Autonomy Development Environment

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.

Key Features

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.

Ultra Light-weight Local Planner

The autonomy stack includes specified local planners for different vehicle configurations, enabling planning dynamically feasible trajectories for autonomous navigation while simultaneously avoiding or interacting with obstacles. Leveraging a pre-calculated motion primitive library, the stack remains lightweight (  <0.5% CPU occupancy on a single core)  while ensuring robust performance.

Aerial Local Planner

Ground Local Planner

Interactive Local Planner

Real-time Global Planner

We provide our global planner series, called FAR planners, for long-range navigation in unknown, large-scale and/or complex environments.  By utilizing the sparse polygon map representation, the global planner can plan a long path (>300m) in real-time (<10ms). When navigating in known map, the planner uses the built visibility graph, and when navigating in unknown space, the planner attempts to discover a way to the goal.  Details can be found in other sub-pages of the website.

Left: Air-FAR Planner; Middle: FAR Planner; Right: Interactive-FAR Planner

Extendable to High-level Robotics and AI Tasks

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

Autonomous Exploration

Visual-Language Navigation

Aerial Autonomy Development Environment

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.

Change Config

For different environment, users may change the configurations for both the autonomy stack and the FAR planners together to achieve best performance. For different encironments, the suggested configurations are shown below.

For autonomy stack, users can change the config in src/vehicle_simulator/launch/system_unity.launch, Line 5  or src/vehicle_simulator/launch/system_gazebo.launch, Line 3  if you are using Gazebo.

<arg name="config" default="CONFIG_TO_USE"/> # indoor, outdoor

For FAR planners, you can change the cooresponding config in far_planner/launch/far_planner.launch or airfar_planner/launch/airfar.launch, Line  7:

<arg name="config_file" default="CONFIG_TO_USE"/> # default or outdoor

Environment Overview

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

Campus (340m x 340m)

Indoor Corridors (130m x 100m)

Multi-storage Garage (140m x 130m, 5 floors)

Tunnel Network (330m x 250m)

Forest (150m x 150m)

Factory (300m x 300m)

Village (240m x 240m)

Urban City (500m x 500m)

Town (800m x 800m)

Old Town (140m x 90m)

Office Building 1 (30m x 30m, Aerial-Ground)

Office Building 2 (50m x 40m, Aerial-Ground)

Sensor Setup

For Unity, we provide both Lidar and Depth Camera for navigation, we also provide a panoramic RGB camera with semantic segmentation for other tasks. To switch between lidar and depth camera, users can simply click the toggle switch on the upper-right side of the simulator. 

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

Waypoint

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 altitude, as shown in the figure below;  3. release the mouse. 

Hold the left button & scroll to adjust altitude

Release the mouse and start navigation

Smart Joystick

Anytime during the navigation, users can use the control panel to navigate the vehicle by clicking the in the virtual joystick, or use a PS3/4 or Xbox controller. The system will switch to smart joystick mode - the vehicle tries to follow the virtual joystick command and avoid collisions at the same time. 

To resume navigation, press the 'Resume Navigation to Goal' button or use the 'Waypoint3D' button to set a new goal. If users wish to resume the navigation while also controlling the flying speed, hold the 'Waypoint-flight button' and adjust the speed using the right joystick at the same time.

Manual

Hold the 'Manual-flight button' below to switch to manual mode, note that all collision avoidance will be disabled.

Ground Autonomy Development Environment

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