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About

Xinyue Kan is a robotics and autonomous systems researcher whose work spans autonomous exploration, multi-robot coordination, precision agriculture, and computer vision. Her most influential contribution, "Online Exploration and Coverage Planning in Unknown Obstacle-Cluttered Environments" (2020), has garnered 62 citations and tackles the challenging problem of resolution-complete coverage for non-holonomic vehicles operating without prior environmental knowledge — a critical capability for field monitoring and search-and-rescue missions. Building on this foundation, her work on multi-robot exploration using hex-decomposed environments demonstrates her commitment to scalable, cooperative robotic systems. Kan has also made meaningful strides in agricultural robotics, with her stochastic task planning framework for precision irrigation (29 citations) offering practical solutions to uncertainty-laden real-world deployments. Her 2023 work on the Centroid Distance Keypoint Detector innovates in 3D point cloud processing by incorporating color information alongside geometry, advancing perception capabilities across robotics applications. Complementing these efforts, her research on data-driven hierarchical control and minimalistic neural network architectures for small robots reflects a broad, systems-level approach to autonomous navigation under uncertainty. Across these diverse contributions, Kan has established herself as a versatile and impactful voice in applied robotics research.