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apollo

An open autonomous driving platform

apollo-note

Note for Apollo 3.0 perception, prediction and planning modules

apollo_ss

bachelor_thesis

Multi-robot collision-free path planning

bsplinopt

带有最大曲率约束的B样条平滑

c-

study

c-_study

carla_ros_bridge_pnc

通过carla-ros-bridge在carla上实现自动驾驶planning and control。

carnd-mpc-project

CarND Term 2 Model Predictive Control (MPC) Project

cmake_planning_demo

demo

cmake_planning_test

cmake_planning_test

d_star_pathplanning

Simple Matlab implementation of D*Lite, Focussed D*, A*, for dynamic path planning for mobile robots

data_struct_teaching

C codes for data structure teaching in HUST

dbhot

东北热项目

dbhot2

db

dig-into-apollo

Apollo notes (Apollo学习笔记) - Apollo learning notes for beginners.

eigen_test

eigen

frenet_path_planning

无人车路径规划算法demo

git-demo

hsm

C++ framework library to simplify state-driven code

hybrid-a-star-annotation

Hybrid A*路径规划器的代码注释

hybrid-path-planning-method

Hybrid Path Planning Method for a UGV and a UAV

hybrid_a_star

Hybrid A Star algorithm C++ implementation

hybrid_astar_planner

This is a global planner plugin of ROS move_base package

improved-path-planning-for-ant-colony-algorithm

localization_in_auto_driving

lujingguihua

这是一个存放代码

marp

In this project, we are concerned with the collective behavior of a group of n >1 mobile agents, which can all move in a plane. The action set of each agent is {N, W, S, E, Stay}. The multi-agent rendezvous problem is to devise strategies for each agent to cause all the agents to eventually rendezvous at a single specified location. The approach is to use stochastic game where the agents repeatedly play games from the collection of normal form games, and the particular game played at any given iteration depends probabilistically on the previous game played and on the actions taken by all agents in that game. The game is played in sequence of stages. At the beginning of each stage the game is in some state. The players select actions and each player receives a payoff that depends on the current state and the chosen actions. The game then moves to a new random state whose distribution depends on the previous state and the actions chosen by the players. The procedure is repeated at the new state and play continues for a finite number of stages until all players reach a goal location. Here, each state is a normal form game played by ‘n’ agents. The transition probability is the probability of transitioning from one state to other state after joint action. Payoff matrix is generated for each agent after every game using value iteration algorithm of Markov Decision Process. The objective of the project is to plan a collision free path for each player so that all the players in the game reach common goal location by minimizing the length of the path.

mdp_path_planning

This repository contains the MATLAB code to devise an optimal policy for the motion of the robot given the obstacles and world boundaries. This file contains implementation to a specific environment wiht known parameters and obstacles, but can easily be modified or generalized for any environment. The code was linked to the V-Rep simulation environment and tested.

mobile-robotics-simulation-toolbox

MATLAB and Simulink utilities for vehicle kinematics, visualization, and sensor simulation.