Bo Jiao

Inner Mongolia Yili Industrial Group (China)

Papers

Total Citations

H-Index

About

Bo Jiao is a researcher advancing intelligent safety systems for heavy construction machinery, with a primary focus on real-time obstacle avoidance for crawler cranes. His work addresses a critical industry challenge: ensuring operational safety in dynamic, high-risk environments where cranes carry loads of dozens of tons amid moving obstacles like workers and vehicles. Jiao’s key contribution lies in applying deep reinforcement learning—specifically Deep Q-Networks (DQN)—to enable cranes to autonomously navigate and avoid both static and dynamic hazards in real time. His most-cited paper, "Real-time Obstacles Avoidance for Crawler Crane based on DQN" (2021), has garnered 2 citations, reflecting its foundational role in integrating AI with construction safety protocols. This work stands out for its practical approach to mitigating collision risks that traditional methods cannot address, offering a scalable solution for smarter, safer worksites. Jiao’s research bridges the gap between theoretical robotics and industrial application, making him a notable contributor to the growing field of autonomous heavy equipment. His achievements highlight a commitment to reducing human error and enhancing operational efficiency in construction and logistics.