Papers
4
Total Citations
10
H-Index
2
About
Qiyuan Liu is a researcher specializing in robotics, with a particular focus on the forward kinematics of parallel robot mechanisms. His work addresses one of the most challenging problems in mechanism design: solving the highly nonlinear, strongly coupled equations that determine a robot’s position and orientation. Liu has pioneered the application of chaos theory and advanced computational methods to overcome the limitations of traditional numerical techniques, which often rely heavily on initial guesses and suffer from poor real-time performance. His key contributions include developing a "Chaotic Finding Method" for 3-DOF parallel robots and an LMF algorithm based on hyper-chaos for forward displacement problems. Most notably, his 2022 work introduced a novel neural network approach using Long Short-Term Memory (LSTM) networks to solve forward kinematics for Stewart and TBBP platforms, marking a significant step toward faster, more accurate, and real-time solutions. While his citation counts (ranging from 2 to 4) reflect a focused, niche impact, Liu’s persistent innovation in blending chaos theory, mathematical programming, and deep learning has advanced the foundational understanding of parallel robot kinematics, offering valuable tools for both researchers and engineers in the field.
Research Focus
Key Achievements
Top Papers
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