Shouwan Gao
Papers
1
Total Citations
2
H-Index
1
About
Shouwan Gao is a leading researcher in intelligent mining and robotic localization, with a focus on overcoming the non-line-of-sight (NLOS) challenges that plague autonomous systems in underground environments. Their most-cited work, "Achieving Cross-Domain NLOS Localization via Edge-Assisted Semi-Supervised Learning" (2025), introduces a novel framework that leverages edge computing and semi-supervised learning to maintain high-precision positioning for coal mine robots (CMRs) even when direct line-of-sight is obstructed. This contribution directly addresses a critical bottleneck in the deployment of autonomous systems in complex, hazardous industrial settings. With 2 citations in a short time since publication, the paper is already gaining attention for its practical impact on safety and efficiency in intelligent mines. Gao’s research bridges the gap between theoretical machine learning and real-world industrial robotics, offering scalable solutions that reduce accuracy degradation in NLOS conditions. Their work is essential reading for researchers in robotic localization, edge AI, and mining automation, positioning them as a rising voice in the quest to make underground operations safer and more autonomous.
Research Focus
Key Achievements
Top Papers
- 1