classical navigation frameworks. More recently, she has pushed boundaries in 3D indoor mapping through sensor fusion techniques that improve both speed and accuracy of RTAB-Map implementations, and developed an enhanced sampling-based exploration strategy optimized for aerial and ground robots alike. Her applied research extends into industrial automation, including a vision system improving the reliability of pick-and-place robots in smartphone camera module testing. With over 50 cumulative citations, Thanh's body of work reflects a consistent drive to bridge theoretical robotics research with tangible engineering solutions.
Research Focus
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Top Papers
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