Quanlong Guan

Papers

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About

Quanlong Guan is a robotics researcher whose work centers on advancing autonomous systems for medical applications, with a particular focus on robotic manipulation and ultrasound imaging. His most impactful contribution, "Online kinematic calibration of robot manipulator based on neural network" (2024), has garnered 39 citations, demonstrating its significance in improving robot precision through adaptive learning methods. This work addresses a fundamental challenge in robotics—accurate calibration—by leveraging neural networks for real-time kinematic adjustments, enhancing the reliability of robotic manipulators in dynamic environments. Guan’s more recent research, "Tissue-View Map for Robotic Carotid Artery Ultrasound Scanning Using Reinforcement Learning" (2025), tackles the operator-dependent nature of medical ultrasound by framing autonomous scanning as a sequential decision-making problem. By integrating reinforcement learning with tissue-view mapping, he aims to reduce reliance on human expertise, potentially making diagnostic ultrasound more accessible and consistent. This work highlights his commitment to translating robotic intelligence into practical healthcare solutions. With a growing citation record and a focus on both foundational robotics and applied medical technology, Guan is establishing himself as a researcher who bridges theory and real-world impact, offering promising avenues for students and engineers interested in intelligent robotic systems.