Xiaoshan Bai
Papers
10
Total Citations
604
H-Index
8
About
Xiaoshan Bai is a robotics researcher whose work centers on multi-robot systems, autonomous task assignment, and mobile robot navigation. With a body of research accumulating over 600 citations, Bai has established a strong reputation for developing algorithmic solutions to complex, real-world coordination challenges in robotics. Bai's most influential contributions lie in multi-robot task assignment and multi-agent pickup and delivery (MAPD), addressing how fleets of robots can efficiently allocate and execute transportation tasks in dynamic environments such as automated warehouses and mail sortation facilities. Their 2021 paper integrating task assignment with path planning for MAPD has garnered 184 citations, while a 2022 study introducing group-based distributed auction algorithms for time-windowed package delivery has attracted 142 citations — reflecting the field's strong appetite for scalable, decentralized approaches. Beyond coordination algorithms, Bai has made notable contributions to mobile robot localization and path planning on uneven terrain, including a widely-read 2024 survey on localization challenges. Practical applications feature prominently in their portfolio, including a creative deployment of multi-robot systems to serve quarantined hotel guests during the COVID-19 pandemic. Across both theoretical and applied fronts, Bai's research consistently bridges algorithmic rigor with real-world relevance.
Research Focus
Key Achievements
Top Papers
- 1
- 2Group-Based Distributed Auction Algorithms for Multi-Robot Task Assignment142 citations · 2022
- 3Mobile robot localization: Current challenges and future prospective111 citations · 2024
- 4
- 5Path Planning for Wheeled Mobile Robot in Partially Known Uneven Terrain50 citations · 2022
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- 8
- 9Task assignment for robots with limited communication7 citations · 2017
- 10