Yimeng Yu
Papers
1
Total Citations
2
H-Index
1
About
Yimeng Yu is a researcher advancing the intersection of robotics and deep learning, with a primary focus on intelligent control systems and kinematic optimization for cooperative robots. Yu’s most notable contribution is the development of an inverse kinematics solution for 5-DOF cooperative robots using Long Short-Term Memory (LSTM) networks, published in 2023. This work directly addresses critical challenges in robotics—lengthy computation times and inefficient path searching—by replacing traditional analytical methods with a data-driven neural approach. By establishing both forward and inverse analytical kinematics models and then training an LSTM to approximate the inverse solution, Yu demonstrated how recurrent neural networks can significantly accelerate real-time robot control. While the paper has garnered 2 citations to date, its methodological innovation positions it as a foundational step toward more responsive and adaptive robotic systems. Yu’s research bridges the gap between classical robotics kinematics and modern deep learning, offering a practical pathway for improving the efficiency of collaborative robots in industrial and service applications. This work is particularly relevant for researchers exploring neural network-based control in constrained robotic platforms.
Research Focus
Key Achievements
Top Papers
- 1