Papers
15
Total Citations
339
H-Index
7
About
Demin Xu is a robotics and autonomous systems researcher whose work spans motion planning, model predictive control, and multi-agent systems. He is best known for his contributions to sampling-based motion planning, particularly his development of the Near-Optimal RRT (NoD-RRT) algorithm, which leverages neural network approximation to achieve near-optimal trajectory generation for vehicles with nonlinear dynamics — a paper that has garnered over 125 citations since 2018. His research on Lyapunov-based model predictive control for nonholonomic mobile robots (49 citations) and policy learning for nonlinear MPC applied to unmanned surface vehicles (60 citations) reflects a sustained effort to make computationally intensive control methods practical for real-time robotic deployment. Xu has also made contributions to path planning under uncertainty using partially observable Markov decision processes and cross-entropy optimization, as well as foundational work in visibility graph construction and autonomous underwater vehicle navigation using potential field methods. More recently, his interests have extended to swarm intelligence and multi-agent trajectory prediction in crowded environments. Across more than fifteen years of research, Xu has consistently bridged theoretical control and planning frameworks with real-world autonomous systems applications.
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