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About

Xiaorong Hou is a researcher whose work lies at the intersection of robotics, path planning, and computational geometry. Hou’s primary contributions focus on developing efficient algorithms for mobile robot navigation, particularly in complex 2D and 3D environments. Most notably, Hou’s 2020 paper on “Surface Optimal Path Planning Using an Extended Dijkstra Algorithm” has garnered 224 citations, demonstrating significant impact in the field. This work addresses the practical challenge of finding optimal paths across uneven terrain—critical for applications in planetary exploration, wild ground navigation, and gaming. Hou also advances bioinspired neural network approaches for robot path planning, introducing a multi-scale map method to handle large-scale environments with high resolution. Further work extends these techniques to multi-robot, one-target pursuit in 3D space, tackling computational cost and time challenges. Earlier in their career, Hou contributed to computational geometry with an explicit criterion for determining the positional relationship between two planar ellipses, a problem relevant to computer graphics, machine vision, and CAD/CAM. Through these contributions, Hou has helped bridge theoretical algorithms with real-world robotic navigation challenges.