Zhengang Li

Wuhan University of Technology, Northeastern University

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Total Citations

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About

Zhengang Li is a robotics and computer vision researcher whose work centers on autonomous navigation, obstacle detection, and accessible assistive technology. His research bridges the gap between cutting-edge machine learning techniques and real-world robotic systems, with a particular emphasis on making such systems practical, cost-effective, and deployable in everyday environments. Li's most recognized contribution is his 2017 work on a ROS-based indoor autonomous exploration and navigation wheelchair, which has garnered 39 citations. This project addressed critical barriers in assistive robotics by delivering a low-cost, highly reusable autonomous navigation system — a meaningful step toward improving mobility independence for individuals with physical disabilities. More recently, Li has advanced the field of real-time obstacle detection through StereoVoxelNet, a deep neural network framework that leverages occupancy voxels derived from stereo camera input. Accumulating over 20 citations since its 2023 publication, this work demonstrates the practical advantages of integrating deep learning with stereo vision for safety-critical navigation applications. Across his research, Li consistently targets the intersection of affordability, performance, and real-world applicability — qualities that make his contributions particularly valuable to researchers and engineers working on autonomous systems and assistive robotics.