Thai-Viet Dang
Papers
14
Total Citations
197
H-Index
7
About
Thai-Viet Dang is a rising leader in autonomous mobile robotics and computer vision, whose work is reshaping how robots perceive and navigate dynamic environments. His research centers on semantic segmentation, obstacle avoidance, and path planning for mobile robots, with a strong emphasis on lightweight, real-time models deployable on monocular cameras. Dang’s most impactful contribution is the development of multi-scale fully convolutional networks for semantic segmentation, enabling robots to distinguish obstacles like walls and pillars from moving objects—a paper that has garnered 63 citations since 2023. He further advanced the field with the IRDC-Net, a lightweight segmentation network, and the hybrid Safe JBS-A*B algorithm combined with improved DWA for global and local path planning. His work on knowledge distillation (KD-SegNet) and ultra-fast segmentation models pushes the frontier of efficient, deployable AI for autonomous mobile robots. With over 150 total citations across his top papers, Dang has also created a comprehensive RGB-D dataset for 6D pose estimation in industrial pick-and-place applications, validated in real-world settings. His research is essential reading for anyone working on vision-based navigation, offering practical solutions that balance accuracy, speed, and computational efficiency.
Research Focus
Key Achievements
Top Papers
- 1
- 2Obstacle Avoidance Strategy for Mobile Robot Based on Monocular Camera38 citations · 2023
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- 6Optimal Navigation Based on Improved A* Algorithm for Mobile Robot9 citations · 2023
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- 10An Ultra Fast Semantic Segmentation Model for AMR’s Path Planning6 citations · 2023