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RACER

RACER, a RApid Collaborative ExploRation approach using a fleet of decentralized UAVs.

We develop a fully decentralized approach for exploration tasks using a fleet of quadrotors. The proposed system features robustness against unstable communication and a high degree of coordination. 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 Transactions on Robotics and is selected as the 2023 IEEE TRO Best Paper!

Try Quick Start to run a demo in a few minutes!

<p align="center"> <img src="files/racer1.gif" width = "400" height = "225"/> <img src="files/racer2.gif" width = "400" height = "225"/> </p>

Complete video: video.

Authors: Boyu Zhou from SYSU STAR Group, and Hao Xu, Shaojie Shen from the HKUST Aerial Robotics Group.

Please cite our paper if you use this project in your research:

@article{zhou2023racer,
  title={Racer: Rapid collaborative exploration with a decentralized multi-uav system},
  author={Zhou, Boyu and Xu, Hao and Shen, Shaojie},
  journal={IEEE Transactions on Robotics},
  year={2023},
  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

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

After that, you need to install LKH-3(LKH-3.0.6 version is recommended) with the following commands. Please make sure the executable file LKH is correctly placed at /usr/local/bin.

wget http://akira.ruc.dk/~keld/research/LKH-3/LKH-3.0.6.tgz
tar xvfz LKH-3.0.6.tgz
cd LKH-3.0.6
make
sudo cp LKH /usr/local/bin
<!-- To simulate the depth camera, we use a simulator based on CUDA Toolkit. Please install it first following the [instruction of CUDA](https://developer.nvidia.com/zh-cn/cuda-toolkit). After successful installation, in the **local_sensing** package in **uav_simulator**, remember to change the 'arch' and 'code' flags in CMakelist.txt according to your graphics card devices. You can check the right code [here](https://github.com/tpruvot/ccminer/wiki/Compatibility). For example: ``` set(CUDA_NVCC_FLAGS -gencode arch=compute_61,code=sm_61; ) ``` -->

Then simply clone and compile our package (using ssh here):

cd ${YOUR_WORKSPACE_PATH}/src
git clone https://github.com/SYSU-STAR/RACER.git
cd ../ 
catkin_make

After compilation you can start a sample swarm 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 swarm_exploration.launch

By default you can see a pillar-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.

<!-- You will find a cluttered scene to be explored (20m x 12m x 2m) and the drone . You can trigger the exploration to start by A sample simulation is shown in the figure. The unknown obstacles are shown in grey, while the frontiers are shown as colorful voxels. The planned and executed trajectories are also displayed. --> <p id="demo1" align="center"> <img src="files/racer3.gif" width = "600" height = "325"/> </p>

Exploring Different Environments

The exploration environments in our simulator are represented by .pcd files. We provide several sample environments, which can be selected in swarm_exploration.launch:

  <!-- Change office.pcd to specify the exploration environment -->
  <!-- We provide office.pcd, office2.pcd, office3.pcd and pillar.pcd in this repo -->
  <node pkg ="map_generator" name ="map_pub" type ="map_pub" output = "screen" args="$(find map_generator)/resource/pillar.pcd"/>

Other examples can be found in map_generator/resource. If you want to use your own environments, simply place the .pcd files in 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:

  <arg name="box_min_x" value="-10.0"/>
  <arg name="box_min_y" value="-15.0"/>
  <arg name="box_min_z" value=" 0.0"/>
  <arg name="box_max_x" value="10.0"/>
  <arg name="box_max_y" value="15.0"/>
  <arg name="box_max_z" value=" 2.0"/>

To create your own .pcd environments, you can use this tool.

<!-- ## 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:--> <p id="demo5" align="center"> <img src="files/create_map.gif" width = "600" height = "340"/> </p> <!-- 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.