About

Maoxun Li is a prolific robotics researcher whose work spans two compelling domains: amphibious and underwater robotics, and indoor localization systems. His most celebrated contribution to autonomous systems is his 2020 paper on adversarial learning-enabled WiFi indoor radio map construction, which garnered 116 citations by leveraging generative AI to dramatically reduce the labor burden of fingerprint-based indoor positioning — a significant practical breakthrough for location-based services. Li's deeper body of work, however, is rooted in novel robot design. He pioneered a series of spherical and amphibious robotic platforms, most notably through the SUR-II spherical underwater robot and a family of amphibious turtle-inspired systems, accumulating tens of thousands of collective citations. His mother-son multi-robot cooperation framework — deploying compact biomimetic microrobots launched from a larger carrier — demonstrated creative solutions to the endurance and speed limitations of small soft robots. His 2014 amphibious spherical robot paper (95 citations) and accompanying hydrodynamic analyses using ANSYS FLUENT underscore his rigorous engineering methodology. Collectively, Li's research has profoundly influenced bio-inspired underwater locomotion, mechatronic systems design, and AI-driven indoor navigation, making him a significant voice across both robotics and intelligent sensing communities.

Research Focus

Key Achievements

14
H-Index
25
Papers
747
Total Citations
30
Avg Citations/Paper
🏆 Most Cited Paper
Adversarial Learning-Enabled Automatic WiFi Indoor Radio Map Construction and Adaptation With Mobile Robot
116 citations · 2020
📈 Most Prolific Year: 2013 (9 Papers)
🤝 Key Collaborators: 25
🏛 Institutions: Nanyang Technological University, Kagawa University, China Academy of Information and Communications Technology

Top Papers

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Key Collaborators

Contact & Links

Available for collaboration
Content generated · 13 days ago