https://github.com/hkust-aerial-robotics/fuel
An Efficient Framework for Fast UAV Exploration
https://github.com/hkust-aerial-robotics/fuel
aerial-robotics autonomous-navigation autonomous-robots motion-planning uav
Last synced: 5 months ago
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An Efficient Framework for Fast UAV Exploration
- Host: GitHub
- URL: https://github.com/hkust-aerial-robotics/fuel
- Owner: HKUST-Aerial-Robotics
- License: gpl-3.0
- Created: 2020-10-18T14:06:42.000Z (almost 5 years ago)
- Default Branch: main
- Last Pushed: 2024-11-19T06:06:55.000Z (11 months ago)
- Last Synced: 2025-04-22T14:15:38.782Z (6 months ago)
- Topics: aerial-robotics, autonomous-navigation, autonomous-robots, motion-planning, uav
- Language: C++
- Homepage:
- Size: 122 MB
- Stars: 1,113
- Watchers: 21
- Forks: 215
- Open Issues: 61
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# FUEL
__News:__
- Feb 24, 2023: the code for **multi-UAV exploration** is released! check [this link](https://github.com/SYSU-STAR/RACER).
- Aug 24, 2021: The CPU-based simulation is released, CUDA is no longer required. Richer exploration environments are provided.
**FUEL** is a powerful framework for **F**ast **U**AV **E**xp**L**oration.
Our method is demonstrated to complete challenging exploration tasks **3-8 times** faster than state-of-the-art approaches at the time of publication.
Central to it is a Frontier Information Structure (FIS), which maintains crucial information for exploration planning incrementally along with the online built map. Based on the FIS, a hierarchical planner plans frontier coverage paths, refine local viewpoints, and generates minimum-time trajectories in sequence to explore unknown environment agilely and safely. Try [Quick Start](#quick-start) to run a demo in a few minutes!
Recently, we further develop a fully decentralized approach for exploration tasks using a fleet of quadrotors. The quadrotor team operates with asynchronous and limited communication, and does not require any central control. The coverage paths and workload allocations of the team are optimized and balanced in order to fully realize the system's potential. The associated paper has been published in IEEE TRO. Check code [here](https://github.com/SYSU-STAR/RACER).
__Complete videos__: [video1](https://www.youtube.com/watch?v=_dGgZUrWk-8), [video2](https://www.bilibili.com/video/BV1yf4y1P7Vj).
__Authors__: [Boyu Zhou](http://sysu-star.com) from SYSU and [Shaojie Shen](http://uav.ust.hk/group/) from the [HUKST Aerial Robotics Group](http://uav.ust.hk/).
Please cite our paper if you use this project in your research:
- [__FUEL: Fast UAV Exploration using Incremental Frontier Structure and Hierarchical Planning__](https://arxiv.org/abs/2010.11561), Boyu Zhou, Yichen Zhang, Xinyi Chen, Shaojie Shen, IEEE Robotics and Automation Letters (**RA-L**) with ICRA 2021 option
```
@article{zhou2021fuel,
title={FUEL: Fast UAV Exploration Using Incremental Frontier Structure and Hierarchical Planning},
author={Zhou, Boyu and Zhang, Yichen and Chen, Xinyi and Shen, Shaojie},
journal={IEEE Robotics and Automation Letters},
volume={6},
number={2},
pages={779--786},
year={2021},
publisher={IEEE}
}
```
Please kindly star :star: this project if it helps you. We take great efforts to develope and maintain it :grin::grin:.
## Table of Contents
- [FUEL](#fuel)
- [Table of Contents](#table-of-contents)
- [Quick Start](#quick-start)
- [Exploring Different Environments](#exploring-different-environments)
- [Creating a _.pcd_ Environment](#creating-a-pcd-environment)
- [Acknowledgements](#acknowledgements)
## Quick Start
This project has been tested on Ubuntu 18.04(ROS Melodic) and 20.04(ROS Noetic).
Firstly, you should install __nlopt v2.7.1__:
```
git clone -b v2.7.1 https://github.com/stevengj/nlopt.git
cd nlopt
mkdir build
cd build
cmake ..
make
sudo make install
```
Next, you can run the following commands to install other required tools:
```
sudo apt-get install libarmadillo-dev
```
Then simply clone and compile our package (using ssh here):
```
cd ${YOUR_WORKSPACE_PATH}/src
git clone git@github.com:HKUST-Aerial-Robotics/FUEL.git
cd ../
catkin_make
```
After compilation you can start a sample exploration demo. Firstly run ```Rviz``` for visualization:
```
source devel/setup.bash && roslaunch exploration_manager rviz.launch
```
then run the simulation (run in a new terminals):
```
source devel/setup.bash && roslaunch exploration_manager exploration.launch
```
By default you can see an office-like environment. Trigger the quadrotor to start exploration by the ```2D Nav Goal``` tool in ```Rviz```. A sample is shown below, where unexplored structures are shown in grey and explored ones are shown in colorful voxels. The FoV and trajectories of the quadrotor are also displayed.
## Exploring Different Environments
The exploration environments in our simulator are represented by [.pcd files](https://pointclouds.org/documentation/tutorials/pcd_file_format.html).
We provide several sample environments, which can be selected in [simulator.xml](fuel_planner/exploration_manager/launch/simulator.xml):
```xml
```
Other examples are listed below.
_office2.pcd_:
_office3.pcd_:
_pillar.pcd_:
If you want to use your own environments, simply place the .pcd files in [map_generator/resource](uav_simulator/map_generator/resource), and follow the comments above to specify it.
You may also need to change the bounding box of explored space in [exploration.launch](https://github.com/HKUST-Aerial-Robotics/FUEL/blob/main/fuel_planner/exploration_manager/launch/exploration.launch):
```xml
```
To create your own .pcd environments easily, check the [next section](#creating-a-pcd-environment).
## Creating a _.pcd_ Environment
We provide a simple tool to create .pcd environments.
First, run:
```
rosrun map_generator click_map
```
Then in ```Rviz```, use the ```2D Nav Goal``` tool (shortcut G) to create your map. Two consecutively clicked points form a wall.
An example is illustrated:
After you've finished, run the following node to save the map in another terminal:
```
rosrun map_generator map_recorder ~/
```
Normally, a file named __tmp.pcd__ will be saved at ```~/```. You may replace ```~/``` with any locations you want.
Lastly, you can use this file for exploration, as mentioned [here](#exploring-different-environments).
## Acknowledgements
We use **NLopt** for non-linear optimization and use **LKH** for travelling salesman problem.