Wanda Guo
Papers
1
Total Citations
2
H-Index
1
About
Dr. Wanda Guo is at the forefront of intelligent construction machinery, with a primary research focus on safety-critical autonomous navigation for heavy-lift equipment. Her work addresses a fundamental challenge in crane operations: ensuring real-time obstacle avoidance in dynamic, high-risk environments where static barriers and moving hazards—such as workers and vehicles—coexist. In her most cited study, "Real-time Obstacles Avoidance for Crawler Crane based on DQN" (2021), Dr. Guo pioneered the application of deep reinforcement learning (DQN) to enable crawler cranes carrying dozens of tons of load to autonomously sense and evade both stationary and moving obstacles. This contribution directly tackles the industry's paramount concern for safety, offering a path toward reducing accidents in congested work sites. While her citation count is still growing, reflecting the emerging nature of this applied AI field, her work is notable for bridging theoretical reinforcement learning with practical, high-stakes industrial robotics. Dr. Guo’s research is essential reading for engineers and researchers developing autonomous systems for heavy machinery, where the margin for error is measured in tons and human lives.
Research Focus
Key Achievements
Top Papers
- 1Real-time Obstacles Avoidance for Crawler Crane based on DQN2 citations · 2021