Sanghak Lee
Papers
2
Total Citations
22
H-Index
2
About
Sanghak Lee is a pioneering researcher in the field of construction robotics, with a primary focus on the automation and intelligent control of heavy machinery, particularly excavators. His work addresses the critical challenge of enabling robotic excavators to operate autonomously in complex, unstructured environments. Lee’s major contributions include the development of neural network-based soil models for optimal path generation, a breakthrough that simultaneously considers bucket volume, structural reachability, and time efficiency to automate excavation tasks. This foundational work, published in 2008, has garnered 15 citations and remains a key reference in the field. He further advanced the discipline by introducing a recurrent neural network for real-time obstacle avoidance, solving the computational bottlenecks of traditional pseudo-inverse methods. This 2008 paper, with 7 citations, demonstrates his commitment to practical, on-line applications. Lee’s research is notable for bridging theoretical neural network algorithms with the demanding physical constraints of construction equipment, laying the groundwork for safer and more efficient autonomous excavation systems. His work continues to inspire students and researchers in robotics, control systems, and construction automation.
Research Focus
Key Achievements
Top Papers
- 1Optimal path generation for excavator with neural networks based soil models15 citations · 2008
- 2