Qiangqiang Guo
Papers
1
Total Citations
12
H-Index
1
About
Qiangqiang Guo is a researcher at the forefront of intelligent construction machinery, with a primary focus on automated excavation and robotic manipulation. His work addresses the critical challenge of enabling heavy equipment like excavators to operate autonomously, safely, and efficiently. Guo’s major contribution lies in pioneering a novel two-stage method for excavator trajectory planning that seamlessly integrates data-driven imitation learning with model-based trajectory optimization. This hybrid approach allows an excavator to first learn efficient operational patterns from human experts and then refine those paths through physics-based optimization, achieving a balance between human-like intuition and computational precision. Although his most cited paper, "Imitation Learning and Model Integrated Excavator Trajectory Planning" (2022), has garnered 12 citations—a strong indicator of early impact in a niche field—his work is foundational for the future of autonomous construction sites. By bridging the gap between learning from demonstration and rigorous control theory, Guo is helping to pave the way for safer, more productive, and fully autonomous heavy machinery in the construction and mining industries.
Research Focus
Key Achievements
Top Papers
- 1Imitation Learning and Model Integrated Excavator Trajectory Planning12 citations · 2022