Papers
5
Total Citations
52
H-Index
3
About
Xiaohui Ren is a leading researcher in robotics and neural computation, specializing in the control and coordination of redundant robot manipulators and multi-robot systems. Their major contributions center on developing innovative recurrent neural network (RNN) architectures to solve complex motion planning and synchronization problems. Ren pioneered the "varying-parameter complementary neural network" for multi-robot tracking and formation via model predictive control, a work that has garnered 31 citations since 2024. They also introduced the "varying-gain neural bicriterion velocity minimization self-motion" (VGN-BCVM-SM) approach, which optimizes self-motion for redundant manipulators by minimizing joint velocity and avoiding singularities, cited 11 times. Further notable achievements include the "state-coupled neural network" (SDNN) for synchronized collaboration of distributed multiple robotic arms, ensuring coordinated motion in both Cartesian and joint spaces, and a constrained workspace entrance crossing motion generation scheme for safe task execution. Ren’s work consistently integrates quadratic programming with neural dynamics, achieving adaptive convergence and high efficiency. With a growing citation impact, their research is pivotal for advancing autonomous robotics, particularly in industrial automation and collaborative multi-agent systems.
Research Focus
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