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apollo

An open autonomous driving platform

astar_qlearning_roboticpathplanning

autonomous-crowd-crossing-navigation-with-hybrid-path-planning

autonomous-driving-and-verification

autonomous_guidance_mpc_and_lqr-lmi

Kinematic MPC and dynamic LPV-LQR state feedback control for an autonomous vehicle

autonomousvehiclepath

Matlab script generates a B-spline path with Parametric Continuity (C2) between two states

beamerstyleslides

🌈Beamer风格的幻灯片模板集。包含了PowerPoint和Keynote两套格式。

blasfeo

Basic linear algebra subroutines for embedded optimization

carnd-mpc-project

CarND Term 2 Model Predictive Control (MPC) Project

carnd-mpc-project-1

My solution to the Udacity Self-Driving Car Engineer Nanodegree Model Predictive Control project

carnd-path-planning-project

My solution to the Udacity Self-Driving Car Engineer Nanodegree Path Planning Project.

carnd-path-planning-project-1

Self-Driving Car Engineer Program: Path Planning Project

carnd_path_planning_matlab

A MATLAB implementation of the path planning project from the Udacity self-driving car nanodegree.

dubins-rrt-for-matlab

RRT (Rapidly-Exploring Random Trees) using Dubins curve, with collision check in MATLAB

dynamic-movement-primitives-and-imitation-learning-robotics

Dynamic movement primitives (DMPs) are a method of trajectory control/planning from Stefan Schaal’s lab. Complex movements have long been thought to be composed of sets of primitive action ‘building blocks’ executed in sequence and \ or in parallel, and DMPs are a proposed mathematical formalization of these primitives. The difference between DMPs and previously proposed building blocks is that each DMP is a nonlinear dynamical system. The basic idea is that you take a dynamical system with well specified, stable behavior and add another term that makes it follow some interesting trajectory as it goes about its business. The DMP differential equations (Transformation System, Canonical System, Non-linear Function) realize a general way of generating point-to-point movements. Imitation learning using linear regression is performed to compute the weight factor W from a demonstrated trajectory dataset, given by a teacher. The quality of the imitation is evaluated by comparing the training data with the data generated by the DMP.

feedforward-and-feedback-control-of-a-vertical-takeoff-and-landing-aircraft

The aircraft dynamics are modeled using two coordinate frames----a non-linear Multi Input Multi Output(MIMO) system. In order to make VTOL follow desired trajectory, we use Iterative control and Preview-time control.

high-definition-map-modeling-for-autonomous-driving

highway-env

An environment for behavioral planning tasks in autonomous driving

hpipm

High-performance interior-point-method QP solvers

hpmpc

Library for High-Performance implementation of solvers for MPC.

ktw_path_tracking_with_model_based_rl-learning_based_mpc-

This code is about the path tracking control using model predictive control with friction parameter learning. This control scheme is also called model-based RL in which planning and learning are repeated to minimize the given cost. Start this code with ptMain.m file.

learngit

lta

LTA Path planning Algorithm

mads.jl

MADS: Model Analysis & Decision Support

matlabrobotics

MATLAB sample codes for mobile robot navigation

matlabwebsocket

A simple event-based WebSocket library for MATLAB.

model-predictive-control

Udacity Self-Driving Car Engineer Nanodegree. Project: Model Predictive Control

mpc_toolbox

MATLAB Toolbox for Model Predictive Control

mpcc

Model Predictive Contouring Controller (MPCC) for Autonomous Racing

nonholonomic-rrt