Papers
2
Total Citations
41
H-Index
2
About
Lingxiao Wang is a researcher specializing in robotic navigation, olfactory sensing, and autonomous systems, with a particular focus on the intersection of artificial intelligence and mobile robotics. His most influential contribution, "Olfactory-Based Navigation via Model-Based Reinforcement Learning and Fuzzy Inference Methods" (2020), has garnered 35 citations and represents a significant advancement in odor source localization technology. In this work, Wang and colleagues developed a novel algorithm enabling mobile robots to locate odor sources within turbulent flow environments by framing the challenge as a reinforcement learning problem, elegantly incorporating belief state estimation and fuzzy inference to handle the inherent uncertainty of real-world olfactory navigation. This work bridges probabilistic reasoning with adaptive control in a practically meaningful way. More recently, Wang has expanded his research horizon by integrating large language models into robotic olfaction, as demonstrated in his 2025 paper introducing a knowledge-driven framework for odor source localization — a forward-looking contribution that reflects the growing role of foundation models in robotics. Together, his body of work positions him as a pioneering voice in intelligent robotic perception, pushing boundaries at the confluence of sensory AI, reinforcement learning, and autonomous navigation.
Research Focus
Key Achievements
Top Papers
- 1
- 2