Zhanting Yuan

Papers

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About

Zhanting Yuan has made significant contributions to the field of robotics and control systems, with a primary focus on redundant robot manipulators and advanced trajectory planning. Their most-cited work, "RNN for Receding Horizon Control of Redundant Robot Manipulators" (2021, 61 citations), introduces a novel planning scheme that leverages recurrent neural networks (RNNs) to achieve precise trajectory tracking in redundant manipulators—a critical challenge for applications ranging from industrial automation to surgical robotics. This research bridges the gap between neural network theory and real-time control, offering a receding horizon approach that enhances adaptability and efficiency. Yuan’s work stands out for its practical impact, providing a robust framework for optimizing manipulator motion in dynamic environments. With 61 citations, this paper has become a key reference for researchers exploring neural network-based control strategies. Yuan’s contributions underscore a commitment to solving complex kinematic problems, positioning them as an emerging voice in robotics. Their research not only advances theoretical understanding but also offers tangible solutions for next-generation robotic systems, making it essential reading for students and engineers alike.