Yuqi Peng

Papers

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About

Yuqi Peng is a researcher specializing in robotics and computational kinematics, with a particular focus on applying neural network techniques to industrial automation. Their most cited work, "Neural Network Based Inverse Kinematics Solution for 6-R Robot Implement Using R Package Neuralnet" (2021, 7 citations), addresses a critical challenge in robotics: solving inverse kinematics for complex 6-revolute jointed robot arms, such as the ABB IRB7600-500. Peng demonstrated that traditional analytical and numerical methods often fall short in achieving precise, real-time positioning for such systems. By leveraging the R package neuralnet, they developed a neural network-based approach that minimizes positional error in the workspace, enabling smoother and more accurate motion control. This contribution is particularly valuable for industries relying on high-precision robotic manipulation, such as manufacturing and assembly. Peng’s work bridges the gap between classical robotics theory and modern machine learning, offering a practical, data-driven alternative to conventional solvers. Their research underscores the potential of neural networks to enhance robotic performance, making them a notable figure in the intersection of artificial intelligence and mechanical engineering.