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Open-Source Robotics: Investigation on Existing
Platforms and Their Application in Education
Eleni Vrochidou, Michail Manios, George Papakostas
Human-Machines Interaction (HUMAIN) Laboratory
Department of Computer and Informatics Engineering
Eastern Macedonia and Thrace Institute of Technology
(EMaTTech)
Kavala, Greece
{evrochid, m.manios, gpapak}@teiemt.gr
Charalabos Aitsidis, Fotis Panagiotopoulos
Department of Business Administration
Eastern Macedonia and Thrace Institute of Technology
(EMaTTech)
Kavala, Greece
{aitsidis, fpanag}@teiemt.gr
Abstract—In recent years a continuous effort to foster robot-
ics at the earliest stages of education is reported. Robots’ code
and hardware are subjected to licensing, a fact that places manu-
facturers on the top of the beneficiaries of this new trend. Hence,
open-source robotics (OSR) enter the mainstream, to enable
rapid development on a lower budget. This paper provides an
overview of the up-to-date commercial OSR platforms that can
support education, in terms of hardware, software and simula-
tors. Extensibility and applicability are investigated, and compar-
ison of features takes place. Future challenges and possibilities
are also discussed.
Keywords—open-source robotics; hardware; software;
simulator; education
I. INTRODUCTION
During the last decade, the robot industry had entered all
fields of every-day life, including education [1]. It has been
proven that hands-on education can support the learning of new
material since it provides a real-world meaning to the taught
abstract knowledge [2], [3]. Robotics have demonstrated their
efficiency as powerful tools for hands-on learning, for both
robotic [4] and non-robotic subjects [5]. In an educational set-
ting, however, these tools must also be inexpensive. If robotics
are becoming essential to schools, more tools and platforms are
needed to reduce the costs in time and money of prototyping
robots. The open-source paradigm offers a potential solution to
this end.
Open-source platforms phenomenon, intensively present
only recently, has a significant impact in software and hard-
ware industries. The collaborative model associated with open-
source communities, has introduced a new evolving model that
grows and expands outside regional borders [6]. The existence
of freely available software and hardware, robust and accessi-
ble for all, allows exploration of fabrication and programming
of various robotic morphologies. Thus, faster adaptation of
technology, increased innovation and reduced costs in terms of
money and time-to-market [7]. Moreover, the fact that open-
source products lack of license fees, contributes to shifting the
traditional license-based models towards service-based models
[8].
It is therefore vital to meet the challenges related to open-
source platforms. This work provides a road map regarding the
most popular open-source platforms, in terms of hardware,
software and simulators. The aim of this work is not only to
present, but to evaluate and synthesize the up-to-date empirical
research results on open-source platforms in education and
offer possible directions for future research.
The layout of the paper is as follows: Section II provides an
overview on the most popular open-source platforms, including
advantages and disadvantages. Section III presents recent ap-
plications of OSR in education. Discussion and future chal-
lenges are provided in Section IV. Conclusions are summarized
in Section V.
II. OPEN-SOURCE ROBOTIC PLATFORMS
This work does not mean to be exhaustive, OSR platforms
are numerous. Five of each category (hardware, software,
simulators) have been selected and presented. The selection is
based on the most recent reports of the bibliography regarding
educational applications that utilize these specific platforms.
A. Hardware
OSR hardware (OSRH) consists of physical technological
pieces designed and offered by the open design movement.
Hardware design refers to mechanical drawings, blueprints,
schematics, 3D model files, integrated circuit layouts, printed
circuit board data etc., that are distributed under free terms.
1) Sparki: A ready-made robotic construction with a
number of advanced sensors and electronic parts integrated on
an Arduino platform. Sparki can be programmed with C++ or
with block-based programming. It is designed to be an
affordable introductory robot for students from elementary-
age to adults. Sparki comes with a distance sensor,
accelerometer, infrared communications, compass, light
sensors, line-follower etc. It is used at over 2000 top schools
and STEM (Science, Technology, Engineering and
Mathematics) programs [9].
2) ArduPilot: An unmanned vehicle Autopilot Suite,
capable of controlling autonomous: multirotor drones, fixed-
wing and VTOL aircrafts, helicopters, ground rovers, boats,
submarines and antenna trackers, that run on an embedded
hardware consisting of microcontroller/s connected to
peripheral sensors for navigation. Sensors include MEMS
gyroscope, accelerometer, compass, laser or sonar altimeter,
GPS, optical flow sensors, airspeed indicators, monocular,
stereoscopic or RGB-D cameras etc. its flexibility makes it
popular in the DIY field and has been integrated into many
airplanes such as the Bixler 2.0 [10].
3) TurtleBot: TurtleBot is an off-the-shelf differential
drive robot equipped with a Microsoft Kinect sensor. It is
based on the iRobot Create platform, with an Asus controller
and an Intel Dual Core Processor. Odometry can be improved
by using a single axis gyroscope, a laser range finder, a
Microsoft KinectTM camera etc., available with the robot.
Turtlebot was developed to meet the cost-concious needs of
schools, laboratories and companies [11].
4) E-puck mobile robot: A wheeled mobile robot,
equipped with a color camera, infrared proximity, 3D
accelerometer, microphone, loudspeaker etc. Sine it is an OSR
hardware, its price is lower than competitors, thus, it is
adopted by the scientific community despite its original
educational orientation [12].
5) iCub: A humanoid robot for research into human
cognition ad artificial intelligence. It is controlled by an an-
board PC104 controller which communicates with actuators
and sensors. It is programmed in C++ and uses YARP for
external communication via Gigabit Ethernet with off-board
software. Power and network connection are provided with an
umbilical cable. It is able to crawl on all fours and sit up to
manipulate objects and its hands have been designed to
support sophisticate manipulation skills [13].
B. Software
A robot is not only plastic or metallic parts. In order to func-
tion, the robot needs intelligence. OSR software (OSRS) ap-
plies the needed intelligence to the robots, in terms of code,
that anyone can inspect, redistribute, modify and enhance. A
global network of programmers can improve OSR software by
adding features to it or fixing parts that do not work correctly.
In that way, the software goes through a type of natural evolu-
tion, which results to rapid development, increased reliability
and decreased cost. OSR software can provide free or low-cost
technology on the classroom that otherwise would be unable
to be afforded.
1) leJOS: Lego Java Operation System, is a replacement
for the standard Lego Firmware, adapts the TinhVM Java
Virtual Machine from the original Mindstorms RCX brick to
the new more powerful NXT brick. It has support for
threading, arrays, recursion, synchronization, exceptions, non-
generic data structures, standard data types, and input and
output. For input and output, streams and sockets are
available. For control purposes, is supports direct connection
via Bluetooth-enabled GPS units for spatial location
information and keyboards for the navigation control of the
robot [14].
2) RoCK: The Robotic Construction Kit is a sorfware
framework for robotics, based on the Orocos Real Time
Toolkit. It is designed to be extensible and includes a number
of drivers and modules for existing applications. RoCK
provides all required tools to set up and run high performance
and reliable robotic systems for a variety of applications in
research and industry [15].
3) ROS: The Robot Operating System, is a collection of
libraries, drivers and tools for effective development and
building of robot systems. It has a Linux-like command tool,
interprocess communication system and various application-
related packages. The ROS-based software is language and
platform-independent. It is implemented in C++, Python, and
LISP. It has experimental libraries in Java and Lua. The ROS
packages include many sensor drivers, navigation tools,
environment mapping, path planning, interprocess
communication visualization tool, a 3D environment
visualization tool etc., that allow effective development of
new robotic systems [16].
4) URBI: The Universal Robotic Body Interface is used as
the main tool for handling various software modules. It
integrates and delivers communications between the two
lowest levels of the architecture. This permits dynamic loading
of modules and total control of their operation. It also delivers
urbiscript, a script programming language used in robotics,
oriented towards parallel and event-based programming.
Urbiscript syntax is based on widely-used programming
languages, and it is integrated with C++ and other languages
such as Java, MATLAB or Python. The orchestration
mechanism that is built into URBI, can handle scheduling and
parallelization of tasks. Thus, all activities of the robot can be
synchronized with each other [17].
5) MRPT: The Mobile Robot Programming Toolkit is a set
of cross platform C++ library and applications released under
the GPL license, that can allow multiple third-party libraries to
work together. It is cross platform, but only currently has been
tested on Windows and Linux. MRPT focuses on
Simultaneous Localization and Mapping (SLAM), computer
vision, and motion planning algorithms [18].
C. Simulators
Robots are meant to operate in a real-world environment,
and therefore are subjected to physical constraints such as
weather, gravity, terrain etc. Thus, the robot’s functionality
needs to be tested before starting to assemble the hardware.
OSR Simulators (OSRSim) play an important role in robotic
applications as tools for testing the efficiency, safety, and
robustness of new algorithms. This is crucial in realistic sce-
narios that require robots to interact closely with humans, e.g.,
in special and typical education or in medical/assistive robot-
ics.
1) Gazebo: An apache-licensed simulation solution, with
advanced 3D graphics for indoor and outdoor robots, virtual
sensors, extensive command line tool collection and the ability
to run simulations on cloud. It has a Client/Server architecture
and a topic-based Publish/Subscribe model of interprocess
communication. Gazebo has a standard Player interface and
additionally a native interface. The Gazebo clients can access
its data through a shared memory. In the process of dynamic
simulation Gazebo can access multiple high-performance
physics engines including Open Dynamics Engine (ODE),
Bullet, Simbody and Dynamic Animation and Robotics
Toolkit (DART). Utilizing Object-Oriented Graphics
Rendering Engine (OGRE), it provides realistic rendering of
environments. It can generate sensor data, from laser range
finders, 2D/3D cameras, Kinect style sensors, contact sensors,
force torque etc. It supports many plugins and many robot
models. It can run simulation on remote servers and it uses
CloudSim to run on Amazon AWS and GzWeb. Its extensive
command line tools, facilitate the simulation introspection and
control [19].
2) MORSE: The Modular OpenRobots Simulation Engine,
is a BSD-licensed project that focuses on generic simulation
for academic robotics, on realistic 3D simulations of indoor or
outdoor environments. It can be entirely controlled from the
command-line, it can be programmed with Python and renders
using the Blender game engine. The OpenGL-based Game
Engine supports shaders, lighting options and multi-texturing.
It comes with a set of standard sensors such as cameras, laser
scanners, GPS, odometry etc., actuators such as speed
controllers, generic joint controllers etc., and robotic bases
such as quadrotors, Pioneer3DX, ATRV etc. [20].
3) V-REP: The Virtual Robot Experimentation Platform is
a robot simulator with integrated development environment,
based on a distributed control architecture. Each model can be
controlled via an embedded script, a plugin, a ROS or
BlueZero node, a remote API client, or a custom solution. This
makes V-REP versatile and ideal for multi-robot applications.
Controllers can be programmed in C/C++, Python, Java, Lua,
Matlab or Octave. It is used for fast algorithm development,
factory automation simulations, fast prototyping and
verification, robotics related education etc. [21].
4) OpenRAVE: Open Robotics Automation Virtual
Environment, provides an environment for testing, developing
and deploying motion planning algorithms in real-world
robotics applications. It focuses on simulation and analysis of
kinematic and geometry information, necessary for motion
planning. It is easily integrated into existing robotic systems,
and provides many command-line tools [22].
5) USARSim: Urban Search And Rescue Simulation is a
3D high-fidelity simulator based on the Unreal Tournament
game engine. It was initially created as a research tool, to aim
urban search and rescue simulation. Currently it used for
research and education and can be extended to model arbitrary
application scenarios. A variety of sensors are supported such
as touch sensors, sound sensors, cameras, and lasers. It is
highly configurable and extendible. Users can add new sensors
or model new robots. It supports Windows, Linux and MacOS
[23].
III. EVALUATION OF OPEN-SOURCE ROBOTICS IN
EDUCATION
In this section, the selected OSR are evaluated according to
the most recent results, derived from their use, in educational
applications. Tables I, II and III summarize the applications of
the bibliography where the selected OSR Hardware, software
and simulators, respectively, have been used, the year of the
application and the study details.
TABLE I. APPLICATION OF THE SELECTED OSRH IN EDUCATION
OSRH
(Study Year)
Study details
Ref.
Sparki (2017)
An informal team of adults, a teen and a toddler were
engaged in understanding the functionality of Sparki
robot on a layout of a city model.
[24]
ArduPilot
(2017)
A competition between final year robotic projects of
students, revealed that competitions can play a major
role in foundational skills like literacy and numeracy,
in soft skills like teamwork, people and time man-
agement, creativity, and in technical skills.
[25]
TurtleBot
(2016)
A cloud-based implementation of GMapping for
TurtleBot, to supply odometry details, so as to build a
map for a fast-moving robot.
[26]
E-puck
(2009)
Undergraduate students used e-puck to understand
and practice embedded programming. Results re-
vealed the efficiency of the robot to illustrate the
concepts of the course.
[27]
iCub (2016)
56 adult participants took part in this study, to reveal
that the robot acceptance is complex dynamic, prevail-
ing distrust on robots. Even when the robot was trust-
ed on social issues, there was a significant distrust on
functional issues.
[28]
TABLE II. APPLICATION OF THE SELECTED OSRS IN EDUCATION
OSRS
(Study Year)
Study details
Ref.
leJOS (2017)
A robotic challenge was organized to allow students
integrate the obtained knowledge of previous attended
modules and practically apply knowledge and skills to
solve a real problem.
[29]
RoCK (2016)
Students of an MSc course were subjected to a set of
laboratory exercises with a gradual increase of diffi-
culty to expose how students that had successfully
conducted the course got involved in European Robot-
ic Challenges.
[30]
ROS (2016)
An embedded robotic platform used for professional
training in Electrical Engineering. ROS was used as
communication and control software, to prove a better
appropriation of theoretical concepts, increased stu-
dents’ enthusiasm, improved ease of communication
and teamwork and greater interest in participation in
research activities.
[31]
URBI (2015)
The OSR platform URBI was used to control a robotic
companion. Experiments demonstrated
that both
children and adults feel comfortable interacting with
the robot and can easily recognize the emotions he
expresses.
[32]
MRPT
(2012)
A team of students won the MAGIC2010 competition
with an autonomous robot that could do path plan-
ning, obstacle recognition and navigation.
[33]
TABLE III. APPLICATION OF THE SELECTED OSRSIM IN EDUCATION
OSRSim
(Study Year)
Study details
Ref.
Gazebo
(2016)
A graduate course project on humanoid robotics. The
target was for the students to map human limbs into
robotic joints, guarantee the stability of the robot and
teleoperate the robot to perform the correct move-
ment.
[34]
MORSE
(2017)
An OSR framework was used for two years in an
undergraduate course of robotics. The goal was path
planning and navigation in a simulated but realistic
environment.
[35]
V-REP
(2016)
Students of a Master program used an OSR platform
for hands-on laboratory sessions with mobile robots,
to contribute to engineering studies.
[36]
OpenRAVE
(2012)
A GUI was developed for users to take part in an
educational workshop regarding control algorithms of
modular robots with the help of a framework.
[37]
USARSim
(2007)
A robot simulator for the Robocup competition com-
munity, promises to be an effective tool for teaching,
due to the high accuracy rendering and physical
simulation.
[38]
IV. DISCUSSION AND FUTUTRE CHALLENGES
Robot industry might be half a century old, yet, it is still
considered as a relatively closed industry. This means that
every manufacturer has its own software, algorithms, data
structures and even programming languages. OSR is a slowly
emerging open-source community. There are many reasons for
the lack of openness in the robotics industry; 1) All manufac-
turers operate in niche markets rather in open-source communi-
ties, 2) the biggest share of the market is covered by users that
make long-term investments, thus, they demand high reliability
and do not trust open-source products, 3) the academic users
who are really interested in the development of robotics and
share the vision of the open-source community, represent only
a negligible market share. As a result, OSRH industry is con-
sidered to be relatively locked-in [39]. On the other hand,
OSRS industry is emerging and ambitious. OSRS and
OSRSim, seem to evolve in the last couple of years, due to the
appearance and maturity of the Linux operating system togeth-
er with the growing employment of the Internet and its open
communication standards (TCP/IP), that allow the easy, cheap,
instantaneous and worldwide exchange of software and data.
The main advantages of OSR are 1) the long term-
availability, 2) the avoidance of locking-in, 3) the interchange-
able software and hardware with common interfaces, 3) the
ability to modify designs 4) the scientific reproducibility and 5)
the lower cost. Existing drawbacks of OSR that should be men-
tioned are 1) the reduction of profits for commercial manufac-
turers and organizations and 2) the association of open-source
with amateurism. To overcome this perception, companies that
use OSR business models, need to work harder to emphasize
their professionalism.
The objective of OSR is to bridge the gap between the re-
search and hobbyist robotics communities. The design goals
for OSR is for the system to 1) be easily assembled from com-
monly-available off-the-shelf parts, by someone with no formal
training, 2) to be affordable 3) be open and accessible, allowing
users to modify it as they please.
The open-source community promises to bring research-
level robots into the undergraduate and high school classroom.
Amateurs in robotics and non-specialists usually face some
challenges. These challenges are 1) to building their first work-
ing robot system, 2) to obtaining relevant information about the
open problems and existing solutions and 3) to find a commu-
nity to demonstrate and compare their developments. OSR
platforms, allows participants with different levels and types of
expertise to share their capabilities and introduce them to a
wider audience. There are many potentials for the design and
control of robots that have been only superficially explored.
Many robotic competitions for students exist, to motivate and
consequently provide the tools and arena with which student,
could make novel contributions. The main objective is to allow
all who can contribute to this vital field of robotics, educators,
researchers, manufacturers and more, to work together to ad-
vance the current state of robotics and beyond.
V. CONCLUSION
This work presents a review of OSR platforms in term of
software, hardware and simulators. Its scope is to analyze and
evaluate the effectiveness of OSR in education, according to
their most recently reported applications. Advantages and
drawbacks of OSR are discussed. The aim of the paper is to
provide comprehensive knowledge of the available OSR plat-
forms, to enlighten amateurs and researchers regarding OSR’s
usability and the possibilities they promise.
It should be acknowledged that the OSR platforms present-
ed in this work are selected by the authors based on the most
recent reports in educational applications, according to the
bibliography. Different criteria would have yield different OSR
platforms and reference articles.
ACKNOWLEDGMENT
This project has received funding from the European Un-
ion’s Horizon 2020 research and innovation programme under
the Marie Skłodowska-Curie grant agreement No 777720.
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