Xinyue Yuan

Papers

Total Citations

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About

Xinyue Yuan is a researcher at the forefront of space robotics and intelligent drilling systems, with a focus on extraterrestrial regolith mechanics and machine learning. Their most cited work, "Real-time prediction of drilling forces inside lunar regolith based on recurrent neural networks" (2022, 13 citations), introduces a novel approach to modeling and forecasting the complex, dynamic forces encountered during lunar drilling operations. By leveraging recurrent neural networks, Yuan enables real-time adaptation of drilling parameters, significantly improving the safety and efficiency of subsurface sampling on the Moon. This contribution is critical for future in-situ resource utilization and planetary science missions, where unpredictable soil behavior poses major risks. Yuan’s research bridges the gap between geotechnical engineering and artificial intelligence, offering a data-driven solution to one of space exploration’s most challenging problems. With a growing citation record and a focus on practical, mission-ready technologies, Yuan is establishing a reputation for advancing autonomous drilling systems that can operate reliably in harsh, remote environments. Their work not only supports upcoming lunar and Martian exploration programs but also inspires new directions in intelligent robotic systems for extreme terrains.