Robots_and_Their_Applications.pdf

Chapter 1
Robots and Their Applications
Although everyone seems to know what a robot is, it is hard to give a precise def-
inition. The Oxford English Dictionary gives the following definition: “A machine
capable of carrying out a complex series of actions automatically, especially one
programmable by a computer.” This definition includes some interesting elements:
• “Carrying out actions automatically.” This is a key element in robotics, but also
in many other simpler machines called automata. The difference between a robot
and a simple automaton like a dishwasher is in the definition of what a “complex
series of actions” is. Is washing clothes composed of a complex series of actions
or not? Is flying a plane on autopilot a complex action? Is cooking bread complex?
For all these tasks there are machines that are at the boundary between automata
and robots.
• “Programmable by a computer” is another key element of a robot, because some
automata are programmed mechanically and are not very flexible. On the other
hand computers are found everywhere, so it is hard to use this criterion to distin-
guish a robot from another machine.
A crucial element of robots that is not mentioned explicitly in the definition is the
use of sensors. Most automata do not have sensors and cannot adapt their actions to
their environment. Sensors are what enable a robot to carry out sanscomplex tasks.
In Sects.1.1–1.5 of this introductory chapter we give a short survey of differ-
ent types of robots. Section1.6 describes the generic robot we use and Sect.1.7
presents the pseudocode used to formalize the algorithms. Section1.8 gives a detailed
overview of the contents of the book.
© The Author(s) 2018
M. Ben-Ari and F. Mondada, Elements of Robotics,
https://doi.org/10.1007/978-3-319-62533-1_1
1

2 1 Robots and Their Applications
robot
fixed
mobile
aquatic
terrestrial
airborne
wheeled
legged
Fig. 1.1 Classification of robots by environment and mechanism of interaction
1.1 Classification of Robots
Robotscanbeclassifiedaccordingtotheenvironmentinwhichtheyoperate(Fig.1.1).
The most common distinction is between fixed and mobile robots. These two types
of robots have very different working environments and therefore require very dif-
ferent capabilities. Fixed robots are mostly industrial robotic manipulators that work
in well defined environments adapted for robots. Industrial robots perform specific
repetitive tasks such soldering or painting parts in car manufacturing plants. With
the improvement of sensors and devices for human-robot interaction, robotic manip-
ulators are increasingly used in less controlled environment such as high-precision
surgery.
By contrast, mobile robots are expected to move around and perform tasks in
large, ill-defined and uncertain environments that are not designed specifically for
robots. They need to deal with situations that are not precisely known in advance
and that change over time. Such environments can include unpredictable entities like
humans and animals. Examples of mobile robots are robotic vacuum cleaners and
self-driving cars.
There is no clear dividing line between the tasks carried out by fixed robots and
mobile robots—humans may interact with industrial robots and mobile robots can
be constrained to move on tracks—but it is convenient to consider the two classes
as fundamentally different. In particular, fixed robots are attached to a stable mount
on the ground, so they can compute their position based on their internal state, while
mobile robots need to rely on their perception of the environment in order to compute
their location.
There are three main environments for mobile robots that require significantly
different design principles because they differ in the mechanism of motion: aquatic
(underwater exploration), terrestrial (cars) and aerial (drones). Again, the classifica-
tion is not strict, for example, there are amphibious robots that move in both water
and on the ground. Robots for these three environments can be further divided into
subclasses: terrestrial robots can have legs or wheels or tracks, and aerial robots can
be lighter-than-air balloons or heavier-than-air aircraft, which are in turn divided into
fixed-wing and rotary-wing (helicopters).
Robots can be classified by intended application field and the tasks they perform
(Fig.1.2). We mentioned industrial robots which work in well-defined environments

1.1 Classification of Robots 3
robot
industrial
service
logistics
manufacturing
medical
home
educational
defense
Fig. 1.2 Classification of robots by application field
on production tasks. The first robots were industrial robots because the well-defined
environment simplified their design. Service robots, on the other hand, assist humans
in their tasks. These include chores at home like vacuum clears, transportation like
self-driving cars, and defense applications such as reconnaissance drones. Medicine,
too, has seen increasing use of robots in surgery, rehabilitation and training. These
are recent applications that require improved sensors and a closer interaction with
the user.
1.2 Industrial Robots
The first robots were industrial robots which replaced human workers performing
simple repetitive tasks. Factory assembly lines can operate without the presence of
humans, in a well-defined environment where the robot has to perform tasks in a
specified order, acting on objects precisely placed in front of it (Fig.1.3).
One could argue that these are really automata and not robots. However, today’s
automata often rely on sensors to the extent that they can be considered as robots.
However, their design is simplified because they work in a customized environment
which humans are not allowed to access while the robot is working.
However, today’s robots need more flexibility, for example, the ability to manip-
ulate objects in different orientations or to recognize different objects that need to be
packaged in the right order. The robot can be required to transport goods to and from
warehouses. This brings additional autonomy, but the basic characteristic remains:
the environment is more-or-less constrained and can be adapted to the robot.
Additional flexibility is required when industrial robots interact with humans and
this introduces strong safety requirements, both for robotic arms and for mobile
robots. In particular, the speed of the robot must be reduced and the mechanical

Fig. 1.3 Robots on an assembly line in a car factory. Source https://commons.wikimedia.org/
wiki/File:AKUKA_Industrial_Robots_IR.jpg by Mixabest (Own work). CC BY-SA 3.0 (http://
creativecommons.org/licenses/by-sa/3.0) or GFDL (http://www.gnu.org/copyleft/fdl.html), via
Wikimedia Commons
design must ensure that moving parts are not a danger to the user. The advantage of
humans working with robots is that each can perform what they do best: the robots
perform repetitive or dangerous tasks, while humans perform more complex steps
and define the overall tasks of the robot, since they are quick to recognize errors and
opportunities for optimization.
1.3 Autonomous Mobile Robots
Many mobile robots are remotely controlled, performing tasks such as pipe inspec-
tion, aerial photography and bomb disposal that rely on an operator controlling the
device. These robots are not autonomous; they use their sensors to give their oper-
ator remote access to dangerous, distant or inaccessible places. Some of them can
be semi-autonomous, performing subtasks automatically. The autopilot of a drone
stabilizes the flight while the human chooses the flight path. A robot in a pipe can
control its movement inside the pipe while the human searches for defects that need

1.3 Autonomous Mobile Robots 5
to be repaired. Fully autonomous mobile robots do not rely on an operator, but instead
they make decisions on their own and perform tasks, such as transporting material
while navigating in uncertain terrain (walls and doors within buildings, intersections
on streets) and in a constantly changing environment (people walking around, cars
moving on the streets).
The first mobile robots were designed for simple environments, for example,
robots that cleaned swimming pools or robotic lawn mowers. Currently, robotic vac-
uum cleaners are widely available, because it has proved feasible to build reasonably
priced robots that can navigate an indoor environment cluttered with obstacles.
Many autonomous mobile robots are designed to support professionals working
in structured environments such as warehouses. An interesting example is a robot
for weeding fields (Fig.1.4). This environment is partially structured, but advanced
sensing is required to perform the tasks of identifying and removing weeds. Even
in very structured factories, robot share the environment with humans and therefore
their sensing must be extremely reliable.
Perhaps the autonomous mobile robot getting the most publicity these days is the
self-driving car. These are extremely difficult to develop because of the highly com-
plex uncertain environment of motorized traffic and the strict safety requirements.
Fig. 1.4 Autonomous mobile robot weeding a field (Courtesy of Ecorobotix)

An even more difficult and dangerous environment is space. The Sojourner and
Curiosity Mars rovers are semi-autonomous mobile robots. The Sojourner was active
for three months in 1997. The Curiosity has been active since landing on Mars in
2012! While a human driver on Earth controls the missions (the routes to drive and
the scientific experiments to be conducted), the rovers do have the capability of
autonomous hazard avoidance.
Much of the research and development in robotics today is focused on making
robots more autonomous by improving sensors and enabling more intelligent control
of the robot. Better sensors can perceive the details of more complex situations, but
to handle these situations, control of the behavior of the robot must be very flexible
and adaptable. Vision, in particular, is a very active field of research because cameras
are cheap and the information they can acquire is very rich. Efforts are being made
to make systems more flexible, so that they can learn from a human or adapt to new
situations. Another active field of research addresses the interaction between humans
and robots. This involves both sensing and intelligence, but it must also take into
account the psychology and sociology of the interactions.
1.4 Humanoid Robots
Science fiction and mass media like to represent robots in a humanoid form. We
are all familiar with R2-D2 and 3-CPO, the robotic characters in the Star Wars
movies, but the concept goes far back. In the eighteenth century, a group of Swiss
watchmakers—Pierre and Henri-Louis Jaquet-Droz and Jean-Frédéric Leschot—
built humanoid automata to demonstrate their mechanical skills and advertise their
watches. Many companies today build humanoid robots for similar reasons.
Humanoid robots are a form of autonomous mobile robot with an extremely
complex mechanical design for moving the arms and for locomotion by the legs.
Humanoidrobotsareusedforresearchintothemechanicsofwalkingandintohuman-
machine interaction. Humanoid robots have been proposed for performing services
andmaintenanceinahouseoraspacestation.Theyarebeingconsideredforproviding
care to the elderly who might feel anxious in the presence of a machine that did
not appear human. On the other hand, robots that look very similar to humans can
generate repulsion, a phenomenon referred to as the uncanny valley.
Humanoid robots can be very difficult to design and control. They are expensive
to build with multiple joints that can move in many different ways. Robots that use
wheels or tracks are preferred for most applications because they are simpler, less
expensive and robust.

1.5 Educational Robots 7
Fig. 1.5 a Thymio robot. Source https://www.thymio.org/en:mediakit by permission of École
Polytechnique Fédérale de Lausanne and École Cantonale d’Art de Lausanne.b Dash robot. Source
https://www.makewonder.com/mediakit by permission of Wonder Workshop
1.5 Educational Robots
Advances in the electronics and mechanics have made it possible to construct robots
that are relatively inexpensive. Educational robots are used extensively in schools,
both in classrooms and in extracurricular activities. The large number of educational
robots makes it impossible to give a complete overview. Here we give few examples
that are representative of robots commonly used in education.
Pre-Assembled Mobile Robots
Many educational robots are designed as pre-assembled mobile robots. Figure1.5a
shows the Thymio robot from Mobsya and Fig.1.5b shows the Dash robot from Won-
der Workshop. These robots are relatively inexpensive, robust and contain a large
number of sensors and output components such as lights. An important advantage
of these robots is that you can implement robotic algorithms “out of the box,” with-
out investing hours in mechanical design and construction. However, pre-assembled
robots cannot be modified, though many do support building extensions using, for
example, LEGO®
components.
Robotics Kits
The LEGO®
Mindstorms robotics kits (Fig.1.6a) were introduced in 1998.1
A kit
consists of standard LEGO®
bricks and other building components, together with
motors and sensors, and a programmable brick which contains the computer that
controls the components of the robot. The advantage of robotics kits is that they are
1The figure shows the latest version called EV3 introduced in 2014.

Fig. 1.6 a LEGO® Mindstorms EV3 (Courtesy of Adi Shmorak, Intelitek), b Poppy Ergo Jr robotic
arms (Courtesy of the Poppy Project)
flexible: you can design and build a robot to perform a specific task, limited only
by your imagination. A robotics kit can also be used to teach students mechanical
design. The disadvantages of robotics kits are that they are more expensive than
simple pre-assembled robots and that exploration of robotics algorithms depends on
the one’s ability to successfully implement a robust mechanical design.
A recent trend is to replace fixed collections of bricks by parts constructed by 3D
printers. An example is the Poppy Ergo Jr robotic arm (Fig.1.6b). The use of 3D
printed parts allows more flexibility in the creation of the mechanical structure and
greater robustness, but does require access to a 3D printer.
Robotic Arms
To act on its environment, the robot needs an actuator which is a component of a
robot that affects the environment. Many robots, in particular robotic arms used in
industry, affect the environment through end effectors, usually grippers or similar
tools (Figs.1.3, 14.1 and 15.5b). The actuators of mobile robots are the motors that
cause the robot to move, as well as components such as the vacuum pump of a vacuum
cleaner.
Educational robots are usually mobile robots whose only actuators are its motors
and display devices such as lights, sounds or a screen. End effectors can be built with
robotics kits or by using additional components with pre-assembled robots, although
educational robotic arms do exist (Fig.1.6b). Manipulation of objects introduces

1.5 Educational Robots 9
Fig. 1.7 Blockly software for the Thymio robot
complexity into the design; however, since the algorithms for end effectors are similar
to the algorithms for simple mobile robots, most of the activities in the book will
assume only that your robot has motors and display devices.
Software Development Environments
Every educational robotics system includes a software development environment.
The programming language can be a version of a standard programming language
like Java or Python. Programming is simplified if a block-based language is used,
usually a language based upon Scratch or Blockly (Fig.1.7).
To further simplify programming a robot by young students, a fully graphical
programming notation can be used. Figure1.8 shows VPL (Visual Programming
Language), a graphical software environment for the Thymio robot. It uses event-
action pairs: when the event represented by the block on the left occurs, the actions
in the following blocks are performed.
Figure1.9 shows the graphical software environment for the Dash robot. It also
uses events and actions, where the actions are represented by nodes and events are
represented by arrows between nodes.

Fig. 1.8 VPL software for the Thymio robot
Fig. 1.9 Wonder software for the Dash robot. Source https://www.makewonder.com/mediakit by
permission of Wonder Workshop

1.6 The Generic Robot 11
1.6 The Generic Robot
This section presents the description of a generic robot that we use to present the
robotics algorithms. The capabilities of the generic robot are similar to those found
in educational robots, but the one you use may not have all the capabilities assumed
in the presentations so you will have to improvise. You may not understand all the
terms in the following description just yet, but it is important that the specification
be formalized. Further details will be given in later chapters.
1.6.1 Differential Drive
The robot is a small autonomous vehicle with differential drive, meaning that it has
two wheels that are driven by independent motors (Fig.1.10). To cause the robot to
move, set the motor power to a value from −100 (full power backwards) through 0
(stopped) to 100 (full power forwards). There is no predefined relationship between
the motor power and the velocity of robot. The motor can be connected to the wheels
through different gear ratios, the type of tires on the wheels affects their traction, and
sandy or muddy terrain can cause the wheels to slip.
Figure1.10 shows a view of the robot from above. The front of the robot is the
curve to the right which is also the forward direction of the robot’s motion. The
wheels (black rectangles) are on the left and right sides of the rear of the robot’s
body. The dot is the point on the axle halfway between the wheels. When the robot
turns, it turns around an axis vertical to this point. For stability, towards the front of
the robot there is a support or non-driven wheel.
Mechanical Drawing
A broken line is the standard notation in mechanical engineering for the axis of symmetry in
a component such as a wheel. When the side view of a wheel is displayed, the intersection
of the two axes of symmetry denotes the axis of rotation that is perpendicular to the plane of
the page. To avoid cluttering the diagrams, we simplify the notation by only showing broken
lines for an axis of rotation of a component such as a wheel. In addition, the intersection
support (on the bottom of the robot)
forward
Fig. 1.10 Robot with differential drive

denoting a perpendicular axis is usually abbreviated to a cross, possibly contained within
the wheel or its axle.
Differential drive has several advantages: it is simple since it has only two motors
without additional components for steering and it allows the robot to turn in place. In
a car, two wheels are driven together (or four wheels are driven in pairs) and there is
a separate complex mechanism for steering called Ackermann steering. Since a car
cannot turn in place, drivers must perform complicated maneuvers such as parallel
parking; human drivers readily learn to do this, but such maneuvers are difficult for an
autonomous system. Anautonomous robot needs toperformintricatemaneuvers with
verysimplemovements,whichiswhydifferentialdriveisthepreferredconfiguration:
it can easily turn to any heading and then move in that direction.
The main disadvantage of a differential drive system is that it requires a third point
of contact with the ground unlike a car which already has four wheels to support it
and thus can move easily on difficult terrain. Another disadvantage is that it cannot
drive laterally without turning. There are configurations that enable a robot to move
laterally (Sect.5.12), but they are complex and expensive. Differential drive is also
used in tracked vehicles such as earth-moving equipment and military tanks. These
vehicles can maneuver in extremely rough terrain, but the tracks produce a lot of
friction so movement is slow and not precise.
Setting Power or Setting Speed
The power supplied by a motor is regulated by a throttle, such as a pedal in a car or levers
in an airplane or boat. Electrical motors used in mobile robots are controlled by modifying
the voltage applied to the motors using a technique called pulse width modulation. In many
educational robots, control algorithms such as those described in Chap.6, are used to ensure
that the motors rotate at a specified target speed. Since we are interested in concepts and
algorithms for designing robots, we will express algorithms in terms of supplying power and
deal separately with controlling speed.
1.6.2 Proximity Sensors
The robot has horizontal proximity sensors that can detect an object near the robot.
There exist many technologies that can be used to construct these sensors, such
as infrared, laser, ultrasound; the generic robot represents robots that use any of
these technologies. We do specify that the sensors have the following capabilities:
A horizontal proximity sensor can measure the distance (in centimeters) from the
robot to an object and the angle (in degrees) between the front of the robot and the
object. Figure1.11a shows an object located at 3cm from the center of the robot at
an angle of 45◦
from the direction in which the robot is pointing.2
In practice, an educational robot will have a small number of sensors, so it may
not be able detect objects in all directions. Furthermore, inexpensive sensors will
not be able to detect objects that are far away and their measurements will not be
2See Appendix A on the conventions for measuring angles.

1.6 The Generic Robot 13
3
c
m
45
(a) (b)
Fig. 1.11 a Robot with a rotating sensor (gray dot), b Robot with two ground sensors on the bottom
of the robot (gray rectangles)
accurate. The measurements will also be affected by environmental factors such as
the type the object, the ambient light, and so on. To simplify our algorithms, we do
not assume any predefined limitations, but when you implement the algorithms you
will have to take the limitations into account.
1.6.3 Ground Sensors
Ground sensors are mounted on the bottom of the robot. Since these sensors are very
close to the ground, there is no meaning to distance or angle; instead, the sensor
measures the brightness of the light reflected from the ground in arbitrary values
between 0 (totally dark) and 100 (totally light). The generic robot has two ground
sensors mounted towards the front of the robot (Fig.1.11b), though sometimes we
present algorithms that use only one sensor. The figure shows a top view of the robot
although the ground sensors are on the bottom of the robot.
1.6.4 Embedded Computer
The robot is equipped with an embedded computer (Fig.1.12). The precise specifi-
cation of the computer is not important but we do assume certain capabilities. The
computer can read the values of the sensors and set the power of the motors. There
is a way of displaying information on a small screen or using colored lights. Signals
and data can be input to the computer using buttons, a keypad or a remote control.
Data is input to the computer by events such as touching a button. The occurrence
of an event causes a procedure called an event handler to be run. The event can
be detected by the hardware, in which case the term interrupt is used, or it can be
detected by the software, usually, by polling, where the operating system checks
for events at predefined intervals. When the event handler terminates, the previous
computation is started up again.

Event handlers are different from sequential programs that have an initial instruc-
tion that inputs data and a final instruction that displays the output, because event
handlers are run in response to unpredictable events. Event handling is used to imple-
ment graphic user interfaces on computers and smartphones: when you click on or
touch an icon an event handler is run. On a robot, an event can be a discrete input
like touching a key. Events can also occur when a continuous value like the value
read by a sensor goes above or below a predefined value called a threshold.
The computer includes a timer which functions like a stopwatch on a smartphone.
A timer is a variable that is set to a period of time, for example, 0.5s, which is
represented as an integer number milliseconds or microseconds (0.5s is 500ms).
The hardware clock of the computer causes an interrupt at fixed intervals and the
operating system decrements the value of the timer. When its value goes to zero, we
say that the timer has expired; an interrupt occurs.
Timers are used to implement repeated events like flashing a light on and off. They
are also used for polling, an alternative to event handlers: instead of performing a
computation when an event occurs, sensors are read and stored periodically. More
precisely, polling occurs as an event handler when a timer expires, but the design of
software using polling can be quite different from the design of event-based software.
1.7 The Algorithmic Formalism
Algorithms that are implemented as computer programs are used by the embed-
ded computer to control the behavior of the robot. We do not give programs in any
specific programming language; instead, algorithms are presented in pseudocode, a
structured format using a combination of natural language, mathematics and pro-
gramming structures. Algorithm 1.1 is a simple algorithm for integer multiplication
using repeated addition. The input of the algorithm is a pair of integers and the output
is the product of the two input values. The algorithm declares three integer variables
x, a, b. There are five statements in the executable part. Indentation is used (as in the
Python programming language) to indicate the scope of the loop. An arrow is used
computer
timer
sensors
buttons
motors
display
Fig. 1.12 Embedded computer

1.7 The Algorithmic Formalism 15
for assignment so that the familiar symbols = and = can be used for equality and
inequality in mathematical formulas.3
Algorithm 1.1: Integer multiplication
integer x ← 0
integer a, b
1: a ← input an integer
2: b ← input a non-negative integer
3: while b = 0
4: x ← x + a // Add the value a to x
5: b ← b − 1 // for each b
The motor power is set using assignment statements:
left-motor-power ← 50
right-motor-power ← −50
We have defined our proximity sensors as returning the distance to a detected
object and its angle relative to the forward direction of the robot, but it will often be
more convenient to use natural language expressions such as:
when object detected in front
when object not detected in back
1.8 An Overview of the Content of the Book
The first six chapters form the core of robotics concepts and algorithms.
Chapter 1 Robots and Their Applications This chapter surveys and classifies
robots. It also specifies the generic robot and formalisms used to present algorithms
in this book.
Chapter 2 Sensors Robots are more than remotely controlled appliances like a
television set. They show autonomous behavior based on detecting objects in their
environment using sensors. This chapter gives an overview of the sensors used by
robots and explains the concepts of range, resolution, precision and accuracy. It also
discusses the nonlinearity of sensors and how to deal with it.
Chapter 3 Reactive Behavior When an autonomous robot detects an object in
its environment, it reacts by changing its behavior. This chapter introduces robotics
algorithms where the robot directly changes its behavior based upon input from its
sensors. Braitenberg vehicles are simple yet elegant examples of reactive behavior.
The chapter presents several variants of line following algorithms.
3Many programming languages use = for assignment and then == for equality and != for inequality.
This is confusing because equality x = y is symmetrical, but assignment is not x=x+1. We prefer
to retain the mathematical notation.

Chapter 4 Finite State Machines A robot can be in different states, where its
reaction to input from its sensors depends not only on these values but also on the
current state. Finite state machines are a formalism for describing states and the
transitions between them that depend on the occurrence of events.
Chapter 5 Robotic Motion and Odometry Autonomous robots explore their envi-
ronment, performing actions. Hardly a day goes by without a report on experience
with self-driving cars. This chapter reviews concepts related to motion (distance,
time, velocity, acceleration), and then presents odometry, the fundamental method
that a robot uses to move from one position to another. Odometry is subject to sig-
nificant errors and it is important to understand their nature.
The second part of the chapter gives an overview of advanced concepts of robotic
motion: wheel encoders and inertial navigation systems that can improve the accu-
racy of odometry, and degrees of freedom and holonomy that affect the planning of
robotic motion.
Chapter 6 Control An autonomous robot is a closed loop control system because
input from its sensors affects its behavior which in turn affects what is measured
by the sensors. For example, a self-driving car approaching a traffic light can brake
harder as it gets close to the light. This chapter describes the mathematics of control
systems that ensure optimal behavior: the car actually does stop at the light and the
braking is gradual and smooth.
An autonomous mobile robot must somehow navigate from a start position to a
goal position, for example, to bring medications from the pharmacy in a hospital to
the patient. Navigation is a fundamental problem in robotics that is difficult to solve.
The following four chapters present navigation algorithms in various contexts.
Chapter 7 Local Navigation: Obstacle Avoidance The most basic requirement
from a mobile robot is that it does not crash into walls, people and other obstacles.
This is called local navigation because it deals with the immediate vicinity of the
robot and not with goals that the robot is trying to reach. The chapter starts with
wall following algorithms that enable a robot to move around an obstacle; these
algorithms are similar to algorithms for navigating a maze. The chapter describes a
probabilistic algorithm that simulates the navigation by a colony of ants searching
for a food source.
Chapter 8 Localization Once upon a time before every smartphone included GPS
navigation, we used to navigate with maps printed on paper. A difficult problem is
localization: canyoudetermineyour current positiononthemap?Mobilerobots must
solve the same localization problem, often without the benefit of vision. The chapter
describes localization by trigonometric calculations from known positions. This is
followed by sections on probabilistic localization: A robot can detect a landmark
but there may be many similar landmarks on the map. By assigning probabilities
and updating them as the robot moves through the environment, it can eventually
determine its position with relative certainty.
Chapter 9 Mapping But where does the map come from? Accurate street maps
are readily available, but a robotic vacuum cleaner does not have a map of your
apartment.Anundersearobotisusedtoexploreanunknownenvironment.Toperform

1.8 An Overview of the Content of the Book 17
localization the robot needs a map, but to create a map of an unknown environment
the robot needs to localize itself, in the sense that it has to know how far it has
moved from one point of the environment to another. The solution is to perform
simultaneous localization and mapping. The first part of the chapter describes an
algorithm for exploring an environment to determine the locations of obstacles. Then
an simplified algorithm for simultaneous localization and mapping is presented.
Chapter 10 Mapping-Based Navigation Now that the robot has a map, suppose
that it is assigned a task that requires it to move from a start position to a goal position.
What route should it take? This chapter presents two algorithms for path planning:
Dijkstra’s algorithm, a classic algorithm for finding the shortest path in a graph, and
the A∗
algorithm, a more efficient version of Dijkstra’s algorithm that uses heuristic
information.
The following chapters present advanced topics in robotics. They are independent
of each other so you can select which ones to study and in what order.
Chapter 11 Fuzzy Logic Control Control algorithms (Chap.6) require the spec-
ification of a precise target value: a heating system needs the target temperature of
a room and a cruise control system needs the target speed of a car. An alternate
approach called fuzzy logic uses imprecise specifications like cold, cool, warm, hot,
or very slow, slow, fast, very fast. This chapter presents fuzzy logic and shows how
it can be used to control a robot approaching an object.
Chapter 12 Image Processing Most robotic sensors measure distances and angles
using lasers, sound or infrared light. We humans rely primarily on our vision. High
qualitydigitalcamerasareinexpensiveandfoundoneverysmartphone.Thedifficulty
is to process and interpret the images taken by the camera, something our brains do
instantly. Digital image processing has been the subject of extensive research and its
algorithms are used in advanced robots that can afford the needed computing power.
In this chapter we survey image processing algorithms and show how an educational
robot can demonstrate the algorithms even without a camera.
Chapter 13 Neural Networks Autonomous robots in highly complex environ-
ments cannot have algorithms for every possible situation. A self-driving car cannot
possibly know in advance all the different vehicles and configurations of vehicles
that it encounters on the road. Autonomous robots must learn from their experi-
ence and this is a fundamental topic in artificial intelligence that has been studied for
many years. This chapter presents one approach to learning: artificial neural networks
modeled on the neurons in our brains. A neural network uses learning algorithms to
modify its internal parameters so that it continually adapts to new situations that it
encounters.
Chapter 14 Machine Learning Another approach to learning is a statistical tech-
nique called machine learning. This chapter describes two algorithms for distinguish-
ing between two alternatives, for example, distinguishing between a traffic light that
is red and one that is green. The first algorithm, called linear discriminant analy-
sis, is based on the means and variances of a set of samples. The second algorithm
uses perceptrons, a form of neural network that can distinguish between alternatives
even when the samples do not satisfy the statistical assumptions needed for linear
discriminant analysis.

Chapter 15 Swarm Robotics If you need to improve the performance of a sys-
tem, it is often easier to use multiple instances of a component rather than trying
to improve the performance of an individual component. Consider a problem such
as surveying an area to measure the levels of pollution. You can use a single very
fast (and expensive) robot, but it can be easier to use multiple robots, each of which
measures the pollution in a small area. This is called swarm robotics by analogy
with a swarm of insects that can find the best path between their nest and a source
of food. The fundamental problem in swarm robotics, as in all concurrent systems,
is to develop methods for coordination and communications among the robots. This
chapter presents two such techniques: exchange of information and physical inter-
actions.
Chapter 16 Kinematics of a Robotic Manipulator Educational robots are small
mobile robots that move on a two-dimensional surface. There are mobile robots
that move in three-dimensions: robotic aircraft and submarines. The mathematics
and algorithms for three-dimensional motion were developed in another central field
of robotics: manipulators that are used extensively in manufacturing. This chapter
presents a simplified treatment of the fundamental concepts of robotic manipula-
tors (forward and inverse kinematics, rotations, homogeneous transforms) in two
dimensions, as well as a taste of three-dimensional rotations.
There are two appendices:
Appendix A Units of Measurement This appendix contains Table A.1 with units
of measurements. Table A.2 gives prefixes that are used with these units.
Appendix B Mathematical Derivations and Tutorials This chapter contains tuto-
rials that review some of the mathematical concepts used in the book. In addition,
some of the detailed mathematical derivations have been collected here so as not to
break the flow of the text.
1.9 Summary
Robots are found everywhere: in factories, homes and hospitals, and even in outer
space. Much research and development is being invested in developing robots that
interactwithhumansdirectly.Robotsareusedinschoolsinordertoincreasestudents’
motivation to study STEM and as a pedagogical tool to teach STEM in a concrete
environment. The focus of this book is the use of educational robots to learn robotic
algorithms and to explore their behavior.
Most educational robots have a similar design: a small mobile robot using differen-
tial drive and proximity sensors. To make this book platform-independent, we defined
a generic robot with these properties. The algorithms presented in this book for the
generic robot should be easy to implement on educational robots, although different
robots will have different capabilities in terms of the performance of their motors
and sensors. The algorithms are presented in a language-independent pseudocode
that should be easy to translate into any textual or graphics language that your robot
supports.

1.10 Further Reading 19
1.10 Further Reading
For a non-technical overview of robotics with an emphasis on biologically-inspired
and humanoid robotics, see Winfield [8].
The International Organization for Standardization (ISO)4
publishes standards for
robotics. On their website https://www.iso.org/ you can find the catalog of robotics
(ISO/TC 299) and formal definitions of robotics concepts: ISO 8373:2012 Robots
and robotic devices—Vocabulary and ISO 19649:2017 Mobile robots—Vocabulary.
The topics in this book are presented in greater detail in advanced textbooks on
robotics such as [3, 6]. Their introductory chapters give many examples of robots.
Educational robots come with documentation of their capabilities and software
development environments. There are also textbooks based upon specific robots,
for example, [7] on programming the LEGO®
Mindstorms robotics kits and [4] on
using Python to program the Scribbler robots. The design of the Visual Programming
Language (VPL) is presented in [5].
Pseudocode is frequently used in textbooks on data structures and algorithms,
starting from the classic textbook [1]. The style used here is taken from [2].
References
1. Aho, A.V., Hopcroft, J.E., Ullman, J.D.: The Design and Analysis of Computer Algorithms.
Addison-Wesley, USA (1974)
2. Ben-Ari, M.: Principles of Concurrent and Distributed Programming, 2nd edn. Addison-Wesley,
USA (2006)
3. Dudek, G., Jenkin, M.: Computational Principles of Mobile Robotics, 2nd edn. Cambridge
University Press, UK (2010)
4. Kumar, D.: Learning Computing with Robots. Lulu (2011). Download from http://calicoproject.
org/Learning_Computing_With_Robots
5. Shin, J., Siegwart, R., Magnenat, S.: Visual programming language for Thymio II robot. In:
Proc. of the 2014 Conference on Interaction Design and Children (IDC) (2014)
6. Siegwart, R., Nourbakhsh, I.R., Scaramuzza, D.: Introduction to Autonomous Mobile Robots,
2nd edn. MIT Press, USA (2011)
7. Trobaugh, J.J., Lowe, M.: Winning LEGO MINDSTORMS Programming. Apress (2012)
8. Winfield, A.: Robotics: A Very Short Introduction. Oxford University Press, USA (2012)
4No, this is not a mistake! ISO is the official abbreviated name of the organization and not an
acronym in any of its three official languages: English, French and Russian.

Open Access This chapter is licensed under the terms of the Creative Commons Attribution 4.0
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