Papers
3
Total Citations
22
H-Index
2
About
Yufan Li is a researcher whose work spans robotics, autonomous navigation, and the intersection of technology with art. With a focus on intelligent systems and biomimetic robotics, Li has made notable contributions to advancing mobile robot capabilities in complex environments. Most prominently, Li developed LN-D3QN, an enhanced deep reinforcement learning algorithm that addresses critical limitations in traditional D3QN-based navigation—specifically sparse reward signals and slow neural network training—offering more efficient and reliable indoor obstacle avoidance for mobile robots. This work has garnered 13 citations since its 2022 publication, establishing Li as a contributor to the growing field of AI-driven robotics. Li's research extends into biologically inspired systems, with a 2024 study examining the hydrodynamic performance of a self-propelled robotic fish navigating pipeline environments, reflecting a broader curiosity about how nature can inform machine design. Perhaps most intriguingly, Li has also engaged with creative technology, contributing scholarly analysis of an interactive robotic art installation that merges human intimacy, celestial phenomena, and light painting. Together, these works reveal a researcher who bridges rigorous engineering with imaginative, interdisciplinary inquiry.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3