Xiaohan Lv
Papers
2
Total Citations
23
H-Index
2
About
Xiaohan Lv is a robotics researcher whose work focuses on intelligent control, trajectory planning, and multi-robot coordination. Their major contributions include developing a Q-learning trajectory planning method for humanoid manipulators based on a Takagi–Sugeno fuzzy parallel distributed compensation structure, which significantly improves tracking accuracy and stability—a critical advance for platforms like the NAO robot widely used in education and research. Lv also proposed a cooperative simultaneous localization and mapping (SLAM) algorithm using a distributed particle filter, enabling multiple robots to collaboratively build maps and localize themselves in unknown environments. This work, with over 20 combined citations from their two most-cited papers, has influenced the fields of humanoid robotics and multi-agent systems. By integrating reinforcement learning with fuzzy control and distributed estimation, Lv has addressed fundamental challenges in autonomous robot operation, making their research highly relevant for students and engineers working on intelligent robotic systems and swarm robotics.
Research Focus
Key Achievements
Top Papers
- 1
- 2