Xiujuan Du
Papers
3
Total Citations
314
H-Index
3
About
Xiujuan Du is a leading researcher in robotics and neural dynamics, with a focus on solving complex, time-variant mathematical problems for real-world applications. Her work centers on developing advanced control schemes for redundant robotic manipulators, particularly through quadratic programming (QP)-based and recurrent neural network (RNN) approaches. Du’s most impactful contribution is her 2020 paper on using RNNs to solve the time-variant generalized Sylvester equation, which has garnered 174 citations and offers a unified framework applicable to robot control and acoustic source localization. She also introduced a generalized repetitive motion planning (RMP) scheme for redundant robots, aided by dynamic neural networks and nonconvex bound constraints, a 2019 work with 137 citations that systematizes existing approaches. Her 2022 paper revisits QP-based control schemes with different emphases, further advancing the field. Du’s research bridges theoretical mathematics and practical robotics, providing robust, real-time solutions that enhance the precision and adaptability of robotic systems in manufacturing and beyond. Her work is essential reading for those interested in neural network-based control and optimization in robotics.
Research Focus
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Top Papers
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