Qingjie Zhao

Papers

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About

Qingjie Zhao’s research lies at the intersection of robotics, computer vision, and intelligent control, with a particular focus on visual servoing and autonomous navigation. Her most cited work, “Appearance-based Robot Visual Servo via a Wavelet Neural Network” (2008, 18 citations), introduced a novel approach that leverages wavelet neural networks for appearance-based control, enabling robots to perform precise visual servoing without requiring explicit 3D models. This contribution addressed key challenges in real-time robotic manipulation by improving robustness to visual variations. Zhao further advanced the field with “Robot Visual Servo with Fuzzy Particle Filter” (2012, 9 citations), which integrated fuzzy logic with particle filtering to enhance state estimation under uncertainty. In “A framework for RF-Visual SLAM” (2013, 4 citations), she tackled the limitations of metric SLAM for long-term navigation by proposing a hybrid framework that combines radio-frequency signals with visual cues, offering a more resilient solution for autonomous robots operating in large-scale environments. Through these works, Zhao has contributed foundational ideas to appearance-based control and hybrid SLAM, demonstrating impact in improving robot perception and navigation in real-world settings.