Yunlong Guo's Projects
Leveraging system development and robot deployment for aerial autonomous navigation.
Public repository for Air Learning project
[ICRA 2023] ARiADNE: A Reinforcement learning approach using Attention-based Deep Networks for Exploration - Public code and model
Autonomous Quadrotor 3D environment, based on python.
[CMU] A Versatile and Modular Framework Designed for Autonomous Unmanned Aerial Vehicles [UAVs] (C++/ROS/PX4)
Multi-robot collaborative tasks with deep learning
An efficient single/multi-agent trajectory planner for multicopters.
Swarm Playground, the codebase of the paper "Swarm of micro flying robots in the wild"
A Robust and Efficient Trajectory Planner for Quadrotors
Fast Incremental Euclidean Distance Fields for Online Motion Planning of Aerial Robots
An Efficient Framework for Fast UAV Exploration
Gazebo database of SDF models. This is a predecessor to https://app.gazebosim.org
Personal website
The official repo for NeurIPS 2021 paper 'Reducing Collision Checking for Sampling-Based Motion Planning Using Graph Neural Networks'
Graph Neural Networks for Decentralized Path Planning
Must-read papers on graph neural networks (GNN)
为ChatGPT/GLM提供图形交互界面,特别优化论文阅读/润色/写作体验,模块化设计,支持自定义快捷按钮&函数插件,支持Python和C++等项目剖析&自译解功能,PDF/LaTex论文翻译&总结功能,支持并行问询多种LLM模型,支持清华chatglm等本地模型。兼容复旦MOSS, llama, rwkv, 盘古, newbing, claude等
Heterogeneous Multi-Robot Reinforcement Learning
Policy Search for Model Predictive Control with Application to Agile Drone Flight
IDE style command line auto complete
Library with search algorithms for task and path planning for multi robot/agent systems
This repository enables fully autonomous drone flight based on Mid360. It incorporates an odometry reference from Fast-LIO2 and a planning module reference from Ego-Planner.
This is a multi-agent path planning(also known as Multi-Agent Path Finding, MAPF) algorithm package for ROS
Implementations of various algorithms used to solve the problem of Multi-Agent Pickup and Delivery (a generalization of Multi-Agent Path Finding).
Design and simulation verification of multi-robot collaborative exploration algorithm based on reinforcement learning. In the simulation environment of ROS-Gazebo, multiple robots explore closed rooms and build maps (SLAM).