Hoai-Linh Tran

Hanoi University of Science and Technology

Papers

Total Citations

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About

Dr. Hoai-Linh Tran is a rising researcher at the intersection of computer vision and autonomous mobile robotics, with a primary focus on real-time semantic segmentation for robotic navigation. His most impactful work, "An Ultra Fast Semantic Segmentation Model for AMR’s Path Planning" (2023), addresses a critical bottleneck in autonomous mobile robot (AMR) systems: the need for rapid, accurate scene understanding to enable safe obstacle avoidance and efficient path planning. By proposing a lightweight yet powerful segmentation model, Tran’s research demonstrates how deep learning can be optimized for on-board, low-latency deployment—a key requirement for real-world robotics. This contribution has already garnered 6 citations in its first year, signaling strong interest from both the robotics and embedded vision communities. Tran’s work bridges the gap between high-level perception and practical robotic control, offering a solution that reduces sensor complexity while maintaining navigational safety. His research is particularly relevant for warehouse logistics, service robots, and autonomous vehicles, where split-second decisions are critical. As a young investigator, Tran is establishing himself as a key voice in efficient deep learning for robotics, with potential for significant future impact.