Jingwen Wu

Papers

Total Citations

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About

Jingwen Wu is a researcher focused on advancing automation and robotics in logistics and industrial production. Her primary research areas include robotic mobile fulfillment systems, warehouse optimization, and robotic manipulation. Wu’s most significant contribution is her work on joint optimization of order picking and replenishment in robotic mobile fulfillment systems (2024, 12 citations), which addresses critical efficiency challenges in modern e-commerce and warehouse operations. She has also explored mapping methods for single LiDAR in indoor degraded environments (2022, 3 citations), contributing to improved navigation for autonomous systems in challenging conditions. Earlier in her career, Wu investigated optimization of pick-and-place routes for Delta robots based on lame curves (2018, 2 citations), where she analyzed the mechanical structure and established kinematic models for high-speed, light-load industrial applications. Her research demonstrates a consistent focus on practical, efficiency-driven solutions for automated systems, from warehouse logistics to robotic manipulation. With her most-cited paper already gaining attention in the field, Wu’s work continues to influence the development of smarter, more efficient robotic systems for industrial and logistical applications.