Aerospace and Electronic Systems Magazine January 2018 - 50

Feature Article:

DOI. No. 10.1109/MAES.2018.160219

Software in the Loop Framework for the Performance
Assessment of a Navigation and Control System of an
Unmanned Aerial Vehicle
Ali Aminzadeh, Mohammadali Amiri Atashgah, University of Tehran, Iran
Alireza Roudbari, Shahid Sattari University of Aeronautical Engineering, Tehran, Iran

INTRODUCTION

RELATED WORK

The use of autonomous vehicles is currently a growing trend, particularly in applications such as manufacturing, hazardous material handling, and surveillance [1]. A quadcopter is an emerging
rotorcraft concept for an unmanned aerial vehicle (UAV) that is
equipped with four rotors, two pairs of counter-rotating, and fixedpitch blades located at the four corners of the vehicle [2]. In a
quadcopter system, yaw is controlled by increasing the angular velocity of rotors that spin in the same direction to a level higher than
the velocity of other rotor pairs, and pitch or roll is controlled by
increasing the angular velocity of a rotor to a level greater than the
velocity of a diametrically opposite rotor (Figure 1) [3].
The 1920s and 1930s saw the launch of a number of manned
quadcopters [4] that require high workload from pilots because of
poor stability augmentation and limited control authority in the
systems [5]. Compared with modern prototypes, early quadcopters
are overweight, thus imposing more load on rotors to produce the
required lift. Despite the advancements evident in modern designs,
however, expensive equipment and the probability of failure during flight tests present challenges in the operation of contemporary
quadcopters. These challenges underscore the importance of simulating flight on the basis of software in the loop (SITL) framework.
SITL can dynamically simulate quadcopter flight, the operation of
quadcopter sensors, and the implementation of protocols for communication with ground stations. In this work, ArduPilot Mega
(APM) firmware was used as the autopilot system of a simulated
quadcopter, and MAVProxy ground station software was adopted
to simulate a virtual environment, send commands to the autopilot
system during a mission, and receive flight data. The virtual simulation was conducted by loading Google maps.

Authors' address: Faculty of New Sciences and Technologies, University of Tehran, North Kargar Street, Tehran, Iran,
Post Code: 1439957131, zip code: 14395-1561M, E-mail:
(aliaminzadeh@ut.ac.ir and atashgah@ut.ac.ir).
Manuscript received October 15, 2016, revised February 8,
2017, and ready for publication April 19, 2017.
Review handled by M. Braasch.
0885/8985/17/$26.00 © 2018 IEEE
50

This section provides an overview of SITL-based simulations for
the performance assessment of a robot's navigation and control
system. Watanabe et al. [6] presented a vision-based system for simultaneous navigation and tracking of a moving ground target in
a Global Positioning System (GPS)-denied environment which is
first validated through a SITL-based simulation. The OpenRobots
simulator, which was built on the basis of Blender and Python, was
used to simulate a UAV flight test in a dynamic three-dimensional
(3D) environment and emulate onboard sensor measurements, such
as GPS, inertial navigation systems, and vision sensing. Eresen et
al. [7] implemented an optical flow-based obstacle avoidance algorithm on a quadcopter in a Google Earth virtual environment,
which was linked to Matlab/Simulink for the quadcopter to follow
the generated path. The successful simulation of autonomous flight,
without any collisions at four different destination points, indicates
the potential of the proposed method for the vision-based navigation of an autonomous flying robot in an urban environment. In [8],
a collision avoidance algorithm for UAVs was designed and implemented. Automatic Dependent Surveillance-Broadcast was used
to detect aircraft, and the tested UAV was required to fly through
predefined waypoints without colliding with other aircraft. The algorithm was verified through the SITL simulation, in which APM
firmware, MAVProxy software, and JSBSim were used as the autopilot system, ground station, and flight simulator, respectively. In
the simulation-based testing of a controller in [9], the environment
and platform of the system were simulated to systematically assess
the reliability and robustness of the designed controller in a virtual
environment. The motion of the quadcopter, which was equipped

Figure 1.

Schematic of yaw, pitch, and roll control of a quadcopter. Left. Yaw to
left. Middle. Pitch down. Right. Roll to left.

IEEE A&E SYSTEMS MAGAZINE

JANUARY 2018

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