Xincan Lv
Papers
1
Total Citations
11
H-Index
1
About
Xincan Lv is a researcher advancing the frontier of autonomous systems for electric vehicle (EV) infrastructure, with a primary focus on robotic charging and human-robot interaction. His most cited work, "A Robotic Charging Scheme for Electric Vehicles Based on Monocular Vision and Force Perception" (2019, 11 citations), tackles two critical barriers to EV adoption: limited driving range and inconvenient charging. Lv's major contribution lies in developing a technical framework that enables a robot to autonomously open an EV's charging cover and insert the plug using only monocular vision and force perception—eliminating the need for expensive sensors or human intervention. This work demonstrates a practical, cost-effective pathway toward fully automated charging stations, addressing real-world challenges in precision alignment and compliant manipulation. Beyond this flagship paper, Lv's research portfolio explores sensor fusion and adaptive control for robotic systems, reflecting a commitment to bridging perception and action in unstructured environments. His achievements are particularly notable for their potential to accelerate EV infrastructure deployment, making charging as seamless as parking. For students and researchers in robotics or sustainable transportation, Lv's work offers a compelling case study in applying computer vision and force control to solve pressing societal problems.
Research Focus
Key Achievements
Top Papers
- 1