Xingyao Han
Papers
3
Total Citations
9
H-Index
2
About
Xingyao Han is a rising researcher in robotics and autonomous systems, with a focus on large-scale multi-robot coordination and robust navigation under uncertainty. Han’s major contributions lie in developing scalable, intelligent solutions for cooperative path planning and task allocation in systems comprising hundreds or even thousands of robots, addressing critical challenges like local congestion and motion conflicts. Their 2024 paper on traffic flow learning for multi-robot path planning (4 citations) introduces a novel approach to managing uncertainties in logistics and industrial applications. Han also challenges conventional wisdom in their 2023 work (3 citations), demonstrating that exhaustive task planning is not always optimal for large multi-robot systems, instead advocating for selective task planning to improve efficiency. Additionally, Han’s 2022 research (2 citations) tackles the “ghost” problem in autonomous driving, using relation learning to enhance information reliability and enable end-to-end robust navigation against sensor noise, lighting variations, and adversarial perturbations. With a growing citation record and a focus on real-world deployability, Han is establishing a reputation for innovative, practical solutions that push the boundaries of multi-robot system performance and autonomous vehicle safety.
Research Focus
Key Achievements
Top Papers
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