Max Robotics — Global Service Robot Directory + US Certification

About

Pengpeng Chen’s research lies at the intersection of intelligent robotics, sensor fusion, and industrial automation, with a strong focus on solving real-world challenges in mining and autonomous navigation. His most impactful work introduces a novel rail inspection robot and fault detection method for coal mine hoisting systems—a critical contribution to mine safety that has earned 24 citations. Chen also pioneers depth estimation through visual-LiDAR fusion, exemplified by his “Motion inspires notion” framework and the LeoVR system, which integrate camera and radar data for applications in autonomous driving and environmental perception. Further demonstrating his versatility, he has developed neural learning algorithms for mobile robot obstacle avoidance and explored swarm robotics for rescue tasks. His recent work tackles the persistent non-line-of-sight (NLOS) localization problem for coal mine robots using edge-assisted semi-supervised learning, pushing the boundaries of reliable positioning in complex underground environments. With a publication record spanning from 2013 to 2025, Chen’s contributions are steadily gaining recognition, and his research continues to bridge the gap between theoretical innovation and practical deployment in safety-critical and autonomous systems.