Papers
12
Total Citations
190
H-Index
7
About
Xiaoli Li is a leading researcher in intelligent robotics and computational intelligence, with a career spanning over two decades of pioneering work in autonomous mobile robot control. Her primary research areas include adaptive control systems, neural network-based robot navigation, and hybrid force/position control for obstacle avoidance. Li's most impactful contribution is her 2011 work on Elman fuzzy adaptive control for mobile robot obstacle avoidance, which has garnered 70 citations and introduced a novel virtual force field approach combined with online learning methods. She has also made significant advances in end-point contact force control using quantitative feedback theory (32 citations) and real-time self-reaction mechanisms in unknown environments using fuzzy neural networks (26 citations). Her 2007 introduction to computational intelligence techniques for robot control remains a valuable resource for researchers, synthesizing neural computation, evolutionary algorithms, and fuzzy logic for industrial applications. More recently, Li has expanded into neurobiologically inspired robotics, proposing the StereoNeuroBayesSLAM system for visual simultaneous localization and mapping, and investigating entorhinal-hippocampal interactions for spatial representation. Her work consistently bridges theoretical control theory with practical robotic applications, achieving over 180 total citations across her most-cited publications.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3
- 4
- 5An introduction to computational intelligence techniques for robot control12 citations · 2007
- 6
- 7
- 8
- 9
- 10