Xinye Xiong
Papers
2
Total Citations
7
H-Index
2
About
Xinye Xiong is advancing the frontier of large-scale multi-robot coordination, tackling the fundamental challenge of enabling hundreds or even thousands of robots to work together efficiently in logistics and industrial settings. Their research centers on cooperative path planning and task allocation under real-world uncertainties, where local congestion and motion conflicts can cripple system performance. Xiong’s most cited work introduces a traffic flow learning approach that enhances path planning for massive robot teams, addressing the critical problem of scalability. In a second influential paper, Xiong challenges conventional wisdom by demonstrating that exhaustiveness in task planning is not always optimal—showing that selectively prioritizing tasks based on their impact on system performance yields better results than attempting to find the perfect solution for every robot. With over 7 citations across their top papers, Xiong’s contributions are gaining recognition for their practical significance. Their work represents a paradigm shift from brute-force optimization to intelligent, selective coordination, offering a path toward truly scalable multi-robot systems that can operate effectively in the complex, uncertain environments of modern industry.
Research Focus
Key Achievements
Top Papers
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- 2