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An open autonomous driving platform
Note for Apollo 3.0 perception, prediction and planning modules
Autoware_ai_study
Multi-robot collision-free path planning
带有最大曲率约束的B样条平滑
study
通过carla-ros-bridge在carla上实现自动驾驶planning and control。
CarND Term 2 Model Predictive Control (MPC) Project
demo
cmake_planning_test
Simple Matlab implementation of D*Lite, Focussed D*, A*, for dynamic path planning for mobile robots
C codes for data structure teaching in HUST
东北热项目
db
Apollo notes (Apollo学习笔记) - Apollo learning notes for beginners.
eigen
无人车路径规划算法demo
C++ framework library to simplify state-driven code
Hybrid A*路径规划器的代码注释
Hybrid Path Planning Method for a UGV and a UAV
Hybrid A Star algorithm C++ implementation
This is a global planner plugin of ROS move_base package
这是一个存放代码
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.
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.
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.