Papers
13
Total Citations
168
H-Index
8
About
Shuo Pang is a robotics and autonomous systems researcher whose work centers on olfactory-based robot navigation, intelligent control, and unmanned vehicle technologies. He is perhaps best known for his pioneering contributions to odor source localization (OSL), a challenging problem in which mobile robots must trace chemical plumes in turbulent, unpredictable environments to locate an odor source — with applications ranging from gas leak detection to wildfire monitoring. Across a sustained research career spanning over a decade, Pang has developed increasingly sophisticated solutions to this problem, progressing from early reactive planning algorithms (2009) through fuzzy inference and adaptive bio-inspired methods to modern deep learning and reinforcement learning approaches, collectively earning over 130 citations in the field. His 2020 work integrating model-based reinforcement learning with fuzzy inference is his most impactful contribution, with 35 citations. Beyond olfaction, Pang has expanded into autonomous underwater vehicles, uncrewed aerial systems for wildfire monitoring, ocean energy-driven marine robots, and decentralized aquatic swarm systems, reflecting a broad commitment to advancing autonomous robotics across challenging real-world environments. His research bridges theoretical machine learning with rigorous physical experimentation, making his work especially valuable for engineers and roboticists tackling real-world autonomous navigation challenges.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3
- 4
- 5Reactive planning for olfactory-based mobile robots14 citations · 2009
- 6AUV Tunnel Tracking Method Based on Adaptive Model Predictive Control9 citations · 2018
- 7
- 8
- 9
- 10