216 citations · 240 across the 5 of their papers we have counts for
5 papers
Learning Predictive Control with Deep Koopman Operators for Autonomous Vehicle Motion Planning
Xinglong Zhang, Yongqian Xiao, Haotian Cao +3
Model Predictive Control (MPC) is widely used for autonomous-vehicle (AV) motion planning, but its real-time applicability is often limited by the need for accurate models and onli…
Vector Field-Guided Learning Predictive Control for Motion Planning of Mobile Robots with Uncertain Dynamics
Yang Lu, Weijia Yao, Yongqian Xiao +4
In obstacle-dense scenarios, providing safe guidance for mobile robots is critical to improve the safe maneuvering capability. However, the guidance provided by standard guiding ve…
DDK: A Deep Koopman Approach for Dynamics Modeling and Trajectory Tracking of Autonomous Vehicles
Yongqian Xiao
Autonomous driving has attracted lots of attention in recent years. An accurate vehicle dynamics is important for autonomous driving techniques, e.g. trajectory prediction, motion…
CKNet: A Convolutional Neural Network Based on Koopman Operator for Modeling Latent Dynamics from Pixels
Yongqian Xiao, Xin Xu, QianLi Lin
With the development of end-to-end control based on deep learning, it is important to study new system modeling techniques to realize dynamics modeling with high-dimensional inputs…
Deep Neural Networks with Koopman Operators for Modeling and Control of Autonomous Vehicles
Yongqian Xiao, Xinglong Zhang, Xin Xu +2
Autonomous driving technologies have received notable attention in the past decades. In autonomous driving systems, identifying a precise dynamical model for motion control is nont…