# Quick Start within 3 Minutes
Compiling tests passed on ubuntu **16.04, 18.04 and 20.04** with ros installed.
You can just execute the following commands one by one.
```
sudo apt-get install libarmadillo-dev
git clone https://github.com/ZJU-FAST-Lab/ego-planner-swarm.git
cd ego-planner-swarm
catkin_make -j1
source devel/setup.bash
roslaunch ego_planner simple_run.launch
```
<!If your network to github is slow, We recommend you to try the gitee repository [https://gitee.com/iszhouxin/ego-planner-swarm](https://gitee.com/iszhouxin/ego-planner-swarm). They synchronize automatically./>
If you find this work useful or interesting, please kindly give us a star :star:, thanks!:grinning:
# Acknowledgements
- This work extends [EGO-Planner](https://github.com/ZJU-FAST-Lab/ego-planner) to swarm navigation.
# EGO-Swarm
EGO-Swarm: A Fully Autonomous and Decentralized Quadrotor Swarm System in Cluttered Environments
**EGO-Swarm** is a decentralized and asynchronous systematic solution for multi-robot autonomous navigation in unknown obstacle-rich scenes using merely onboard resources.
<p align = "center">
<img src="pictures/title.gif" width = "413" height = "232" border="5" />
<img src="pictures/outdoor.gif" width = "413" height = "232" border="5" />
<img src="pictures/indoor1.gif" width = "413" height = "232" border="5" />
<img src="pictures/indoor2.gif" width = "413" height = "232" border="5" />
</p>
**Video Links:** [YouTube](https://www.youtube.com/watch?v=K5WKg8meb94&ab_channel=FeiGao), [bilibili](https://www.bilibili.com/video/BV1Nt4y1e7KD) (for Mainland China)
## 1. Related Paper
EGO-Swarm: A Fully Autonomous and Decentralized Quadrotor Swarm System in Cluttered Environments, Xin Zhou, Jiangchao Zhu, Hongyu Zhou, Chao Xu, and Fei Gao (Accepted by ICRA2021). [Preprint](https://arxiv.org/abs/2011.04183v1), [Science report](https://www.sciencemag.org/news/2020/12/watch-swarm-drones-fly-through-heavy-forest-while-staying-formation).
## 2. Standard Compilation
**Requirements**: ubuntu 16.04, 18.04 or 20.04 with ros-desktop-full installation.
**Step 1**. Install [Armadillo](http://arma.sourceforge.net/), which is required by **uav_simulator**.
```
sudo apt-get install libarmadillo-dev
```
**Step 2**. Clone the code from github or gitee. This two repositories synchronize automaticly.
From github,
```
git clone https://github.com/ZJU-FAST-Lab/ego-planner-swarm.git
```
<!--Or from gitee,
```
git clone https://gitee.com/iszhouxin/ego-planner-swarm.git
```
/-->
**Step 3**. Compile,
```
cd ego-planner
catkin_make -DCMAKE_BUILD_TYPE=Release -j1
```
**Step 4**. Run.
In a terminal at the _ego-planner-swarm/_ folder, open the rviz for visuallization and interactions
```
source devel/setup.bash
roslaunch ego_planner rviz.launch
```
In another terminal at the _ego-planner-swarm/_, run the planner in simulation by
```
source devel/setup.bash
roslaunch ego_planner swarm.launch
```
Then you can follow the gif below to control the drone.
<p align = "center">
<img src="pictures/sim_demo.gif" width = "640" height = "360" border="5" />
</p>
## 3. Using an IDE
We recommend using [vscode](https://code.visualstudio.com/), the project file has been included in the code you have cloned, which is the _.vscode_ folder.
This folder is **hidden** by default.
Follow the steps below to configure the IDE for auto code completion & jump.
It will take 3 minutes.
**Step 1**. Install C++ and CMake extentions in vscode.
**Step 2**. Re-compile the code using command
```
catkin_make -DCMAKE_BUILD_TYPE=Release -DCMAKE_EXPORT_COMPILE_COMMANDS=Yes
```
It will export a compile commands file, which can help vscode to determine the code architecture.
**Step 3**. Launch vscode and select the _ego-planner_ folder to open.
```
code ~/<......>/ego-planner-swarm/
```
Press **Ctrl+Shift+B** in vscode to compile the code. This command is defined in _.vscode/tasks.json_.
You can add customized arguments after **"args"**. The default is **"-DCMAKE_BUILD_TYPE=Release"**.
**Step 4**. Close and re-launch vscode, you will see the vscode has already understood the code architecture and can perform auto completion & jump.
## 4. Use GPU or Not
Packages in this repo, **local_sensing** 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 **local_sensing** packages, to
```
set(ENABLE_CUDA true)
```
CUDA will be turned-on to generate depth images as a real depth camera does.
Please remember to also change the 'arch' and 'code' flags in the line of
```
set(CUDA_NVCC_FLAGS
-gencode arch=compute_61,code=sm_61;
)
```
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!
**local_sensing** is the simulated sensors. If ```ENABLE_CUDA``` **true**, it mimics the depth measured by stereo cameras and renders a depth image by GPU. If ```ENABLE_CUDA``` **false**, it will publish pointclouds with no ray-casting. Our local mapping module automatically selects whether depth images or pointclouds as its input.
For installation of CUDA, please go to [CUDA ToolKit](https://developer.nvidia.com/cuda-toolkit)
## 5. Use Drone Simulation Considering Dynamics or Not
Typical simulations use a dynamic model to calculate the motion of the drone under given commands.
However, it requires continous iterations to solver a differential equation, which consumes quite a lot computation.
When launching a swarm of drones, this computation burden may cause significant lag.
On an i7 9700KF CPU I use, 15 drones are the upper limit.
Therefore, for compatibility and scalability purposes, I use a "[fake_drone](https://github.com/ZJU-FAST-Lab/ego-planner-swarm/tree/master/src/uav_simulator/fake_drone)" package to convet commands to drone odometry directly by default.
If you want to use a more realistic quadrotor model, you can un-comment the node `quadrotor_simulator_so3` and `so3_control/SO3ControlNodelet` in [simulator.xml](https://github.com/ZJU-FAST-Lab/ego-planner-swarm/blob/master/src/planner/plan_manage/launch/simulator.xml) to enable quadrotor simulation considering dynamics.
Please don't forget to comment the package `poscmd_2_odom` right after the above two nodes.
## 6. Utilize the Full Performance of CPU
The computation time of our planner is too short for the OS to increase CPU frequency, which makes the computation time tend to be longer and unstable.
Therefore, we recommend you to manually set the CPU frequency to the maximum.
Firstly, install a tool by
```
sudo apt install cpufrequtils
```
Then you can set the CPU frequency to the maximum allowed by
```
sudo cpufreq-set -g performance
```
More information can be found in [http://www.thinkwiki.org/wiki/How_to_use_cpufrequtils](http://www.thinkwiki.org/wiki/How_to_use_cpufrequtils).
Note that CPU frequency may still decrease due to high temperature in high load.
<!--
# Improved ROS-RealSense Driver
We modified the ros-relasense driver to enable the laser emitter strobe every other frame, allowing the device to output high quality depth images with the help of emitter, and along with binocular images free from laser interference.
<p align = "center">
<img src="pictures/realsense.PNG" width = "640" height = "158" border="5" />
</p>
This ros-driver is modified from [https://github.com/IntelRealSense/realsense-ros](https://github.com/IntelRealSense/realsense-ros) and is compatible with librealsense2 2.30.0.
Tests are performed on Intel RealSense D435 and D435i.
Parameter ```emitter_on_off``` is to turn on/off the added function.
Note that if this function is turned on, the output frame rate from the device will be reduced to half of the frame rate you set, since the device uses half of the stream for depth estimation and the other half as bi
ego-planner-swarm 注释版 （631个子文件） 
solar_system.agr 226KB .catkin_workspace 98B multi_map_server.cc 5KB multi_map_visualization.cc 3KB local_sensing_node.cfg 564B CHANGELOG 171B .clang-format 290B FindCUDA.cmake 54KB run_nvcc.cmake 10KB FindEigen.cmake 8KB arch.cmake 5KB FindmvIMPACT.cmake 5KB FindGSL.cmake 4KB parse_cubin.cmake 4KB make2cmake.cmake 3KB FindEigen.cmake 3KB color.cmake 512B cmake_modules.cmake 136B FindEigen.cmake~ 3KB config 278B bspline_optimizer.cpp 68KB grid_map.cpp 32KB ego_replan_fsm.cpp 29KB planner_manager.cpp 23KB stepper_copying.cpp 23KB bridge_node.cpp 21KB maps.cpp 21KB aerialmap_display.cpp 16KB drone_detector.cpp 16KB odom_visualization.cpp 16KB probmap_display.cpp 15KB symplectic_steppers.cpp 14KB pose_utils.cpp 13KB random_forest_sensing.cpp 13KB multi_probmap_display.cpp 13KB pcl_render_node.cpp 12KB gamelikeinput.cpp 12KB stepper_with_units.cpp 11KB dyn_a_star.cpp 11KB uniform_bspline.cpp 11KB euroc.cpp 11KB quadrotor_simulator_so3.cpp 11KB raycast.cpp 10KB Quadrotor.cpp 10KB polynomial_traj.cpp 10KB default_operations.cpp 9KB integrate.cpp 9KB waypoint_generator.cpp 9KB obj_predictor.cpp 9KB stepper_with_ranges.cpp 9KB planning_visualization.cpp 9KB obj_generator.cpp 8KB so3_control_nodelet.cpp 8KB implicit_euler_mtl.cpp 7KB fusion_algebra.cpp 7KB adams_bashforth.cpp 7KB range_algebra.cpp 7KB ces_randommap.cpp 7KB runge_kutta_controlled_concepts.cpp 7KB traj_server.cpp 6KB integrate_implicit.cpp 6KB integrate_times.cpp 6KB stepper_details.cpp 6KB pointcloud_render_node.cpp 6KB resizing_lattice.cpp 5KB runge_kutta_concepts.cpp 5KB pose_tool.cpp 5KB runge_kutta_error_concepts.cpp 5KB solar_system.cpp 5KB gmp_integrate.cpp 5KB resizing.cpp 5KB check_gmp.cpp 5KB runge_kutta.cpp 5KB heun.cpp 5KB fpu.cpp 4KB two_dimensional_phase_lattice.cpp 4KB perlinnoise.cpp 4KB rosenbrock4.cpp 4KB harmonic_oscillator.cpp 4KB black_hole.cpp 4KB uav_utils_test.cpp 4KB bulirsch_stoer.cpp 4KB gauss_packet.cpp 4KB phase_oscillator_ensemble.cpp 4KB spreading.cpp 4KB SO3Control.cpp 4KB generic_error_stepper.cpp 4KB generation.cpp 3KB decode_msgs.cpp 3KB trivial_state.cpp 3KB harmonic_oscillator_units.cpp 3KB goal_tool.cpp 3KB adams_bashforth_moulton.cpp 3KB stochastic_euler.cpp 3KB adams_moulton.cpp 3KB gradient_descent_optimizer.cpp 3KB stiff_system.cpp 3KB mockamap.cpp 3KB chaotic_system.cpp 3KB lorenz_point.cpp 3KB共 631 条
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资源评论
张景淇2023-07-29我发现这个文件中的注释非常细致，通过仔细阅读，我可以深入了解“ego-planner-swarm”的原理和功能，为我的研究工作提供了很大的帮助。
牛站长2023-07-29注释版的“ego-planner-swarm”文件对于新手来说非常友好，它用简洁的语言和例子说明了每一步的操作，使整个过程变得更加可控和易于理解。
梁肖松2023-07-29这个文件的注释提供了很多关键的信息，使我对“ego-planner-swarm”有了更清晰的认识，并能够更快地上手使用它。
家的要素2023-07-29我觉得这个文件的注释很详细，不仅给出了代码的解释，还提供了一些实际应用场景的示例，非常有助于我们理解其功能。
吹狗螺的简柏承2023-07-29这个文件确实给我们提供了很多有用的注释和指导，让研究者们能够更好地理解和应用“ego-planner-swarm”。
