Yingxin Yan

Papers

Total Citations

H-Index

About

Yingxin Yan is a researcher focused on advancing autonomous navigation and intelligent manufacturing through robotics and deep learning. Their primary research areas include mobile robot collision avoidance, deep reinforcement learning, and automated laser scanning systems. Yan’s most notable contribution is the development of a deep reinforcement learning method based on Double Deep Q-Network (DDQN) for mobile robot collision avoidance, which enables robots to autonomously learn navigation and obstacle avoidance by processing target positions and obstacle data. This work, published in 2019, has garnered 46 citations, reflecting its significance in the field of robotics and artificial intelligence. Additionally, Yan has explored automated laser scanning for industrial applications, proposing an intelligent system for scanning objects with unknown models to enhance efficiency in reverse engineering and quality control. While this work has received 2 citations, it underscores Yan’s commitment to practical, industry-oriented solutions. Yan’s research bridges the gap between theoretical reinforcement learning and real-world robotic applications, offering valuable insights for students and researchers interested in autonomous systems, intelligent manufacturing, and human-robot interaction. Their work continues to inspire advancements in safe, efficient robot navigation.