Qiming Huang

Papers

Total Citations

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About

Qiming Huang is a researcher focused on intelligent robotics and computer vision, with particular expertise in autonomous navigation and real-time localization systems. His work addresses critical challenges in mobile robot autonomy, especially for service-oriented applications like tour guide robots in complex environments. Huang’s most notable contribution is a path planning algorithm based on deep reinforcement learning, which improves upon the traditional Deep Q-learning Network (DQN) by mitigating two key defects: overfitting and overestimation. This work, published in 2022, has garnered 3 citations and demonstrates a practical approach to enabling robots to autonomously navigate dynamic spaces. Additionally, Huang has explored real-time localization through feature point matching, drawing inspiration from human visual perception to quantify camera movement between video frames. By comparing state-of-the-art image feature extraction methods, his 2022 paper (1 citation) contributes to more efficient and accurate localization for mobile systems. Huang’s research bridges reinforcement learning and computer vision, offering scalable solutions for autonomous robotics. His work is particularly relevant for students and researchers interested in deep learning-based navigation, sensor fusion, and the deployment of intelligent agents in real-world settings.