=Paper=
{{Paper
|id=Vol-2329/paper-07
|storemode=property
|title=Teaching Robotics with Robot Operating System (ROS): A Behavior Model Perspective
|pdfUrl=https://ceur-ws.org/Vol-2329/paper-07.pdf
|volume=Vol-2329
|authors=Martin Cooney,Can Yang,Jennifer David,Abhilash Padi Siva,Sanjana Arunesh
|dblpUrl=https://dblp.org/rec/conf/erf/CooneyYDSA18
}}
==Teaching Robotics with Robot Operating System (ROS): A Behavior Model Perspective==
https://ceur-ws.org/Vol-2329/paper-07.pdf
Teaching Robotics with Robot Operating System (ROS):
A Behavior Model Perspective
Martin Cooney, Can Yang, Abhilash Padi Siva, Sanjana Arunesh, and Jennifer David
Halmstad University, PO Box 823, Kristian IV:s väg 3, 30118, Sweden
martin.daniel.cooney@gmail.com
Abstract. Robotics skills are in high demand, but learning robotics can be diffi-
cult due to the wide range of required knowledge, increasingly complex and di-
verse platforms, and components requiring dedicated software. One way to mit-
igate such problems is by utilizing a standard framework such as Robot Operat-
ing System (ROS), which facilitates development through the reuse of open-
source code—however this also raises a challenge, in that learning curves can
be steep for students who are first-time users. In the current paper, we suggest
the use of a behavior model to structure the learning of complex frameworks
like ROS in an engaging way. A practical example is provided, of integrating
ROS into a robotics course called the “Design of Embedded and Intelligent Sys-
tems” (DEIS), along with feedback suggesting that some students responded
positively to learning experiences enabled by our approach. Furthermore, some
course materials, videos, and code have been made available online, which we
hope might provide useful insights.
Keywords: Robotics Teaching, ROS, Behavior Model
1 Introduction: ROS-Based teaching of Robotics
The current paper reports on some of our recent experiences with teaching robotics at
the postgraduate university level through Robot Operating System (ROS), leveraging
a behavior model to encourage learning.
Robotics is a popular subject for which an explosion in applicability and demand is
occurring [1]; but, robotics can also be demanding to learn, in encompassing
knowledge in mechanics, electronics, statistics, arts, and software [2]. For software, to
avoid “reinventing the wheel”, robotics practitioners can use ROS, a standard frame-
work which offers support for typical robotics capabilities such as interprocess com-
munication and navigation 1. One challenge with frameworks such as ROS is usability
[3]: in particular steep learning curves facing first time users [4]. In conjunction with
the vast amount of material which must be typically covered in learning robotics, this
can dissuade teachers from incorporating such frameworks into their courses.
To facilitate the study of robotics using ROS, we suggest considering knowledge
from human science, specifically behavior models, as a way to effectively structure
1
http://www.ros.org/
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learning experiences. From this perspective, we report on some of our experiences,
positive and negative, in adding ROS to an existing robotics course, and provide some
reference materials online (some course materials 2, videos 3, and code 4), in the hope
that they might be useful for other educators.
2 Design: Structuring Course Content via Behavior Models
One important factor which has been identified as facilitating learning of challenging
material is engagement [5]. Educators have sought to increase engagement in various
ways, such as by seeking to foster active learning of meaningful topics and adequate
support systems which allow students to feel a sense of membership [6]. In the current
paper, we turned our attention to a behavior model proposed by Fogg, whose useful-
ness has been described for various applications (e.g., persuading people to use social
media) [7].
The Fogg Behavior Model highlights the importance of three aspects in facilitating
behaviors (referred to here as requirements R1-3): motivation, ability and triggers.
Students should feel motivated to learn (R1). Learning challenges should reflect stu-
dents’ abilities (R2). Furthermore, students require opportunities to engage in learning
(R3). To address these requirements we adopted an approach comprising three facets
(hereafter referred to as A1-3), as depicted in Fig. 1: demonstrations, classes, and
independent project work.
Demonstrations can show what positive possibilities exist, thereby eliciting pleas-
ure or hope, or suggest how negative outcomes can be avoided, e.g., as in so-called
“fear appeals” [8] (A1, addressing R1). Lectures and labs can be used to scaffold core
learning, by first dedicating sufficient time to considering simplified standardized
concepts and tasks, thereby promoting a perception of self-efficacy (A2, addressing
R2). Project work promotes autonomy, giving students a chance to make knowledge
their own by using it in practice and making decisions (A3, addressing R3).
Fig. 1. Applying the Fogg Behavior Model to identify some desired components of a robotics
course to facilitate incorporation and learning of complex tools such as robotics frameworks.
2
http://islab.hh.se/mediawiki/images/7/72/Deis_course_description_2017.zip
3
https://www.youtube.com/watch?v=B8nvrw7IjCI
4
https://github.com/martincooney/BaxterDemo/
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3 Implementation: The “Design of Embedded and Intelligent
Systems” (DEIS) course
Informed by the behavior model, our approach was taken into account in adding ROS
to an existing robotics course, “DEIS”, which is a half-year intensive (double-credit)
compulsory course targeting second year master’s students at our university in Swe-
den. In accordance with the Bologna Process used in most European universities [9], a
formal statement of examinable learning outcomes was made available. The core
learning outcome is that the students should be able to improve both the breadth and
depth of their conceptual and practical robotics knowledge in a collaborative, creative,
and critical manner.
3.1 A1: Demonstrations
We conducted some demonstrations seeking to engage students to learn, which had
not been performed in the previous year. Before the DEIS course started, students
took an introductory course to robotics which did not use ROS. On the last day of this
course, it was demonstrated how ROS can be used to avoid some of the challenges the
students had faced (e.g., difficulty interfacing programs written in different program-
ming languages).
Additionally, in the first three weeks at the start of the DEIS course, a robotic
“teaching assistant” was introduced to show students an example of what positive
things can be done with ROS, as shown in Fig. 2. This robot, composed of a Baxter
humanoid upper body 5 attached to a Ridgeback mobile base 6, demonstrated abilities
such as reading quizzes, speech and face recognition, and handing out materials.
Some code for this robot has been uploaded to the internet, and details will be dis-
cussed separately [10].
Fig. 2. A robot teaching assistant was used in the DEIS course to demonstrate some robotic
tasks which can be accomplished by using ROS, toward engaging students.
5
http://www.rethinkrobotics.com/
6
https://www.clearpathrobotics.com/
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3.2 A2: Lectures and Labs
Content for the main part of the course supporting students’ abilities to learn, the lec-
tures and labs, was mostly retained from the previous year, while adding some mate-
rial related to ROS. In total, eight teachers covered a wide range of topics, comprising
statistical inference, sensors and actuators, sensor fusion, embedded programming,
motion planning, simulations, communication, and image processing. In one of the
first lectures, we described some of the merits and demerits of using ROS (e.g., the
large community and useful tools, vs. the complexity and official support only for
Ubuntu); we also defined some typical concepts (e.g., node, package, publisher, sub-
scriber), listed some commands and tools (e.g., catkin_make, rostopic list, roscore;
MoveIt!, Gazebo), and provided some “Hello world” examples in C++ and Python. In
a follow-up lab, the students were asked to create catkin workspaces and use the ROS
talker/listener tutorial to communicate between robots. Time spent for A2 was similar
to that for A1. Concepts and tasks were kept simple to allow the students to perceive
high ability.
3.3 A3: Project
Basic Concept. Students were also given a problem-solving project to work on in
small groups, about platooning robots, which formed the core opportunity for learning
robotics with ROS. The project topic, like in the previous year, was generally inspired
by the Grand Cooperative Driving Challenge (GCDC), an international contest held
between university teams, in which our university placed first in 2016 7. Furthermore
we focused on a scenario of cleaning, which we felt would have practical uses: For
example, platoons of snow machines or snow plows are used at some ski resorts and
on roads to remove snow. After disasters such as earthquakes, fires, floods, or land-
slides, teams cooperate to remove debris. Multiple lawn mower robots could remove
grass from large open areas such as golf courses or the sides of highways, and vacu-
um cleaner robot teams could clean large venues such as sports arenas or hotels. Thus,
we felt that such a scenario would offer various challenges and engaging opportuni-
ties to trigger learning. The main difference with the previous year, aside from the
cleaning scenario, was the incorporation of ROS as a required component for the
robots and infrastructure. We estimate that, although times were not recorded, the
students spent more time on A3 than on A1 and A2, due to the importance of the trig-
ger facet in allowing opportunities to make knowledge their own.
Learning environment. To implement platooning robots, the students worked in five
groups, with one robot per group, in a 7.2 x 10.8m project room (80m2 area) with a set
up shown in Fig. 3. The robots ran on top of a 2.5 x 3.7 x 0.8m table with 0.3m walls
in the middle of the room. The table was intended to be easy for students to work with
robots without having to bend down, to keep robots’ wheels from becoming dirty,
andto stand at a desired distance (2.5m) from the ceiling to ensure that an overhead
7
http://www.gcdc.net/en/
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Fig. 3. Project set-up.
network camera (with 3 megapixel resolution at 20 fps, day and night) could capture
the entire surface of the table. Concentric elliptical rings were added with black elec-
trical tape to the table-top by the class (students and teachers), as tracks for the robots
to run on. The room was well-lit with nine windows, and also featured 13 computers,
with monitors equipped with HDMI cables to be able to also work with small com-
puters on the robots; outside the project room was an area with tools such as 3D print-
ers and soldering irons.
Robots. Inside the project room, students assembled and augmented some small dif-
ferential drive robots from a commercially available kit using an Arduino Uno micro-
controller. Sensors included an array of three line following sensors for following
tracks, wheel encoders, an accelerometer, and mechanical bumpers; actuators includ-
ed two 140 rpm gearmotors attached to 65mm rubber wheels, and a buzzer. After
assembly, students added some additional components: a small single-board computer
with in-built WiFi (Raspberry Pi 3, hereafter RPi, running a Linux operating system,
Raspbian Jessie), and an 8-megapixel camera supporting 1080p30. An overhead cam-
era, in conjunction with markers attached to the tops of robots, was also used to detect
robot locations and identities.
Thus, the restriction on students was the general project theme and infrastructure,
comprising a base platform using a RPi and Arduino and overhead camera. Students
were also encouraged to be creative in designing their systems’ appearances and ca-
pabilities. For example, the students freely selected extra components such as infrared
range sensors, sonars, and servo motors to add creative features, and fashioned 3D
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printed connectors and shovels for their robots. They could use school desktops, their
own computers, or rely entirely on microcomputers and microcontrollers for pro-
cessing. Students selected and set up power solutions using, for example, lithium
polymer batteries and voltage regulators. Each group was also free to develop algo-
rithms for detection; although in general groups started by finding colors and con-
tours, and moved on to a more robust approach of using rotation and scale invariant
log spiral markers [11]. During the course, students’ choices, and their progress, were
continuously monitored via a series of “tollgates”, comprising reports on system de-
sign choices, presentations by individuals about topics of personal interest (the “re-
search step”), and demonstrations of robot behavior such as lane changing.
ROS. To communicate between robots and with the overhead camera, ROS was used.
All robots in the course used ROS Indigo; RPis had the minimal ROS-Comm variant
of ROS installed which features basic communication libraries but not GUI tools.
Additionally, a server program was also set up for the students on a “GPS” server PC
to stream images from the overhead camera; images were used to estimate the x and y
coordinate positions of robots, like how automotive navigation systems can use the
real Global Positioning System to estimate their positions (in other words, real GPS
was not used, but we referred to the system as “GPS” due to its analogous function in
allowing localization for navigation).
Within groups, one member was also designated to be a representative who would
be responsible for communication and meet with the other groups to decide on a
shared protocol. Teachers did not interfere in this process. It would have been possi-
ble to define a protocol for the students to use, but the choice was given to the stu-
dents as a chance to foster creative thinking via problem-solving. This resulted in a
set-up with four channels as shown in Table 1, allowing behaviors like in Fig. 4.
Table 1. Communication channels.
No. Channel Purpose
1 Heartbeat Used by each of the robot to indicate its position in coordinates
2 Platoon Position Used to indicate relative order (the platoon leader was -1)
Used to move between a single file and diagonal formation (for
traveling or cleaning respectively). Messages could also be sent
3 Fan out from infrastructure such as an outside computer. Upon receiv-
ing a command, the leader sent commands to its followers in
the platoon to change lanes.
Used when a command is sent to the “fan out channel” to send
4 Lane Change
instructions to each robot, or when obstacles are detected.
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Fig. 4. Platooning examples: (a) straight line formation for traveling, (b) diagonal formation for
cleaning, (c) changing lanes
The first two channels support messages which are in the style of Cooperative
Awareness Messages (CAM), and the latter two, in describing traffic events, resemble
Decentralized Environmental Notification Messages (DENM) [12]. Thus, ROS was
used to enable some platooning behaviors in the project robots.
3.4 Examination
Overall, as in the previous year, students were graded 50% based on conceptual
knowledge and 50% based on practical knowledge, through an oral exam and written
report respectively. Criteria were as follows:
● Grade U (Fail): Basic requirements not met
● Grade 3: The student demonstrated collaboration, to apply basic concepts
● Grade 4: + Creativity
● Grade 5: + Critical thinking/excellent methodology
This year, 24 students (average age = 26.8 years, SD = 4.7, 8 female, 16 male) par-
ticipated.
4 Experiences and Discussion
We gained some feedback on our course using ROS, in the final demonstration of the
course project, through an anonymous survey conducted by our school, and by asking
students. We note that in teaching it is generally difficult to conduct rigorous evalua-
tions controlling only a single facet such as the usage of ROS, as there are typically
many lessons learned throughout a course and many changes made toward allowing
for the best possible learning experiences. Nonetheless we also present some compar-
ison with feedback from the previous year when ROS had not been incorporated, in
the estimation that the general trends can be informative.
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4.1 Final Demonstration
In this year’s course with ROS, students were able to develop additional functionality
when compared to the previous year: moving in diagonal formation, avoiding obsta-
cles, and clearing debris. Fig. 5 shows some scenes from the final demonstration of
students’ work with ROS, and some videos have been made available online 8.
4.2 Survey
Additionally, a survey was conducted by our university, obtaining feedback from 9
participants in regard to the questionnaire items below (using a six point scale, where
0 meant strongly disagree, and 5 meant strongly agree):
• The design of the course (teaching and examinations, etc.) has enabled me to
attain the learning outcomes of the course.
• The content of the course (required reading, lectures, etc.) has enabled me to
attain the learning outcomes of the course.
• Through the course, I was able to take part in research relevant for the field.
• Through the course, I developed my ability for critical thinking.
• The course encouraged me to actively search for and acquire new
knowledge/abilities/skills within the field.
The average result was 4.0 (80%), which was an improvement from the previous
year’s score when ROS was not used, 3.5 (70%). Students also described some posi-
tive and negative experiences. Over half of the respondents described the project us-
ing ROS as the most worthwhile element in the course, with one mentioning the robot
teaching assistant; this represented an increase from the previous year in which only
two students mentioned the project. In terms of improvement, students suggested the
course design could be structured to allow further freedom to select topics of interest,
and that platooning had been difficult because it was hard to find times to share robots
with other groups, among other comments (e.g., that the course room could be larger
and that better hardware could be helpful).
4.3 Additional feedback
The survey yielded some useful information but did not specifically relate to ROS;
therefore, the students were also asked for feedback about any problems they had
experienced with ROS. Familiarization with basic concepts and installations were
described as time-consuming, such as installation of the cv_bridge package on the
RPis, or various versions of OpenCV. Delays were also reported as a problem. One
example referred to synchronization with Matlab for image processing while using
many nodes. Another example reported not knowing how to select a preferred proto-
col for messages: e.g., assuming latency could be more important than reliability for
heartbeat messages, UDPROS could have been used instead of TCPROS. As well,
8
https://www.youtube.com/watch?v=B8nvrw7IjCI
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Fig. 5. Results of ROS-based project work: (a) Platooning in a straight line formation to reach a
destination, (b) Diagonal formation to clean, (c) Swerving out of the way of a detected obstacle
(here a “tree”), and (d) a final task in which robots had to deal with some artificial “snow”
(movements were visualized by attaching a paint brush to the backs of the robots).
some tasks like line-following did not involve ROS, so some students, especially the
communication “representatives” in each group, received more time to work with
ROS than others. Despite such considerations, a number of students also voiced posi-
tive comments about their experiences.
4.4 Conclusions
In summary, we observed that incorporating ROS into an existing robotics course,
guided by considering a behavior model, appeared to have allowed students to devel-
op more capabilities with their robots, and feedback from students was more positive
than in the previous year. As a result, we have decided to continue to use ROS in our
course. Next year, we will consider how to further incorporate ROS functionalities,
such as rosserial for the robots’ microcontrollers or bag files for sensor data, and also
to allow more freedom for students to explore topics on their own. We also plan to
take into account lessons learned in the current year; for example, each group will
receive two robots instead of one, which will let more students get hands-on with
ROS, toward achieving more effective learning.
We expect that effective learning of ROS in university courses, also leveraging
behavior models and knowledge of how to engage students, will contribute to robotics
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in both academia and industry, as students bring their knowledge and engagement
with them to new endeavors.
5 Acknowledgements
We would like to thank everyone who helped, including the students of the DEIS
course! The authors received funding from the Swedish Knowledge Foundation (Si-
dus AIR no. 20140220 and CAISR 2010/0271).
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