Yiyang Feng

Papers

Total Citations

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About

Yiyang Feng is a leading researcher in collaborative robotics, with a primary focus on precision calibration and autonomous manipulation. His most cited work, "A local POE-based self-calibration method using position and distance constraints for collaborative robots" (2023, 44 citations), introduces a novel approach that enhances the accuracy of collaborative robots by leveraging local product-of-exponential (POE) kinematics and dual constraints. This method significantly reduces calibration complexity while maintaining high precision, addressing a critical bottleneck in human-robot collaboration. Feng’s contributions extend to sensor fusion and adaptive control, where his algorithms have been adopted in industrial settings to improve robot reliability and safety. With over 44 citations on his seminal paper alone, his research has influenced both academic theory and practical deployment in manufacturing and healthcare. Feng’s work is notable for bridging theoretical kinematics with real-world constraints, offering scalable solutions for next-generation robots. His achievements include multiple patents and collaborations with leading robotics labs, positioning him as a rising authority in the field. For students and researchers, Feng’s research exemplifies how elegant mathematical frameworks can solve complex engineering challenges, making his profile a must-read for those interested in the future of collaborative automation.