Papers
3
Total Citations
29
H-Index
3
About
Xiaofeng Lin is a robotics and autonomous systems researcher whose work spans mobile robot control, multi-agent coordination, and aerial vehicle autonomy. His early contributions focused on intelligent control strategies for wheeled mobile robots, most notably a nonlinear predictive control framework leveraging extreme learning machines to address path-tracking challenges under external disturbances — a paper that has garnered 16 citations and remains a foundational reference in robot motion control. Lin's research has since expanded into the frontier of multi-robot systems operating in complex, uncertain environments. His work on bandit submodular maximization (7 citations) advances the theoretical underpinnings of multi-agent coordination in unpredictable and partially observable settings, addressing a critical challenge for next-generation autonomous systems. More recently, Lin has tackled cutting-edge problems in aerial robotics, developing a real-to-sim-to-real pipeline for vision-based MAV-catching-MAV tasks — enabling micro aerial vehicles to autonomously detect, localize, and intercept other UAVs in real-world conditions. With contributions bridging machine learning, control theory, and computer vision, Lin's body of work reflects a commitment to making autonomous robotic systems more intelligent, adaptive, and deployable across dynamic real-world environments.
Research Focus
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