xzh's starred repositories
End-to-end-Autonomous-Driving
[IEEE T-PAMI] All you need for End-to-end Autonomous Driving
MotionPlanning
Motion planning algorithms commonly used on autonomous vehicles. (path planning + path tracking)
RacingLMPC
Implementation of the Learning Model Predictive Controller for autonomous racing
mpc-car-tutorial
A tutorial of using MPC (both implementations of nonlinear MPC and linear time-varying MPC) for reference tracking with a bicycle model.
open-shell-book
开源书籍：《Shell 编程范例》，面向操作对象学 Shell！本书作者发布了《360°剖析 Linux ELF》视频课程，欢迎订阅：https://www.cctalk.com/m/group/88089283
Racing-LMPC-ROS2
C++ ROS2 packages that implement learning model predictive control for real-world autonomous race cars.
quadrotor_acados
Model Predictive Control for Quadrotor using acados
PINNs-based-MPC
We discuss nonlinear model predictive control (NMPC) for multi-body dynamics via physics-informed machine learning methods. Physics-informed neural networks (PINNs) are a promising tool to approximate (partial) differential equations. PINNs are not suited for control tasks in their original form since they are not designed to handle variable control actions or variable initial values. We thus present the idea of enhancing PINNs by adding control actions and initial conditions as additional network inputs. The high-dimensional input space is subsequently reduced via a sampling strategy and a zero-hold assumption. This strategy enables the controller design based on a PINN as an approximation of the underlying system dynamics. The additional benefit is that the sensitivities are easily computed via automatic differentiation, thus leading to efficient gradient-based algorithms. Finally, we present our results using our PINN-based MPC to solve a tracking problem for a complex mechanical system, a multi-link manipulator.
MPC_Local_Planner
Local Planner for a differential drive mobile robot using MPC and Acados with ROS Implementation
mpc_local_planner
The mpc_local_planner package implements a plugin to the base_local_planner of the 2D navigation stack. It provides a generic and versatile model predictive control implementation with minimum-time and quadratic-form receding-horizon configurations.
autonomous_driving_mpc
Model Predictive Controller for Autonomous Driving implemented using ROS and C++
neural-mpc
Real-time Neural MPC: Deep Learning Model Predictive Control for Quadrotors and Agile Robotic Platforms
robust-tube-mpc
An example code for robust model predictive control using tube
IR-STP-Planner
Introduction and source code to paper: ``IR-STP: Enhancing Autonomous Driving with Interaction Reasoning in Spatio-Temporal Planning''
optimization_dynamics
Implementation and examples from Trajectory Optimization with Optimization-Based Dynamics https://arxiv.org/abs/2109.04928
ros_motion_planning
Motion planning and Navigation of AGV/AMR：ROS planner plugin implementation of A*, JPS, D*, LPA*, D* Lite, Theta*, RRT, RRT*, RRT-Connect, Informed RRT*, ACO, PSO, Voronoi, PID, LQR, MPC, DWA, APF, Pure Pursuit etc.
DRL-Pytorch
Clean, Robust, and Unified PyTorch implementation of popular Deep Reinforcement Learning (DRL) algorithms (Q-learning, Duel DDQN, PER, C51, Noisy DQN, PPO, DDPG, TD3, SAC, ASL)
Sparrow-V1
A Reinforcement Learning Friendly Simulator for Mobile Robot
model-based-rl-with-mpc
Model-Based Reinforcement Learning for Highway Environment with MPC Planning: This repository presents an RL framework integrating Model Predictive Control (MPC) for strategic decision-making in simulated highway scenarios.
Apollo-DL-IAPS
Apollo Discrete Points Smoother