Robot and Robotics

Robotics and

artificial intelligence

A ROBOT IS A SOPHISTICATED MACHINE, OR A SET OF MACHINES WORKING

Together, that performs certain tasks. Some people imagine robots as having two legs, a

head, two arms with functional end effectors (hands), an artificial voice, and an electronic

brain. The technical name for a humanoid robot is android. Androids are within

the realm of technological possibility, but most robots are structurally simpler than, and

don’t look or behave like, people.

An electronic brain, also called artificial intelligence (AI), is more than mere fiction,

thanks to the explosive evolution of computer hardware and software. But even

the smartest computer these days would make a dog seem brilliant by comparison.

Some scientists think computers and robots might become as smart as, or smarter than,

human beings. Others believe the human brain and mind are more complicated than

anything that can be duplicated with electronic circuits.

Asimov’s three laws

In one of his early science-fiction stories, the famous author Isaac Asimov first mentioned

the word robotics, along with three “fundamental rules” that all robots ought

to obey:

• First Law: A robot must not injure, or allow the injury of, any human

being.

• Second Law: A robot must obey all orders from humans, except orders

that would contradict the First Law.

• Third Law: A robot must protect itself, except when to do so would contradict

the First Law or the Second Law.

Autonomous robots

A robot is autonomous if it is self-contained, housing its own computer system, and

not depending on a central computer for its commands. It gets around under its own

power, usually by rolling on wheels or by moving on two, four, or six legs.

. The more complex

the task, and the more different things a robot must do, the more autonomy it will have.

The most autonomous robots have AI. The ultimate autonomous robot will act like a living

animal or human. Such a machine has not yet been developed, and it will probably

be at least the year 2050 before this level of sophistication is reached.

Androids

An android is a robot, often very sophisticated, that takes a more or less human form.

An android usually propels itself by rolling on small wheels in its base. The technology

for fully functional arms is under development, but the software needed for their

operation has not been made cost-effective for small robots.

An android has a rotatable head equipped with position sensors. Binocular, or

stereo, vision allows the android to perceive depth, thereby locating objects anyplace

within a large room. Speech recognition and synthesis are common.

Because of their humanlike appearance, androids are ideal for use wherever there

are children. Androids, in conjunction with computer terminals, might someday replace

school teachers in some situations

Robot arms

Robot arms are technically called manipulators. Some robots, especially industrial

robots, are nothing more than sophisticated manipulators. A robot arm can be categorized

according to its geometry. Some manipulators resemble human arms. The

joints in these machines can be given names like “shoulder,” “elbow,” and “wrist.”

Other manipulators are so much different from human arms that these names don’t

make sense. An arm that employs revolute geometry is similar to a human arm, with

a “shoulder,” “elbow,” and “wrist.” An arm with cartesian geometry is far different

from a human arm, and moves along axes (x, y, z) that are best described as “upand-

down,” “right-and-left,” and “front-to-back.”

Degrees of freedom

The term degrees of freedom refers to the number of different ways in which a robot

manipulator can move. Most manipulators move in three dimensions, but often

they have more than three degrees of freedom.

“ You can use your own arm to get an idea of the degrees of freedom that a robot arm

might have. Extend your right arm straight out toward the horizon. Extend your index

finger so it is pointing. Keep your arm straight, and move it from the shoulder. You can

move in three ways. Up-and-down movement is called pitch. Movement to the right and

left is called yaw. You can rotate your whole arm as if you were using it as a screwdriver.

This motion is called roll. Your shoulder therefore has three degrees of freedom: pitch,

yaw, and roll.

Now move your arm from the elbow only. This is hard to do without also moving

your shoulder. Holding your shoulder in the same position constantly, you will see that

your elbow joint has the equivalent of pitch in your shoulder joint. But that is all. Your

elbow, therefore, has one degree of freedom.

Extend your arm toward the horizon again. Now move only your wrist. Try to keep

the arm above the wrist straight and motionless. Your wrist can bend up-and-down,

side-to-side, and it can also twist a little. Your lower arm has the same three degrees of

freedom that your shoulder has, although its roll capability is limited.

In total, your arm has seven degrees of freedom: three in the shoulder, one in the

elbow, and three in the arm below the elbow.”

You might think that a robot should never need more than three degrees of freedom.

But the extra possible motions, provided by multiple joints, give a robot arm versatility

that it could not have with only three degrees of freedom. (Just imagine how

inconvenient life would be if your elbow and wrist were locked and only your shoulder

could move.)

Degrees of rotation

The term degrees of rotation refers to the extent to which a robot joint, or a set of

robot joints, can turn clockwise or counterclockwise about a prescribed axis. Some

reference point is always used, and the angles are given in degrees with respect to

that joint.

Rotation in one direction (usually clockwise) is represented by positive angles;

rotation in the opposite direction is specified by negative angles. Thus, if angle X _

58 degrees, it refers to a rotation of 58 degrees clockwise with respect to the reference

axis. If angle Y _ 274 degrees, it refers to a rotation of 74 degrees counterclockwise.

Articulated geometry

The word articulated means “broken into sections by joints.” A robot arm with articulated

geometry bears some resemblance to the arm of a human. The versatility

is defined in terms of the number of degrees of freedom. For example, an arm might

have three degrees of freedom: base rotation (the equivalent of azimuth), elevation

angle, and reach (the equivalent of radius). If you’re a mathematician, you might recognize

this as a spherical coordinate scheme. There are several different articulated

geometries for any given number of degrees of freedom.

Cartesian coordinate geometry

Another mode of robot arm movement is known as cartesian coordinate geometry

or rectangular coordinate geometry. This term comes from the cartesian system

often used for graphing mathematical functions. The axes are always perpendicular

to each other. Variables are assigned the letters x and y in a two-dimensional cartesian

plane, or x, y, and z in cartesian three-space. The dimensions are called reach

for the x variable, elevation for the y variable, and depth for the z variable

Robotic hearing and vision

Machine hearing involves more than just the picking up of sound (done with a microphone)

and the amplification of the resulting audio signals (done with an amplifier).

A sophisticated robot can tell from which direction the sound is coming and

perhaps deduce the nature of the source: human voice, gasoline engine, fire, or barking

dog.

Binaural hearing

Even with your eyes closed, you can usually tell from which direction a sound is coming.

This is because you have binaural hearing. Sound arrives at your left ear with

a different intensity, and in a different phase, than it arrives at your right ear. Your

brain processes this information, allowing you to locate the source of the sound, with

certain limitations. If you are confused, you can turn your head until the sound direction

becomes apparent to you.

Robots can be equipped with binaural hearing. Two sound transducers are positioned,

one on either side of the robot’s head. A microprocessor compares the relative

phase and intensity of signals from the two transducers. This lets the robot determine,

within certain limitations, the direction from which sound is coming. If the robot is confused,

it can turn until the confusion is eliminated and a meaningful bearing is obtained.

If the robot can move around and take bearings from more than one position, a more accurate

determination of the source location is possible if the source is not too far away.

Hearing and AI

With the advent of microprocessors that can compare patterns, waveforms, and

huge arrays of numbers in a matter of microseconds, it is possible for a robot to determine

the nature of a sound source, as well as where it comes from. A human voice

produces one sort of waveform, a clarinet produces another, a growling bear produces

another, and shattering glass produces yet another. Thousands of different

waveforms can be stored by a robot controller and incoming sounds compared with

this repertoire. In this way, a robot can immediately tell if a particular noise is a lawn

mower going by or person shouting, an aircraft taking off or a car going down the

street.

Beyond this coarse mode of sound recognition, an advanced robot can identify a

person by analyzing the waveform of his or her voice. The machine can even decipher

commonly spoken words. This allows a robot to recognize a voice as yours or that of

some unknown person and react accordingly. For example, if you tell your personal robot

to get you a hammer and a box of nails, it can do so by recognizing the voice as yours

and the words as giving that particular command. But if a burglar comes up your walkway,

approaches your robot, and tells it to go jump in the lake, the robot can trundle off,

find you by homing in on the transponder you’re wearing for that very purpose, and let

you know that an unidentified person in your yard just told it to hydrologically dispose

of itself.

Visible-light vision

A visible-light robotic vision system must have a device for receiving incoming images.

This is usually a charge-coupled device (CCD) video camera, similar to the

type used in home video cameras. The camera receives an analog video signal. This

is processed into digital form by an analog-to-digital converter (ADC). The digital

signal is clarified by digital signal processing (DSP). The resulting data goes to the

robot controller. The moving image, received from the camera and processed by the

circuitry, contains an enormous amount of information. It’s easy to present a robot

controller with a detailed and meaningful moving image. But getting the machine’s

brain to know what’s happening, and to determine whether or not these events are

significant, is another problem altogether.

Vision and AI

There are subtle things about an image that a machine will not notice unless it has

advanced AI. How, for example, is a robot to know whether an object presents a

threat? Is that four-legged thing there a big dog, or is it a bear cub? How is a robot to

forecast the behavior of an object? Is that stationary biped a human or a mannequin?

Why is it holding a stick? Is the stick a weapon? What does the biped want to do with

the stick, if anything? The biped could be a department-store dummy with a closedup

umbrella or a baseball bat. It could be an old man with a cane. Maybe it is a hunter

with a rifle.

You can think up various images that look similar, but that have completely different

meanings. You know right away if a person is carrying a tire iron to help you fix a flat

tire, or if the person is clutching it with the intention of smashing something up. How is

a robot to determine subtle things like this from the images it sees? It is important for a

police robot or a security robot to know what constitutes a threat and what does not.

In some robot applications, it isn’t necessary for the robot to know much about

what’s happening. Simple object recognition is good enough. Industrial robots are programmed

to look for certain things, and usually they aren’t hard to identify. A bottle that

is too tall or too short, a surface that’s out of alignment, or a flaw in a piece of fabric is

easy to pick out.

Sensitivity and resolution

Sensitivity is the ability of a machine to see in dim light or to detect weak impulses

at invisible wavelengths. In some environments, high sensitivity is necessary. In others,

it is not needed and might not be wanted. A robot that works in bright sunlight

doesn’t need to be able to see well in a dark cave. A robot designed for working in

mines, pipes, or caverns must be able to see in dim light, using a system that might

be blinded by ordinary daylight.

Resolution is the extent to which a machine can differentiate between objects. The

better the resolution, the keener the vision. Human eyes have excellent resolution, but

machines can be designed with greater resolution. In general, the better the resolution,

the more confined the field of vision must be. To understand why this is true, think of a

telescope. The higher the magnification, the better the resolution (up to a certain maximum

useful magnification). Increasing the magnification reduces the angle, or field, of

vision. Zeroing in on one object or zone is done at the expense of other objects or zones.

Sensitivity and resolution are interdependent. If all other factors remain constant,

improved sensitivity causes a sacrifice in resolution. Also, the better the resolution, the

less well the vision system will function in dim light.

Invisible and passive vision

Robots have a big advantage over people when it comes to vision. Machines can see

at wavelengths to which humans are blind.

Human eyes are sensitive to electromagnetic waves whose length ranges from 390

to 750 nanometers (nm). The nanometer is a billionth (10_9) of a meter, or a millionth

of a millimeter. The longest visible wavelengths look red. As the wavelength gets

shorter, the color changes through orange, yellow, green, blue, and indigo. The shortest

waves look violet. Energy at wavelengths somewhat longer than 750 nm is infrared

(IR); energy at wavelengths somewhat shorter than 390 nm is ultraviolet (UV).

Machines, and even nonhuman living species, often do not see in this exact same

range of wavelengths. In fact, insects can see UV that we can’t and are blind to red and

orange light that we can see. (Maybe you’ve used orange “bug lights” when camping to

keep the flying pests from coming around at night or those UV devices that attract bugs

and then zap them.) A robot might be designed to see IR or UV or both, as well as (or

instead of) visible light. Video cameras can be sensitive to a range of wavelengths much

wider than the range we see.

Robots can be made to “see” in an environment that is dark and cold and that radiates

too little energy to be detected at any electromagnetic wavelength. In these cases

the robot provides its own illumination. This can be a simple lamp, a laser, an IR device,

or a UV device. Or the robot might emanate radio waves and detect the echoes; this is

radar. Some robots can navigate via ultrasonic echoes, like bats; this is sonar.

Binocular vision

Binocular machine vision is the analog of binocular human vision. It is sometimes

called stereo vision.

In humans, binocular vision allows perception of depth. With only one eye, that is,

with monocular vision, you can infer depth to a limited extent on the basis of perspective.

Almost everyone, however, has had the experience of being fooled when looking at

a scene with one eye covered or blocked. A nearby pole and a distant tower might seem

to be adjacent, when in fact they are a city block apart.

Color sensing

Robot vision systems often function only in gray scale, like old-fashioned 1950s television.

But color sensing can be added, in a manner similar to the way it is added to

television systems.

Color sensing can help a robot with AI tell what an object is. Is that horizontal surface

a floor inside a building, or is it a grassy yard? (If it is green, it’s probably a grassy

yard or maybe a playing field with artificial turf.) Sometimes, objects have regions of different

colors that have identical brightness as seen by a gray-scale system; these objects,

obviously, can be seen in more detail with a color-sensing system than with a

vision system that sees only shades of gray.

In a typical color-sensing vision system, three gray-scale cameras are used. Each

camera has a color filter in its lens. One filter is red, another is green, and another is blue.

These are the three primary colors. All possible hues, levels of brightness, and levels of

saturation are made up of these three colors in various ratios. The signals from the three

cameras are processed by a microcomputer, and the result is fed to the robot controller.

Robotic navigation

Mobile robots must get around in their environment without wasting motion, without

running into things, and without tipping over or falling down a flight of stairs.

The nature of a robotic navigation system depends on the size of the work area, the

type of robot used, and the sorts of tasks the robot is required to perform. In this section,

we’ll look at a few of the more common methods of robotic navigation.

Clinometer

A clinometer is a device for measuring the steepness of a sloping surface. Mobile robots

use clinometers to avoid inclines that might cause them to tip over or that are

too steep for them to ascend while carrying a load.

The floor in a building is almost always horizontal. Thus, its incline is zero. But

sometimes there are inclines such as ramps. A good example is the kind of ramp used

for wheelchairs, in which a very small elevation change occurs. A rolling robot can’t

climb stairs, but it might use a wheelchair ramp, provided the ramp isn’t so steep that it

would upset the robot’s balance or cause it to lose its payload.

In a clinometer, a transducer produces an electrical signal whenever the device is

tipped from the horizontal.

Edge detection

The term edge detection refers to the ability of a robotic vision system to locate

boundaries. It also refers to the robot’s knowledge of what to do with respect to

those boundaries.

A robot car, bus, or truck might use edge detection to see the edges of a road and

use the data to keep itself on the road. But it would have to stay a certain distance from

the right-hand edge of the pavement to avoid crossing into the lane of oncoming traffic

 It would have to stay off the road shoulder. It would have to tell the difference

between pavement and other surfaces, such as gravel, grass, sand, and snow. The

robot car could use beacons for this purpose, but this would require the installation of

the guidance system beforehand. That would limit the robot car to roads that are

equipped with such navigation aids.

The interior of a home contains straight-line edge boundaries of all kinds, and each

boundary represents a potential point of reference for a mobile robotic navigation system.

The controller in a personal home robot would have to be programmed to know

the difference between, say, the line where carpet ends and tile begins and the line

where a flight of stairs begins. The vertical line produced by the intersection of two

walls would present a different situation than the vertical line produced by the edge of

a doorway.

Embedded path

An embedded path is a means of guiding a robot along a specific route. This scheme

is commonly used by a mobile robot called an automated guided vehicle (AGV). A

common embedded path consists of a buried, current-carrying wire. The current in

the wire produces a magnetic field that the robot can follow. This method of guidance

has been suggested as a way to keep a car on a highway, even if the driver isn’t

paying attention. The wire needs a constant supply of electricity for this guidance

method to work. If this current is interrupted for any reason, the robot will lose

its way unless some backup navigation method (or good old human control) is

substituted.

Alternatives to wires, such as colored or reflective paints or tapes, do not need a

supply of power, and this gives them an advantage. Tape is easy to remove and put

somewhere else; this is difficult to do with paint and practically impossible with wires

embedded in concrete. However, tape will be obscured by even the lightest snowfall,

and at night, glare from oncoming headlights might be confused for reflections from the

tape.

Range sensing and plotting

Range sensing is the measurement of distances to objects in a robot’s environment

in a single dimension. Range plotting is the creation of a graph of the distance

(range) to objects, as a function of the direction in two or three dimensions.

For one-dimensional (1-D) range sensing, a signal is sent out, and the robot measures

the time it takes for the echo to come back. This signal can be sound, in which case

the device is sonar. Or it can be a radio wave; this constitutes radar. Laser beams can also

be used. Close-in, one-dimensional range sensing is known as proximity sensing.

Two-dimensional (2-D) range plotting involves mapping the distance to various objects,

as a function of their direction. The echo return time for a sonar signal, for example,

might be measured every few degrees around a complete circle, resulting in a set of

range points. A better plot would be obtained if the range were plotted every degree,

every tenth of a degree, or even every minute of arc (1/60 degree). But no matter how

detailed the direction resolution, a 2-D range plot renders only one plane, such as the

floor level in a room, or some horizontal plane above the floor. The greater the number

of echo samples in a complete circle (that is, the smaller the angle between samples),

the more detail can be resolved at a given distance from the robot, and the greater the

distance at which a given amount of detail can be resolved.

Three-dimensional (3-D) range plotting is done in spherical coordinates: azimuth

(compass bearing), elevation (degrees above the horizontal), and range

(distance). The distance must be measured for a large number of directions—

preferably at least several thousand—at all orientations. In a furnished room, a 3-

D sonar range plot would show ceiling fixtures, things on the floor, objects on top

of a desk, and other details not visible with a 2-D plot. The greater the number of

echo samples in a complete sphere surrounding the robot, the more detail can be

resolved at a given distance, and the greater the range at which a given amount of

detail can be resolved.

Epipolar navigation

Epipolar navigation is a means by which a machine can locate objects in three-dimensional

space. It can also navigate, and figure out its own position and path.

Epipolar navigation works by evaluating the way an image changes as the viewer

moves. The human eyes and brain do this without having to think, although they are

not very precise. Robot vision systems, along with AI, can do it with extreme

precision.

Imagine you’re piloting an airplane over the ocean. The only land you see is a

small island. You have an excellent map that shows the location, size, and shape of

this island . For instrumentation, you have only a computer, a video cam-

era, and AI software. You can figure out your coordinates and altitude, using only

these devices, by letting the computer work with the image of the island. As you fly

along, you aim the camera at the island and keep it there. The computer sees an image

that constantly changes shape. The computer has the map data, so it knows the

true size, shape, and location of the island. The computer compares the shape/size of

the image it sees, from the vantage point of the aircraft, with the actual shape/size of

the island, which it knows from the map data. From this alone, it can determine your

altitude, your speed relative to the surface, your exact latitude, and your exact longitude.

There is a one-to-one correspondence between all points within sight of the

island and the size/shape of the island’s image.

Epipolar navigation works on any scale, for any speed. It is a method by which robots

can find their way without triangulation, direction finding, beacons, sonar, or radar.

It is only necessary that the robot have a computer map of its environment and that

viewing conditions be satisfactory.

Telepresence

Telepresence is a refined, advanced form of robot remote control. The robot operator

gets a sense of being “on location,” even if the remotely controlled machine, or

telechir, and the operator are miles apart. Control and feedback are done by means

of telemetry sent over wires, optical fibers, or radio.

What it’s like

What would it be like to operate a telechir? Here is a possible scenario. The robot is

autonomous and has a humanoid form. The control station consists of a suit that you

wear or a chair in which you sit with various manipulators and displays. Sensors can

give you feelings of pressure, sight, and sound.

You wear a helmet with a viewing screen that shows whatever the robot camera

sees. When your head turns, the robot head, with its vision system, follows, so you see

an image that changes as you turn your head, as if you were in a space suit or diving suit

at the location of the robot. Binocular robot vision provides a sense of depth. Binaural

robot hearing allows you to perceive sounds. Special vision modes let you see UV or IR;

special hearing modes let you hear ultrasound or infrasound.

Robot propulsion can be carried out by means of a track drive, a wheel drive, or robot

legs. If the propulsion uses legs, you propel the robot by walking around a room.

Otherwise you sit in a chair and drive the robot like a car. The telechir has two arms,

each with grippers resembling human hands. When you want to pick something up, you

go through the motions. Back-pressure sensors and position sensors let you feel what’s

going on. If an object weighs 10 pounds, it will feel as if it weighs 10 pounds. But it will

be as if you’re wearing thick gloves; you won’t be able to feel texture. You might throw

a switch, and something that weighs 10 pounds feels as if it only weighs one pound. This

might be called “strength _ 10” mode. If you switch to “strength _ 100” mode, a 100-

pound object seems to weigh 1 pound.

Applications

You can certainly think of many different uses for a telepresence system. Some applications

are

• Working in extreme heat or cold

• Working under high pressure, such as on the sea floor

• Working in a vacuum, such as in space

• Working where there is dangerous radiation

• Disarming bombs

• Handling toxic substances

• Police robotics

• Robot soldier

• Neurosurgery

Of course, the robot must be able to survive conditions at its location. Also, it must

have some way to recover if it falls or gets knocked over.

NOTE: Extracted and modified from the book: “Teach Yourself Electric and Electronics 3rd edition - (Malestrom)”