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About

Sunzid Hassan is an emerging researcher specializing in robotic odor source localization (OSL), autonomous navigation, and multi-modal sensing systems. His work sits at the compelling intersection of robotics, artificial intelligence, and sensory fusion, addressing the challenge of enabling mobile robots to detect and navigate toward odor sources in unknown environments. Hassan's most significant contribution, "Robotic Odor Source Localization via Vision and Olfaction Fusion Navigation Algorithm" (2024), has garnered 13 citations and advances traditional OSL approaches by fusing visual and olfactory sensor data into a cohesive navigation framework. Building on this foundation, he has pioneered the integration of large language models into OSL systems, with his knowledge-driven LLM framework (2025) and multi-modal LLM approach (2024) reflecting a forward-thinking embrace of generative AI in robotics. His earlier work on customizing the TurtleBot3 platform for OSL tasks demonstrates a strong commitment to practical, deployable solutions. With over 26 cumulative citations across a concise but focused body of work, Hassan is establishing himself as a notable voice in intelligent robotic sensing, with promising implications for applications in environmental monitoring, search-and-rescue operations, and industrial safety.