Khan Raqib Mahmud
Louisiana Tech University, University of Liberal Arts Bangladesh
Papers
6
Total Citations
28
H-Index
3
About
Khan Raqib Mahmud is at the forefront of robotic odor source localization (OSL), pioneering multi-modal approaches that fuse vision, olfaction, and large language models (LLMs) to enable autonomous agents to locate odor sources in unknown environments. His most impactful work, the "Robotic Odor Source Localization via Vision and Olfaction Fusion Navigation Algorithm" (2024, 13 citations), introduces a novel navigation algorithm that integrates visual and olfactory sensor data, overcoming the limitations of traditional single-sensor methods. Mahmud further advances the field with his knowledge-driven framework (2025, 6 citations) and multi-modal LLM integration (2024, 3 citations), which leverage AI to interpret complex environmental cues and guide robots more intelligently. He has also contributed to practical robotics, developing an obstacle-avoiding fire extinguishing robot (2020) and customizing Turtlebot3 platforms for OSL tasks (2023). With a growing citation count and a clear trajectory toward intelligent, autonomous navigation, Mahmud’s work is shaping the next generation of search-and-rescue, environmental monitoring, and industrial safety robots.
Research Focus
Key Achievements
Top Papers
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- 5Multi-Modal Robotic Platform Development for Odor Source Localization2 citations · 2023
- 6