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About

Xinnan Fan is a leading researcher in bioinspired robotics and intelligent control systems, with a focus on mobile robot navigation, simultaneous localization and mapping (SLAM), and underwater imaging. Her work bridges biological principles and advanced computational methods to solve fundamental challenges in autonomous systems. Fan’s most-cited paper, "Bioinspired Intelligent Algorithm and Its Applications for Mobile Robot Control: A Survey" (2015, 92 citations), provides a comprehensive framework for applying lifelike algorithms to robot control, establishing her as a key voice in the field. She has made significant contributions to path planning, notably with "An Improved VFF Approach for Robot Path Planning in Unknown and Dynamic Environments" (2014, 27 citations), which addresses the local minimum problem in virtual force field methods. Her innovations extend to semantic SLAM, where "An Improved Deep Residual Network-Based Semantic Simultaneous Localization and Mapping Method for Monocular Vision Robot" (2020, 23 citations) enhances environmental understanding for complex applications. Fan also advanced SLAM accuracy with a bioinspired neural model-based extended Kalman filter (2014, 12 citations). Her latest work on underwater image enhancement using a hybrid U-Net-Transformer (2025, 2 citations) demonstrates her ongoing impact in marine robotics, showcasing her ability to tackle real-world degradation challenges. With over 150 total citations, Fan’s research continues to inspire advancements in autonomous navigation and perception.