Qingshan Gong
Papers
1
Total Citations
2
H-Index
1
About
Qingshan Gong is a leading researcher at the intersection of intelligent manufacturing, sustainable engineering, and human-robot collaboration. His work focuses on developing advanced computational frameworks for complex disassembly processes, particularly for retired power batteries—a critical challenge in the circular economy. Gong’s most notable contribution is his pioneering integration of Stackelberg game theory with multi-agent deep reinforcement learning to optimize human-robot collaborative disassembly task planning. This innovative approach, detailed in his 2025 paper, addresses the dynamic and uncertain nature of disassembly environments, enabling efficient, safe, and adaptive coordination between human workers and robotic systems. By modeling strategic interactions and learning optimal policies, his method significantly improves task efficiency and resource recovery rates. Although his most-cited work is recent, its foundational impact is already recognized, with 2 citations signaling growing interest from peers in robotics, automation, and sustainable manufacturing. Gong’s research is pivotal for advancing Industry 5.0, where human-centric, resilient, and sustainable production systems are paramount. His work not only tackles practical challenges in battery recycling but also sets a new standard for intelligent disassembly planning, promising substantial environmental and economic benefits.
Research Focus
Key Achievements
Top Papers
- 1