Jiali Shen

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About

Jiali Shen’s research lies at the intersection of robotics, autonomous navigation, and intelligent control systems, with a particular focus on enabling machines to perceive, localize, and act within complex environments. Shen’s most cited work introduces a visually guided museum guide robot, ATLAS, which integrates visitor detection, voice interaction, and touch-screen interfaces—a pioneering effort in human-robot interaction that has garnered 14 citations. Building on this foundation, Shen advanced mobile robot autonomy through a Support Vector Machine (SVM)-based SLAM algorithm, leveraging SVM’s classification strengths to enhance simultaneous localization and mapping from sensor data. This work, cited 6 times, demonstrates a novel machine learning approach to feature selection for robust indoor navigation. Shen also contributed to underwater robotics with a predictive fuzzy PID control method for autonomous underwater vehicles (AUVs), addressing nonlinear and time-varying dynamics in shallow-water surge conditions. Additional research on SVM-based visual localization further underscores Shen’s commitment to fusing computer vision with topological mapping for reliable robot positioning. With a career spanning both terrestrial and marine platforms, Shen’s contributions offer practical, learning-driven solutions to core challenges in autonomous systems, making their work a valuable reference for students and researchers in robotics and control engineering.