CREATING AN UPPER-LEVEL UNDERGRADUATE ROBOTICS ELECTIVE
IN COMPUTER SCIENCE
Kevin Workman and Stephanie Elzer
Millersville University
{kaworkman,elzer }@cs.millersville.edu
Abstract
Robotics as an upper-level undergraduate computer
science class has a lot of potential, since it allows students
to apply their skills in a new and novel domain, and since
many students find it motivating to obtain the immediate,
concrete feedback of seeing their program control a robot.
Because of both cost concerns and the practical
constraints of the available robotic platforms, the
traditional approach to teaching a robotics class has been
to select a single platform and write code using an API
specific to that platform. However, as the price of various
robotic platforms decreases, and the selection increases, it
is becoming feasible for universities to purchase a variety
of robots. Microsoft Robotics Studio provides a useful
way to utilize multiple robotic platforms in one class
without wasting time on learning multiple platformspecific APIs. This paper documents our experience
utilizing Microsoft Robotics Studio, along with Legos
NXTs and iRobot Creates, in an upper-level
undergraduate robotics elective.
way for students to apply programming techniques they
learned in previous classes. An upper level robotics
course provides a valuable combination of applying
students’ knowledge and exposing them to an exciting
emerging technology.
The Traditional Approach to Teaching
Robotics
The robotics industry includes many diverse robotics
platforms with a wide assortment of functions, domains,
and abilities. This diversity actually complicates teaching
a robotics course. The robotics field is full of options, but
an undergraduate class does not have the time or
resources to cover them all. Instead, most modern robotic
classes concentrate on one platform, and students in the
class work with only one robot throughout the entire
class. This approach eliminates the time spent learning
multiple platform-specific APIs but greatly limits the
ability to introduce different robotic ideas and paradigms
to the class.
Introduction
Microsoft Robotics Studio
In January 2007, Microsoft released Robotics Studio, a
developing environment geared towards the control of
various robotics platforms. Bill Gates stated in his article
“A Robot in Every Home” that robotics will impact the
next 30 years in the same way that personal computing
impacted the last 30 years. [1] One of the jobs of an
undergraduate computer science department is to
introduce its students to up and coming technologies.
During the fall semester of 2007, Millersville University
offered robotics as an upper level computer science
elective for the first time in ten years. Because the course
had experienced such a long hiatus from the schedule, it
was completely revamped, from the robotics platforms
being utilized to the software used to control those robots.
After evaluating the developments that had occurred in
robotics education over the last decade, the decision was
made to utilize the newly available Microsoft Robotics
Studio, along with Lego NXTs, and iRobot Creates to
complete various goals in a lab environment. Not only did
the robotics course offer undergraduate students the
opportunity to gain exposure to the potential within the
robotics field, but Microsoft Robotics Studio provided a
In developing the course, we chose to utilize Microsoft
Robotics Studio because of its ability to control multiple
robotic platforms using the same interface. By exposing
the students to different kinds of robots, the class more
effectively introduced theories and examples that would
have otherwise been difficult to illustrate. Eliminating the
need to learn a new syntax for each platform allowed the
students to instead focus on the more important
underlying semantics of robotics design and
implementation. It also provided a concrete example of
the benefits of abstraction and object-oriented design of
software.
To briefly describe Microsoft Robotics Studio, some
terms must be defined. Almost every action is performed
by a service, which controls a specific software or
hardware entity. The services interact with each other
much like threads. The Concurrency and Coordination
Runtime, or CCR, oversees the execution of each service
and the interaction between them. The Decentralized
Software Service, or DSS, organizes, creates, and
manipulates the services. The DSS also handles higher-
level creation of projects, which conveniently creates a
shell of a new service that a user can use, append, and link
to other services to produce complex functionality. A
manifest file provides the link between the software and
the hardware and can easily be replaced to link to
different hardware platforms. [3] For example, a user can
program commands for a basic drive behavior, then
replace the manifest file in the project with any manifest
file from a robot with a drive system. The service
automatically converts the higher level commands to low
level instructions exclusive to each robot. This is a main
selling point of Robotics Studio: the ability to quickly
switch which robot the code controls without changing
the code itself.
In addition to these features, Robotics Studio also
includes a powerful simulation runtime. In the simulation
environment, users can recreate any number of
complicated scenes including classrooms, labs, mazes,
real world environments, etc. Then any robot compatible
with Robotics Studio can be placed in the scene and
interact with the environment. A physics engine
accurately handles all collision, gravity, and other realistic
force calculations so objects react in real time the way
they would in the outside world. Users can even create
new robots and items based on components of preexisting
models. The simulation environment makes it possible to
introduce students to robotic platforms with prohibitively
high cost or size and environments too complicated or
dangerous to realistically model in real life.
Our Approach
We approached robotics with a focus on artificial
intelligence techniques and intelligent robots. Basing the
lecture material on An Introduction to AI Robotics by
Robin Murphy, we focused on the various robotic
paradigms
(hierarchical,
reactive
and
hybrid
deliberative/reactive) and the design process involved in
building intelligent robots [2]. In the class, sound software
design shared the same importance as logical hardware
choice. Examples of real life robots actually used in the
field supplemented theory, and the lab assignments gave
the students a chance to apply the concepts which they
had learned.
Robotics Studio users can program in any .NET language,
and the class used C# because of the students’ familiarity
with C-based languages. By choosing a language that
resembled other languages the students have used, the
students’ learning curve was greatly reduced. After using
some simple instructions to send cursory commands to the
robots, the students quickly delved into the more powerful
options offered by C# that they might have not seen in
other classes.
Laboratory Exercises
The laboratory section of the class consisted of six
assignments, and the students had several weeks to
complete each before starting the next one. Students
worked in pairs of their choosing because of the limited
number of robots available. Each group shared one Lego
NXT robot and one X10 camera, and the class shared
three Lego extension kits, two iRobot Creates, and one
Pioneer P3-DX. The laboratory assignments paired
closely with lessons taught in lecture, and allowed the
students to apply their knowledge in a very concrete
manner. They could actually see the fruits of their labors,
which turned out to be extremely rewarding at times and
extremely frustrating at others.
Lab 1: Introduction to Microsoft Robotics
Studio and Lego NXT
The first assignment instructed the students to work
through the first three basic tutorials for Microsoft
Robotics Studio, available from the Microsoft Developers
Network website. The first tutorial introduces the students
to the idea of services, a main component of Robotics
Studio. A service is an object oriented entity that controls
a specific piece of hardware or software, and by
combining multiple services, a user can program complex
behaviors. The second tutorial focuses on how to combine
services. At the end of the second tutorial, students could
switch on and off a motor output through a push button
input. With this knowledge and a bit of creativity, they
could easily add another button, and increase or decrease
the motor speed depending on which button was pressed.
The third tutorial adds more functionality to this. Using
the same types of services from tutorial two, tutorial three
builds a simple robot with two wheels and two bumpers.
The robot travels forward until a bumper comes into
contact with an obstacle, at which point the robot rotates a
certain number of degrees depending upon which bumper
was pressed and repeats the process. An important note is
that even at this early stage, Microsoft Robotics Studio
allowed the students to quickly swap robots. The same
code that worked for a Lego NXT works for an iRobot
Create with just a few minor changes, covered later in this
paper. By the end of the third tutorial, students had a
working understanding of Robotics Studio.
This lab was one of the more straightforward
assignments. Students simply had to follow the online
directions to learn the basics of Robotics Studio. The
students reused topics learned in lab one in every single
other lab. Creating a project, accessing a service, and
linking to other services all continued to be crucial skills
throughout the class.
The only downside to these tutorials was the amount of
time required for completion. It took a surprisingly long
time for students to work through the exercises. The
initial hardware configuration took longer than expected,
which increased the time frame of the entire lab. The lab
assignment initially included the fourth tutorial, but due to
time restraints, the fourth tutorial turned into a separate
lab assignment.
Lab 2: Teleoperation
The second lab assignment, based on robotics tutorial
four, applied the knowledge learned in the first lab. To
further demonstrate the topic of teleoperation, which was
covered in the lecture, the second lab called for the
students to build a robot using Lego NXT kits and a GUI
to control the robot remotely. The students then attached
cameras to their robots and navigated an obstacle course
using only the GUI they created and a live camera feed.
Like most teleoperation situations, the students could not
actually see the robot or the course, only the image sent
back by the camera. Students quickly learned the
importance of the topics covered in lecture as their robots
crashed into walls and tripped over obstacles.
students’ grades. The obstacle course served as a fun
motivator, but it may be worth considering awarding
some extra credit to the students whose robots performed
the best
Lab 3: Wander Behavior and Floor Covering
The third lab supplemented another topic covered in
lecture: wander behaviors. After covering different
approaches to designing wandering behavior in robots in
the lectures, the students had the opportunity to design
and implement their own algorithms. They used an iRobot
Create and attempted to design an effective floor covering
algorithm, either by themselves or based on existing
theories. The techniques with which the students
approached the problem varied from straightforward
applications to complicated algorithms based on existing
theories.
This lab introduced the iRobot Create. Without Robotics
Studio, the students would have had to waste precious lab
time learning the syntax for the new domain, but our
students were able to jump right into the real action. The
students applied their experience with the Lego NXT to
the iRobot Create and used the same kind of commands to
operate the robot. Even though the iRobot Create
includes several sensors not available through Lego NXT,
students accessed the new readings very similarly to how
they accessed Lego NXT readings. Robotics Studio
simplified the transition from one robot to another, and
the students wasted little time adjusting.
Figure 1: Students observing a robot navigating the
obstacle course in Lab 2
This lab relied heavily on the techniques learned in the
first lab. The first lab consisted of a few simple examples,
and the second lab collected those examples into a useful
application. The fun and competitive nature of the
obstacle course drove the students to improve their
robotic designs and GUI to outdo their peers, which lead
to some interesting and unexpected improvements above
and beyond what the lab requested.
In this first iteration, the robots’ performance in the
obstacle course did not actually count towards the
Figure 2: iRobot Create and various hardware
components
After they had an acceptable floor covering algorithm
worked out on the iRobot Create, they had to modify it to
avoid obstacles as well using a Lego sonar service.
Switching back to the Lego NXT robots to solve a similar
problem showed the similarities and differences between
the robots. Students saw in practice a lesson taught in
lecture: different types of sensor suites can solve the same
problem. There is no “right answer” as long as the robot
effectively handles the situation.
The wander behavior and floor covering lab fit in well
with the lecture lessons. The iRobot Creates showed a
real-life application of floor covering since the success of
the iRobot Roomba vacuum product line relies heavily
upon floor covering and wander behaviors. The students
also saw that a simple approach is sometimes better than
an overly complicated plan as some simpler algorithms
outperformed more complex programs.
Lab 4: The Reactive Paradigm: Modeling
Biological Behaviors
Lab Four gave the students quite a bit of freedom in
applying topics from lecture in lab. The lab gave the
students the goal of modeling biological behaviors seen in
nature, specifically collecting food and avoiding
predators. The students decided how to accomplish those
goals, what kind of gathering system to use, whether to
use wheels or legs, how to react to predators, etc. The
robots utilized a camera, and by using a Robotics Studio
service for an image processing software product called
RoboRealm [4], the students programmed the ability to
tell the difference between food and predators based on
color.
already extensive Lego NXT kits each group shared. The
robots came in many forms. The methods of collecting
the food, in this case blue colored plastic balls, varied
greatly from robot to robot. One robot used four legs
instead of two wheels, a first for this class. The robots
also reacted differently to predators, in this instance red
colored plastic balls. Some robots ran directly away,
while others flanked the danger.
The variations in the robots mirrored variations in
behavior in the natural world. Students first learned how
existing roboticists model their robots after animals found
in nature and then designed their own robots themselves.
Again, it is difficult to quantify creativity, but generally
the students who worked the hardest received the best
grades.
Lab 5: Line Following
Lab Five handled a common educational robotics
problem: line following. The students modified the
functionality of built-in cliff sensors in iRobot Creates to
detect lines instead of cliffs, and created programs to
make the iRobots follow a path marked by lines. Students
also used a camera attached to a Lego NXT robot and
used RoboRealm with Robotics Studio to follow a line
based upon camera input as well.
This lab demonstrated the versatility of the iRobot Create
platform. The cliff sensors are actually designed to detect
sudden drop offs, preventing the robots from falling down
stairs or off desks. However, by delving into the lower
levels of the programming, the cliff sensors can be
modified to detect the reflectivity of the surface beneath
them, perfect for line detection.
Lab 6: Object Counting
Figure 3: A Lego robot designed to throw balls at a
target
The students were limited only by their creativity and the
amount of Lego pieces they had at their disposal. The
Lego extension kits solved the latter, supplementing the
The sixth and final lab assignment gave the students the
goal of counting objects using any of the ideas introduced
in the other labs. The point of this lab was to show that
the students could easily adapt previously learned topics
to tackle new problems. Almost every group used a Lego
NXT robot with sonar to drive by objects and count them
based upon their sonar readings. The antithesis of the
first lab, this lab took a surprisingly short amount of time
to complete. After all the problems the students solved,
this one seemed easy by comparison. The fact that the
students completed this lab in a short amount of time
proved the effectiveness of the other labs.
Final Project
In addition to the labs, the students also had the option of
writing a research paper or tackling a larger-scale robotics
project of their choosing. A surprising number of students
chose to write a paper, but the robots the students did
build were very interesting. Two students created ball
launchers that automatically tracked their targets using
cameras and the RoboRealm service. One student created
a robot that draws pictures and attempted to have the
drawings based on camera input. Another student created
a robot that, when given a can, found a recycling bin and
recycled the can. The final project gave the students
enough freedom to explore any aspect of robotics in
which they had an interest but that may not have been
covered in class.
learned how professional roboticists used certain theories
to develop robots for specific applications and applied the
same principles to the problems given to them in the
assignments the very next lab meeting.
The labs also shared a difficulty in terms of evaluation.
How does an educator quantify creativity? Students who
worked the hardest on their robots should be rewarded.
However, if they worked very hard on an idea that didn’t
quite translate to a successfully functioning robot, it can
be difficult to appropriately assess their effort. This
problem occurs in other domains (such as complex
programming projects) as well, but it seems to be more
acute within the robotics realm, where the problem could
actually be in the hardware (inaccurate sensors, etc.)
Experimentation and trial and error are important
components of the creative process in the sciences. On
the other hand, an important aim in any computer science
project is to end up with a working product. So, the
question becomes how one can motivate students if their
grade is not solely based on performance but avoid
punishing students who developed a great theory that
unraveled in practice.
Implementation Suggestions
Figure 4: Recycling Robot built on iCreate base
A final project helps bring together the concepts
introduced earlier in the class and allows the students to
explore their own interests within the subject. The
students can demonstrate their knowledge more
completely by applying what they’ve learned to an
encompassing problem than separating each subject into
individual lab assignments. While a paper also allows
this, to some degree, we found that the projects were more
challenging, and to a large degree, more rewarding to the
students who undertook the challenge.
Pros and Cons of the Setup of the Class
All of the laboratory assignments shared common
strengths and weaknesses. Each assignment paired very
successfully with the topics covered in lecture. Students
Microsoft Robotics Studio provided a great link between
the language syntax already known to students and
unfamiliar robotics semantics. We highly recommend
using it as a main focus in the laboratory section of the
course. It has an increasing amount of third party support,
which gives users many robotics and software platforms
as compatible options. RoboRealm is an example of one
such software platform. The program focuses on machine
vision. By using the service the company released,
Robotics Studio can interact with RoboRealm to give
robots advanced vision capabilities. Neither Robotics
Studio nor RoboRealm cost anything for education
purposes.
To help motivate the students to design creative robots
instead of robots that simply fulfill the requirements of
the lab, we suggest having a grading system based on
design as well as performance. As seen in the second lab,
friendly competition between students drove them to
work harder on designing creative robots. Basing the
students’ grade on both performance and design enables
the instructor to reward hard working students without
punishing students whose theories simply did not work
out. Because grading the assignments is not just a matter
of running the code, but also of connecting to particular
robots and testing the code, we suggest having the student
demonstrate the functionality of the code for the instructor
of the class. We also found it helpful to have the students
include a written description of their algorithms and
thought processes regarding the development of their labs.
Future Work
We are currently researching the usefulness of yet another
robotic platform called Lynxmotion. The company has
released the support files necessary to make their robots
work with Microsoft Robotics Studio, and controlling
them is just a matter of finding out what exactly each
command does.
Robotics Studio may not fully support all of the
capabilities of a robotic family like Lynxmotion right out
of the box, but users can add functionality to Robotics
Studio, expand the platform, and create services for
anything not yet included. For example, our Pioneer P3DX is equipped with a sonar suite that Robotics Studio
does not yet support. However, the Pioneer software
includes low level signal handlers, and we created a new
Pioneer sonar service by feeding the signals into a higher
level generic sonar service. Now we can interact with the
sonar on the Pioneer through Robotics Studio even though
Robotics Studio did not originally include this ability.
Similarly, we can add any missing functionality to new
robots by routing low level signals through high level
handlers, which provides new opportunities for
independent studies outside of the classroom.
Conclusion
We see an exciting future in this class. Although iterative
improvements are always possible (particularly after
major curriculum development), we were quite satisfied
with the new software and hardware being utilized in the
course. The increasing availability of various robotic
platforms provides students with more room to express
their programming creativity, and Microsoft Robotics
Studio allows increased experimentation with different
hardware. Although we might have to develop our own
functionality for some robots, this can take the shape of
new independent studies. In fact, robotics offers many
opportunities for undergraduate student research projects.
Thus offering robotics improves the major by not only
increasing class options but also by opening up
educational opportunities outside the classroom.
About the Authors
The work described in this paper was funded by a
PASSHE Professional Development Grant awarded to Dr.
Stephanie Elzer. The grant allowed Kevin Workman, a
senior at Millersville University, to work fulltime on this
project, under Dr. Elzer's supervision, during the summer
of 2007. The Robotics course was offered during the Fall
2007 semester. The work has continued as an independent
study project on incorporating Lynxmotion robots into
Microsoft Robotics Studio during Spring 2008.
References
[1] Bill Gates, A Robot in Every Home.
American Magazine, January 2007 Issue.
Scientific
[2] Robin Murphy, Introduction to ai
robotics
(Cambridge, Massachusetts: The MIT Press, 2000).
[3]
Microsoft
Robotics
Studio.
MSDN.
http://msdn2.microsoft.com/en-us/robotics/default.aspx
[14 February 2008]
[4] Free Robotic Machine Vision Software. RoboRealm.
http://www.roborealm.com/ [14 February 2008]