Liang Yang

Shenyang Institute of Automation, City University of New York

Papers

Total Citations

357

H-Index

About

Liang Yang is a robotics and autonomous systems researcher whose work spans robot path planning, structural health monitoring, and subsurface infrastructure inspection. His most influential contribution, a 2016 survey on robot 3D path planning algorithms, has accumulated over 321 citations, establishing itself as a foundational reference in the field. This work systematically examined methods for computing optimal, collision-free trajectories in three-dimensional workspaces under real-world kinematic constraints, making it an essential resource for robotics researchers and engineers alike. Beyond path planning, Yang has applied autonomous systems to practical civil engineering challenges. His 2017 work on robotic inspection of concrete spalling and crack detection demonstrated how robotics can reduce human error and cost in structural health monitoring — a paper that has garnered 34 citations and reflects his commitment to bridging robotics with infrastructure maintenance. More recently, he has pushed into subsurface sensing, developing GPRNet, a deep learning-based system that reconstructs 3D models of underground utilities from sparse Ground Penetrating Radar measurements — advancing non-destructive evaluation beyond conventional image-based detection. Across his career, Yang's research consistently addresses real-world safety and efficiency challenges, positioning him as a versatile contributor at the intersection of robotics, computer vision, and intelligent infrastructure inspection.