Conference PaperPDF Available

Galileo Aid Drone: A System Integration for Autonomous Wildfire Assistants

Galileo Aid Drone: A System Integration for Autonomous Wildfire
Diego Calder´
on, Rodrigo Cordero, Ariel Gonz´
alez, Ali Lemus and Julio Fajardo
Abstract Mobile robots are playing an important role as
monitoring and immediate response services supporting risk
management systems for natural or artificial disasters. In this
work, an integration of systems based on a Unmanned Aerial
Vehicle collects relevant information and show the status of
a wildfire through an interactive dashboard, which can be
accessed by the risk and disaster management systems through
any computer. In addition, the system can identify people and
communities at risk by presenting their estimated geolocation
on a map, as well as the spread of the fire. In this way, it will
be possible to understand the magnitude of the disaster, and
thus develop better rescue and monitoring strategies.
Monitoring system, risk management, rescue robots.
Wildfires are a serious problem that has both economic
and environmental effects to the world, putting in danger
people, animals and property that are near or inside of
the affected area. They produce an economical lost leaving
people without their homes as well as environmental damage,
such as the water contamination in the near rivers [1].
The first responders struggle with several issues in order to
control the fire and to evacuate the zone to prevent the loss
of human lives and property. Some of the more challenging
issues is to monitor and predict the spread of the fire in
order to establish a plan to find and rescue the humans near
the affected area. Autonomous robots have become more
popular in recent years to assist first responders in natural and
artificial disasters, specially the Unmanned Aerial Vehicles
(UAVs). This, because of its small size, relatively low-cost
components and due its ability to carry cameras and sensors
on board that can help firefighters to explore the fire-affected
area from an aerial view. In this way, the UAV can acquire
relevant information about the wildfire and start looking for
hot spots such as the geolocation of people and communities
on risk, identify the best routes to evacuate them and plan
better strategies to contain the fire based on the observations
acquired by the system.
In this work, a quadrotor-based solution is presented to
assist first responders during a wildfire, giving them the
ability to quickly and continuously explore and monitor the
affected area using an interactive dashboard, which can be
accessed by the risk and disaster management systems using
any computer with a Linux distribution.
The architecture of this solution, as shown in Fig. 1, is
based-on a quadrotor with a Fligh Managment Unit (FMU)
RDDRONE-FMUK66, which has as NXP Kinetis K66 at
180MHz Microcontroller Unit (MCU), that runs the PX4
open source autopilot software stack [2]. Also, there is
a NXP NavQ as a companion computer which has the
following specifications: 2GB of RAM, a Quad-Core ARM
A53 at 1.8 GHz which runs a lite version of Ubuntu 20.04
Operating System. On other hand, there are three cameras
in the system, the first one is an Intel RealSense D435i
depth camera, which provides RGB-D images (depth sensor)
and orientation information through its integrated inertial
measurement unit (IMU); this camera is connected to the
NavQ using a USB type C port. The second one, is a
FLIR Lepton 2.5 Longwave Infrared (LWIR) Micro Thermal
Camera, which is connected to the NavQ board using its
SPI and I2C ports that are available on the NavQ board and
finally, there is a Google Coral RGB Camera that is also
connected to the NavQ using the MIPI CSI interface.
Furthermore, the system integration is made using the
Robotic Operating System (ROS) [3]. Thus, the rosmaster
and nodes to communicate with the cameras and the FMU
are running in the NavQ single board computer. In order to
detect people, animals and property at risk, YOLOv3 was
employed due its speed and lightweight, making it a good
option to be executed in a single board computer [4].
In a similar way, different computers can run ROS in a
distributed way, in order to connect to the ROS master that
is running in the NavQ and to access the information that
is published by the different nodes running on the NavQ.
Additionally, Mapviz is used as a visualization tool to show
a dashboard with the information that the drone is gathering
during the flight.
Fig. 1. Block diagram of the system architecture implemented on the
A. Hardware
1) Quadrotor: The UAV (shown in Fig. 2) is based on
a carbon fiber mechanical frame of approximately 500 mm
Fig. 2. Galileo Aid Drone during a test flight.
of diagonal size with four Brushless DC (BLDC) motors
of 920 kV with its respective Electronic Speed Controlles
(ESCs). The FMU is based on an ARM Cortex-M4 MCU and
is supported by the business-friendly open-source PX4 flight
stack. This software is in charge to manage the BLDC motors
drivers and the GPS NEO-M8N module. On the other hand,
the system has two RF communications modules, a telemetry
radio HGD-TELEM915, to establish communication with
any computer or tablet via MAVLink protocol and another
to connect with the remote control. Moreover a 4200mAh
Lithium-Polymer Battery supplies power to the system.
2) NXP NavQ: A companion computer is added to leave
the FMU just in charge of flight management. This single
board computer was chosen because it is designed to be
mounted on mobile robots having multiple useful peripheras,
providing access to I2C, UART and SPI communication
protocols which are more convenient to communicate with
the FMU and cameras. Furthermore, it’s capable of running
ROS nodes such as mavros to communicate with PX4 stack
using a UART port, and it provides WiFi and Bluetooth
connections that are useful to share information with other
3) Cameras: The system has two mounted cameras to
acquire aerial views of the scene, the first one is the LWIR
FLIR Lepton 2.5 camera to obtain and monitor thermal
images of the fire with a resolution of 80x60 pixels and a
spectral range with a longwave infrared of 8µm to 14 µm
see Fig. 3(b), and the second one is the Google Coral RGB
camera that is used to detect people and property at potential
risk. In addition, to avoid possible collisions with obstacles,
an Intel RealSense D435i is mounted. This camera provides
RGB and Depth images with a resolution up to 1920x1080
pixels at 30 fps for RGB and 1280x720 pixels at 90 fps for
B. Software
1) YOLOv3: This module is used because it is a state-of-
the art solution to do real-time object detection in images
using limited resources and it still very fast. Specifically, a
custom CNN was trained based on a subset of the COCO
Dataset using the categories that are more important to
identify in a disaster such as people, animals and property
as shown in Fig. 3(a).
2) Objects/People Detection and Localization: One of
the key tasks of the system is keeping track of the peo-
Fig. 3. Galileo aid drone aerial view. (a) Identification of a person using
YOLOv3, (b) Thermal image of an small fire.
ple/property. This task is implemented using the information
from the bounding boxes that are provided by YOLO as well
as feature matching techniques for aerial images. Moreover,
the characteristics of the camera and the data from the GPS
(longitude, latitude and altitude) are considered to make an
estimation of the coordinates of an object or a person and
its added to the system to keep track of it.
3) Dashboard: An interactive interface that displays the
collected information during the flight is implemented to
support risk management systems. Specifically, it shows
the geolocation of the UAV into a map using the Global
Positioning System (GPS) information. The trajectory of the
UAV is drawn in the map using a red line and markers show
the location of people and animals. The video that is being
captured from the Google Coral camera that is mounted on
the drone is displayed in the top left corner as shown in
Fig. 4.
Fig. 4. Dashboard based on MapViz to see the markers and the drone
The system integration proposed in this work can suc-
cessfully support first responders in disaster situation. The
utilization of a distributed system like ROS facilitates the
management and processing of the information displayed in
the dashboard. Moreover, since it is a distributed system
it can be scaled to use multiple UAVs and integrate the
information in a single dashboard.
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ın, S. H. Doerr, X. L. Otero, and C. J. Chafer, “Quantity,
composition and water contamination potential of ash produced under
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multithreaded open source robotics framework for deeply embedded
platforms,” in 2015 IEEE International Conference on Robotics and
Automation (ICRA), 2015, pp. 6235–6240.
[3] M. Quigley, K. Conley, B. Gerkey, J. Faust, T. Foote, J. Leibs,
R. Wheeler, and A. Y. Ng, “ROS: an open-source robot operating
system,” in ICRA workshop on open source software, vol. 3, no. 3.2.
Kobe, Japan, 2009.
[4] J. Redmon and A. Farhadi, “YOLOv3: An incremental improvement,
ResearchGate has not been able to resolve any citations for this publication.
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