Jingwen Xu
Papers
2
Total Citations
15
H-Index
2
About
Jingwen Xu is a robotics researcher whose work focuses on solving critical coordination and planning challenges in multi-robot systems, particularly for warehouse automation. Her primary research areas include autonomous recharging strategies, probabilistic path planning under uncertainty, and decentralized multi-robot coordination. Xu’s most influential contribution is her 2019 paper on efficient recharging task planning for multi-robot systems, which addresses the previously disjoint problems of deciding *when* and *where* robots should recharge—a fundamental bottleneck for continuous warehouse operations. This work has garnered 13 citations, establishing a foundation for practical, scalable battery management in autonomous fleets. In her 2020 work, Xu advanced the field by proposing a bi-level probabilistic path planning algorithm that explicitly accounts for motion uncertainty. By partitioning warehouse maps into interconnected districts and introducing a hierarchical planning architecture, her approach improves system efficiency even when robots cannot perfectly predict each other’s movements. Xu’s research bridges theoretical planning algorithms with real-world operational constraints, making her work directly relevant to the growing field of logistics robotics. Her contributions are particularly valuable for students and researchers interested in the intersection of multi-agent systems, uncertainty-aware planning, and practical deployment challenges in industrial environments.
Research Focus
Key Achievements
Top Papers
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