Jialin Cui

Papers

Total Citations

H-Index

About

Jialin Cui’s research lies at the intersection of robotics, automation, and intelligent systems, with a focus on developing practical algorithms for real-world applications. His work spans two key areas: deep reinforcement learning for autonomous navigation and computer vision for precision manufacturing. In his most cited paper, Cui introduces Re-DQN, a deep reinforcement learning-based algorithm for complete coverage path planning in lawn mowing robots. This work addresses a critical challenge in smart home and agricultural automation—reducing manual labor through efficient, autonomous operation. The algorithm’s innovative approach to comprehensive coverage has garnered attention as the field rapidly evolves. Earlier, Cui contributed to industrial automation with a quick computer vision algorithm for tracking welding lines in TIG welding pipes. By combining edge detection, binarization, and target segmentation, his method enhances precision in locating welding torches and lines. Though each paper holds 5 citations, their impact reflects foundational contributions to both service robotics and manufacturing. Cui’s ability to bridge theoretical advances with tangible engineering solutions marks him as a researcher dedicated to pushing the boundaries of autonomous systems.