Wenlu Wang

Wuhan University of Science and Technology

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About

Wenlu Wang is a researcher focused on advancing autonomous navigation and perception for mobile robots, with key contributions in local path planning and instance segmentation. Wang’s most-cited work, “Local Path Planning for Mobile Robots Based on Fuzzy Dynamic Window Algorithm” (2023, 27 citations), addresses the challenge of human-robot collaboration by enhancing the dynamic window approach with fuzzy logic, enabling safer and more adaptive real-time navigation in complex environments. This work is pivotal for improving robot decision-making in dynamic settings. More recently, Wang proposed “An end-to-end instance segmentation method based on improved ConvNeXt V2” (2024, 2 citations), which leverages the powerful ConvNeXt V2 backbone within the RTMDet framework to boost the accuracy and efficiency of indoor mobile robots in locating and segmenting environmental instances. This innovation demonstrates Wang’s commitment to integrating state-of-the-art deep learning techniques into practical robotics. By bridging fuzzy control and modern neural architectures, Wang’s research significantly impacts the development of more intelligent and responsive autonomous systems, offering valuable insights for students and researchers in robotics and computer vision.