The environment is meant for leveraging system development and robot deployment for autonomous ground and aerial navigation. Integrating Gazebo and Unity simulators and containing autonomous navigation modules such as collision avoidance, waypoint following, and assistive teleoperation, users can develop autonomous navigation systems and later on deploy to real flying robots with minor sim-to-real gap.

Multi-scale Complex Environments

The development environment includes 29 multi-scale scenes with different complexity. Including 17 cluttered indoor photo-realistic scenes for ground autonomous navigation, 12 large-scale complex  scenes for aerial autonomous navigation and aerial-ground autonomy.  The environment also supports both Gazebo and Unity. Because scenes are using  either ROS's built-in simulator or executable file format, each of them can be quickly deployed and tested in 10 minutes (details in Quick Start and Simulation Environments).

Our development environment supports both ground and aerial vehicles. The guidance for configuring the Aerial Autonomy Environment can be found below. Instruction for setting up the Ground Autonomy Environment is available here. 

High-performance Depolyable Autonomy Stack

The Autonomy Stack takes <10% CPU load overall, <1ms and <1% CPU for local path planning and execution, <20% CPU load for real-time global path planning. It can also be directly deployed to real robots for various field applications.

Extendable to High-level Robotics and AI Tasks

Different field applications have already validated the high extendability of the platform.

7 Minutes Quick Start

The repository has been tested in Ubuntu 20.04 with ROS Noetic.  Install dependencies with command below.

sudo apt install libusb-dev python-yaml python-is-python3 # may need 'sudo apt update' first

git clone https://github.com/Bottle101/aerial_autonomy_development_environment.git

In a terminal,  go to the folder and compile.

cd aerial_autonomy_development_environment

catkin_make

Download factory (default) or any of our Unity environment models and unzip the files to the 'src/vehicle_simulator/mesh' folder.

Launch the system.

source ./devel/setup.bash  

roslaunch vehicle_simulator system_unity.launch  

Now, users can send a waypoint by 3 step: 1. click the 'Waypoint3D' button in RVIZ; 2. click a point in the map, hold the left mouse button and scroll the mouse wheel to adjust the height;  3. release the mouse.  You can also use virtual joystick in the left side to achieve assistive teleoperation.

(Optional) If you want to use Gazebo, please follow the instruction below.

# run a script to download environment models for Gazebo

./src/vehicle_simulator/mesh/download_environments.sh

# launch

source ./devel/setup.bash 

roslaunch vehicle_simulator system_gazebo.launch

Simulation Environments

Change Environment

The repository contains a set of simulation environments of different types and scales. To launch the system with a particular environment, change the config in src/vehicle_simulator/launch/system_unity.launch, Line 4 :

<arg name="map_name" default="SCENE_TO_USE"/> 

#factory, village, urban_city, town, old_town, office_building_1, office_building_2

If you are using Gazebo, change src/vehicle_simulator/launch/system_gazebo.launch, change the Line 3 :

<arg name="map_name" default="SCENE_TO_USE"/> # garage, indoor, campus, tunnel, forest

The simulation environments are kept in 'src/vehicle_simulator/meshes'.  A quick overview is shown below. In each environment, 'map.ply' is a 3D pointcloud of the overall map accessible through software like CloudCompare and MeshLab. Autonomous navigation systems can use the point cloud as a prior map if needed.

Environment Overview

Gray font means the environment is in Unity, Orange means Gazebo.

For Gazebo, we only provide Lidar for navigation, while users can still use depth camera for other tasks by subscribing /rgbd_camera/depth/points.

Navigation Modes

Details about our ground-based autonomy development platform can be found in this page: www.cmu-exploration.com/ 

Credits

The code is based on Ground-based Autonomy Base Repository and Aerial Autonomy Base Repository by Ji Zhang and Chao Cao at CMU.

Tunnel network environment is provided by Tung Dang at University of Nevada, Reno.

velodyne_simulator and joystick_drivers packages are from open-source releases.

Main contributor: Botao He, Guofei Chen, Ji Zhang

Links

Aerial Navigation Development Environment: Leveraging system development and robot deployment for aerial autonomy.

AI Meets Autonomy: Vision, Language, and Autonomous Systems Tutorial & CMU VLA Challenge

References

[1] B. He, G. Chen, C. Fermuller, Y. Aloimonos and J. Zhang. Air-FAR: Fast and Adaptable Routing for Aerial Navigation in Large-scale Complex Unknown Environments. arXiv preprint arXiv:2409.11188 (2024). [PDF]

[2] B. He, G. Chen, W. Wang, J. Zhang, C. Fermuller, and Y. Aloimonos. Interactive-FAR: Interactive, Fast and Adaptable Routing for Navigation Among Movable Obstacles in Complex Unknown Environments.  IEEE/RSJ Intl. Conf. on Intelligent Robots and Systems (IROS) 2024. [PDF]

[3] F. Yang, C. Cao, H. Zhu, J. Oh, and J. Zhang. FAR Planner: Fast, Attemptable Route Planner using Dynamic Visibility Update. IEEE/RSJ Intl. Conf. on Intelligent Robots and Systems (IROS). Kyoto, Japan, Oct. 2022. Best Student Paper Award. (PDF) (Video) 

[4] C. Cao, H. Zhu., F. Yang, Y. Xia, H. Choset, J. Oh, and J. Zhang. Autonomous exploration development environment and the planning algorithms. In 2022 International Conference on Robotics and Automation (ICRA) (pp. 8921-8928). IEEE.[PDF]