Robotics skills are in high demand, but learning robotics can be difficult due to the wide range of required knowledge, increasingly complex and diverse platforms, and components requiring dedicated software. One way to mitigate such problems is by utilizing a standard framework such as Robot Operating System (ROS), which facilitates development through the reuse of opensource 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 Systems” (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.
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 materials2, videos3, and code4), in the hope that they might be useful for other educators. 2
One important factor which has been identified as facilitating learning of challenging
material is engagement [
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 students’ 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
pleasure or hope, or suggest how negative outcomes can be avoided, e.g., as in so-called
“fear appeals” [
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
Sweden. In accordance with the Bologna Process used in most European universities [
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 programming 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 body5 attached to a Ridgeback mobile base6, 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
discussed separately [
Content for the main part of the course supporting students’ abilities to learn, the lectures and labs, was mostly retained from the previous year, while adding some material 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, subscriber), 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
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 20167. 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 landslides, 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 vacuum 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 opportunities 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 trigger 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/ 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 electrical 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 computers on the robots; outside the project room was an area with tools such as 3D printers and soldering irons.
Robots. Inside the project room, students assembled and augmented some small differential drive robots from a commercially available kit using an Arduino Uno microcontroller. Sensors included an array of three line following sensors for following tracks, wheel encoders, an accelerometer, and mechanical bumpers; actuators included 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 camera, 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
capabilities. 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
processing. Students selected and set up power solutions using, for example, lithium
polymer batteries and voltage regulators. Each group was also free to develop
algorithms for detection; although in general groups started by finding colors and
contours, and moved on to a more robust approach of using rotation and scale invariant
log spiral markers [
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 possible to define a protocol for the students to use, but the choice was given to the students 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.
No. Channel 1 Heartbeat 2 3 4
Platoon Position Fan out Lane Change
Purpose Used by each of the robot to indicate its position in coordinates 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 from infrastructure such as an outside computer. Upon receiving 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 instructions to each robot, or when obstacles are detected.
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) [
This year, 24 students (average age = 26.8 years, SD = 4.7, 8 female, 16 male) participated. 4
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 evaluations 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 comparison with feedback from the previous year when ROS had not been incorporated, in the estimation that the general trends can be informative. 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 obstacles, and clearing debris. Fig. 5 shows some scenes from the final demonstration of students’ work with ROS, and some videos have been made available online8. 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 positive and negative experiences. Over half of the respondents described the project using 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
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 protocol 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 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 positive comments about their experiences. 4.4
In summary, we observed that incorporating ROS into an existing robotics course, guided by considering a behavior model, appeared to have allowed students to develop 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 in both academia and industry, as students bring their knowledge and engagement with them to new endeavors. 5
We would like to thank everyone who helped, including the students of the DEIS course! The authors received funding from the Swedish Knowledge Foundation (Sidus AIR no. 20140220 and CAISR 2010/0271).