Papers
1
Total Citations
2
H-Index
1
About
Boqiang Chen is a researcher at the forefront of parallel robotics and intelligent control systems, with a primary focus on advancing the computational efficiency and accuracy of kinematic solutions. His most notable contribution is the development of a novel approach to solving the forward kinematics of parallel mechanisms, such as the Stewart platform and TBBP manipulator, by integrating Long Short-Term Memory (LSTM) neural networks. This method addresses the critical limitations of traditional numerical techniques—namely, their heavy reliance on initial value iterations, low precision, and poor real-time performance. By leveraging deep learning, Chen’s work offers a more robust and faster alternative, enabling real-time control in complex robotic systems. His 2022 paper on this topic has garnered 2 citations, signaling early recognition in the field. Chen’s research bridges the gap between classical mechanics and modern AI, with potential applications in aerospace, manufacturing, and surgical robotics. His innovative use of LSTM for kinematic modeling marks a significant step toward smarter, more adaptive robotic systems.
Research Focus
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Top Papers
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