Papers
4
Total Citations
100
H-Index
4
About
Kaipu Wang is at the forefront of sustainable manufacturing and intelligent automation, with a research focus on robotic disassembly, disassembly line balancing (DLB), and human-robot collaboration for end-of-life (EOL) product recycling. His work addresses a critical environmental and economic challenge: how to efficiently and safely recover valuable materials from discarded products. Wang’s major contributions include pioneering energy-efficient robotic parallel disassembly sequence planning (42 citations), which significantly reduces the cost and environmental impact of manual disassembly. He has also advanced the field by developing a dynamic balancing approach for U-shaped robotic disassembly lines using deep reinforcement learning (33 citations), enabling more flexible and efficient task allocation. His research further extends to multi-robotic systems under preventive maintenance scenarios and green, human-robot collaborative disassembly lines that incorporate destructive disassembly techniques. With over 100 total citations, Wang’s work is highly influential in shaping next-generation recycling systems. His notable achievements include integrating artificial bee colony algorithms and reinforcement learning to solve complex, real-world disassembly optimization problems, positioning him as a key innovator in circular economy and sustainable robotics.
Research Focus
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