Zhang Zhiwen
Papers
3
Total Citations
52
H-Index
3
About
Zhang Zhiwen is a leading researcher in sustainable manufacturing and intelligent automation, with a primary focus on human-robot collaboration (HRC) for the disassembly of end-of-life (EOL) products, particularly electric vehicle batteries. Their work directly addresses the critical challenge of efficient recycling and resource reutilization. A major contribution is the development of advanced AI-driven decision-making frameworks for dynamic task allocation. For instance, their 2025 paper on "Dynamic task allocations with Q-learning based particle swarm optimization" (25 citations) pioneers a hybrid optimization method for real-time HRC coordination. They have further advanced the field by integrating digital twins with multi-agent deep reinforcement learning, as seen in their highly cited work on "Multi-scenario digital twin-driven human-robot collaboration" (21 citations), which models complex disassembly processes using dynamic time Petri-nets. Most recently, Zhang has introduced knowledge graph-driven process reasoning, employing graph attention networks to semantically analyze and decompose disassembly strategies, enabling more adaptive and intelligent task allocation. With a rapidly growing citation impact, Zhang Zhiwen is establishing themselves as a key innovator at the intersection of AI, robotics, and circular economy, driving the future of automated, sustainable recycling.
Research Focus
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Top Papers
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