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Framework for Autonomous Navigation of Micro Aerial Vehicles

Authors	Email
Leo Campos	leobardo.e.campos.macias@intel.com
Rafael de la Guardia	rafael.de.la.guardia@intel.com
Lead Architect	Email
Leo Campos	leobardo.e.campos.macias@intel.com

This package provides the implementation of a framework for autonomous drone navigation towards a given goal within an unknown cluttered environment while fulfilling dynamical constraints.

It can be simulated using RotorS or implemented in actual drones. It has been fully tested on Aero Ready to Fly Drone.

We provide the instructions necessary for getting started.

If you are using this framework implementation within the research for your publication, please cite:

@article{Campos2020,
author = {Campos-Macías, Leobardo and Aldana-López, Rodrigo and de la Guardia, 
	Rafael and Parra-Vilchis, José I. and Gómez-Gutiérrez, David},
title = {Autonomous navigation of MAVs in unknown cluttered environments},
journal = {Journal of Field Robotics},
volume = {n/a},
number = {n/a},
pages = {},
doi = {10.1002/rob.21959},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/rob.21959},
eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1002/rob.21959}
}

A video of some real-world experiments is available at: https://youtu.be/79IFfQfvXLE

Installation Instructions - Ubuntu 18.04 with ROS Melodic

1. Install ROS Melodic with dependencies:

$ sudo sh -c 'echo "deb http://packages.ros.org/ros/ubuntu $(lsb_release -sc) main" > /etc/apt/sources.list.d/ros-latest.list'
$ wget http://packages.ros.org/ros.key -O - | sudo apt-key add -
$ sudo apt update
$ sudo apt install ros-melodic-desktop-full ros-melodic-joy ros-melodic-octomap-ros ros-melodic-mavlink python-wstool python-catkin-tools protobuf-compiler libgoogle-glog-dev ros-melodic-control-toolbox python-rosdep python-rosinstall python-rosinstall-generator python-wstool build-essential ros-melodic-gps-common ros-melodic-gps-umd ros-melodic-gpsd-client
$ sudo rosdep init
$ rosdep update
$ echo "source /opt/ros/melodic/setup.bash" >> ~/.bashrc
$ source ~/.bashrc

2. Init the ros workspace and clone necessary packages

$ mkdir -p ~/catkin_ws/src
$ cd ~/catkin_ws/src
$ catkin_init_workspace  # initialize your catkin workspace
$ wstool init
$ wget https://raw.githubusercontent.com/IntelLabs/autonomousmavs/master/dependencies.rosinstall
$ wstool merge dependencies.rosinstall
$ wstool update

3. The workspace is built with catkin tools

$ cd ~/catkin_ws/
$ catkin build

4. Add sourcing

$ source devel/setup.bash

Maze Simulation

Launch the maze simulation environment and the quad-copter AscTec Hummingbird model with a RealSense R200 attached

$ roslaunch ootp_simulator maze.launch

The simulator starts by default in paused mode. To start the navigation algorithm, open another terminal and type:

$ source ~/catkin_ws/devel/setup.bash
$ roslaunch ootp_simulator maze_node.launch

You should see the drone navigating towards the exit of the maze.

The following topics display the navigation process:

• /map [visualization_msgs::MarkerArray]: Displays the currently occupied map
• /path [nav_msgs::Path]: Displays the proposed path to the goal
• /path_consumed [nav_msgs::Path]: Displays the path followed by the drone

You have to change the frame world to "world" in Rviz to see the topics.

The navigation framework only needs as input the odometry, given by RotorS plugin, and the depth image, provided by Real Sense plugin, with the two topics:

• /rs0r200/camera/depth/image_raw [sensor_msgs::ImageConstPtr]
• /hummingbird/odometry_sensor1/odometry [nav_msgs::Odometry]

and outputs the next state that, for simulation purposes, consists only on the next position and yaw:

• /hummingbird/command/trajectory [trajectory_msgs::MultiDOFJointTrajectory]

Finally, the file ootp_simulator/config/simulation_maze.yaml is used to configure the parameters used by the algorithm, such as the camera calibration or the initial and final given poses.

Real World Experiments

Real world experiments were performed using the Aero Ready to Fly Drone. A custom fly controller was implemented requiring the position and two derivatives.

Depth image was provided by the Intel RealSense D435, position was provided by the Intel RealSense Tracking Camera T265, and orientation with the internal BMI160-IMU:

• /depth/image_raw [sensor_msgs::ImageConstPtr]
• /odom_control/pose [geometry_msgs::PoseStamped]
• /euler [geometry_msgs::Vector3Stamped]

Position, velocity, and acceleration for x, y, z, and yaw states are given by:

• /aero/ref_pos [geometry_msgs::PoseStamped]
• /aero/ref_vel [geometry_msgs::PoseStamped]
• /aero/ref_acc [geometry_msgs::PoseStamped]

Finally, the file ootp_ros/config/aero.yaml is used to configure the parameters used by the algorithm, such as the camera calibration or the initial and final given poses.

License

This project is licensed under the BSD-3-Clause see the LICENSE file for details