Fast-Racing

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An Open-source Strong Baseline for SE(3) Planning in Autonomous Drone Racing

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# Fast-Racing

An Open-source Strong Baseline for SE(3) Planning in Autonomous Drone Racing

## 0. Overview
**Fast-Racing** is a strong baseline that focuses on high-quality and extremely aggressive SE(3) trajectory generation.

The back-end optimization is a parallel extension of [__GCOPTER__](https://github.com/ZJU-FAST-Lab/GCOPTER) for drone racing, also powered by [__MINCO__](https://arxiv.org/pdf/2103.00190.pdf).

**Related Paper**:

Please cite __BOTH__ papers below if this repo helps you.

- [Fast-Racing: An Open-source Strong Baseline for SE(3) Planning in Autonomous Drone Racing](https://arxiv.org/abs/2105.10276), Zhichao Han, Zhepei Wang, Neng Pan, Yi Lin, Chao Xu, and Fei Gao
- [Geometrically Constrained Trajectory Optimization for Multicopters](https://arxiv.org/abs/2103.00190), Zhepei Wang, Xin Zhou, Chao Xu, and Fei Gao

**Video Links**: [youtube](https://www.youtube.com/watch?v=kjSU2vXCXXg) or [bilibili](https://www.bilibili.com/video/BV1sq4y1779e/).

![](https://raw.githubusercontent.com/ZJU-FAST-Lab/Fast-Racing/main/figs/1.gif)

![](https://raw.githubusercontent.com/ZJU-FAST-Lab/Fast-Racing/main/figs/2.gif)

## 1. Setup
All the tests are conducted in the Linux environment on a computer equipped with an Intel Core i7-10700 CPU and a GeForce RTX 2060 GPU.

Moreover, Our software is developed and tested in Ubuntu 18.04, 20.04 with ROS installed.

ROS can be installed here: [ROS Installation](http://wiki.ros.org/ROS/Installation).

Also,  we need to install `gcc>=8.0.0` and `Eigen>=3.3.0`.

(**Notion:** if you are using Ubuntu 20.04, just ignore this step because higher version of  ` gcc` and `Eigen` have been installed while you are installing ROS; but if you are using Ubuntu18.04, you need to update these versions.)

### Install `gcc>=8.0.0:`

1. Type the following command to install it.

```
    sudo apt-get install gcc-8 g++-8
    ```

2. Verify installation by:

```
    gcc-8 --version
    ```

### Install `Eigen>=3.3.0`

You can download the source code package from [Eigen Installation](https://eigen.tuxfamily.org/index.php?title=Main_Page).

Please make sure to install the **correct version**.

Your can run the following command to check `Eigen` version.

```
sudo gedit /usr/include/eigen3/Eigen/src/Core/util/Macros.h
```

## 2. Download Track Binaries

1. Download the [**settings.json**](https://github.com/ZJU-FAST-Lab/Fast-Racing/releases/tag/v1.0) and move it to`~/Documents/AirSim/settings.json`.

2. Download any one of the tracks such as [**Zhangjiajie.zip**](https://github.com/ZJU-FAST-Lab/Fast-Racing/releases/tag/v1.0), and unzip it.

3. Test it. Take `Zhangjiajie` as an example. Open a terminal window, `cd` to `Zhangjiajie/`directory. and type the following command:

```
   ./run.sh -windowed
   ```

## 3. Build on ROS

1. Install the dependence.

```
   sudo apt-get install libarmadillo-dev
   sudo apt-get install ros-${ROS_VERSION_NAME}-octomap*
   sudo apt-get install ros-${ROS_VERSION_NAME}-tf2-sensor-msgs ros-${ROS_VERSION_NAME}-tf2-geometry-msgs ros-${ROS_VERSION_NAME}-mavros*
   ```

where `${ROS_VERSION_NAME}` is the name of your ROS release.

2. Create an empty new workspace and clone this repository to your workspace:

```
   cd ~/your_catkin_ws/src
   git clone https://github.com/ZJU-FAST-Lab/Fast-Racing
   cd ..
   ```

3. Compile it.

If your default gcc isn't 8 or greater (check using `gcc --version`), then compilation will fail. In that case, use `gcc-8` explicitly as follows.

```
   catkin_make -DCMAKE_C_COMPILER=gcc-8 -DCMAKE_CXX_COMPILER=g++-8 
   ```

## 4. Run the Simulation

1. Run the track binary.

Before the simulation racing, you need to run the rack binary.

Open a terminal window, `cd` to `Zhangjiajie/`, `Urbancity/` or `Moderncity/` directory. and type the following command:

```
    ./run.sh -windowed
    ```

2. Run the planner.

Open a new terminal window, `cd ` to `~/your_catkin_ws/` and type:

```
    source devel/setup.bash
    ```

Then, run the script corresponding to the track. Take `Zhangjiajie` as the example:

```
    ./zhangjiajie.sh 
    ```

After the global map is set up, you can use the **3D Nav Goal** in **RVIZ** to trigger the planning.

Here is an example:

![](https://raw.githubusercontent.com/ZJU-FAST-Lab/Fast-Racing/main/figs/3.gif)

## 5. Use GPU or Not

Packages in this repo, **plan_manage** have GPU, CPU two different versions. By default, they are in CPU version for better compatibility. By changing

```
set(ENABLE_CUDA false)
 ```

in the _CMakeList.txt_ in ****plan_manage**** packages, to

```
set(ENABLE_CUDA true)
 ```

CUDA will be turned-on to use the parallel architecture to speed up the computation of the optimization.

Please remember to also change the 'arch' and 'code' flags in the line of

```
    set(CUDA_NVCC_FLAGS 
      -gencode arch=compute_70,code=sm_70;
    ) 
```

in _CMakeList.txt_, if you encounter compiling error due to different Nvidia graphics card you use. You can check the right code [here](https://github.com/tpruvot/ccminer/wiki/Compatibility).

Don't forget to re-compile the code!

For installation of CUDA, please go to [CUDA ToolKit](https://developer.nvidia.com/cuda-toolkit)

## 6. Licence
The source code is released under [GPLv3](http://www.gnu.org/licenses/) license.

## 7. Maintaince

For any technical issue, please contact Zhichao HAN (zhichaohan@zju.edu.cn) or [Zhepei WANG](https://zhepeiwang.github.io/) (wangzhepei@live.com).

For commercial inquiries, please contact [Fei GAO](https://ustfei.com/) (fgaoaa@zju.edu.cn).

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