Chenglei Fang

Papers

Total Citations

H-Index

About

Chenglei Fang is a researcher focused on mobile robotics and human-robot interaction, with particular expertise in developing low-complexity, real-time tracking systems. His most cited work, "People Following System Based on LRF" (2018, 3 citations), introduces a robust target-following framework for differential-driven mobile robots using Laser Range Finders (LRF). The key innovation lies in employing a dynamic threshold method to achieve reliable person tracking with minimal computational overhead—a critical advancement for resource-constrained robotic platforms. This work demonstrates Fang's ability to balance algorithmic simplicity with practical effectiveness, addressing the fundamental challenge of autonomous human following in dynamic environments. While his citation count reflects an early-career stage, the research contributes meaningfully to the broader field of assistive and service robotics, where efficient sensor processing is paramount. Fang's approach offers a scalable solution for applications ranging from warehouse logistics to personal companion robots, highlighting his potential for future contributions in intelligent autonomous systems.