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Sensors and Sensor Networks 20.2 Sensor Networks 345

Middleware

(MidFSN)

Resource

management

QoS control

services

Base station

Photoelectronic

sensor

Inductive sensor

Fig. 20.10 Wireless microsensor network system with two different types of microsensor nodes in an industrial automa-

tion application (after [20.43])

architectures, researchers [20.43] have recently been

developing a middleware for facility sensor network

(MidFSN) whose layered structure is classiﬁed into

three layers as depicted in Fig.20.9.

20.2.6 Sensor Network Applications

Distributed microsensor networks have mostly been ap-

plied to military applications. However, a recent trend

in sensor networks has been to apply the technology to

various industrial applications. Figure 20.10 illustrates

a networked sensor application used in factory automa-

tion. Environment applications such as examination of

ﬂowing water and detection of air contaminants require

ﬂexible and dynamic topology for the sensor network.

Biomedical applications of collecting information from

internal human body are based on bio/nanotechnology.

For these applications, geometrical and dynamical char-

acteristics of the system must be considered at the

design step of network architecture [20.44]. It is also

essential to use fault-tolerant network protocols to ag-

gregate very weak signals without losing any critical

signals. Speciﬁcally designed sensor network systems

can also be applicable for intelligent transportation

systems, monitoring material ﬂow, and home/ofﬁce net-

work systems.

Tachometer

GPS

Transponder

Accelerometer

Gyroscope

Kalman filter

Doppler

Fig. 20.11 Networked sensors for train tracking and tracing (global

positioning system (GPS))

Public transportation systems are another exam-

ple of sensor network applications. Sensor networks

have been successfully implemented into highway sys-

tems for vehicle and trafﬁc monitoring, providing key

technology for intelligent transportation systems (ITS).

Recently various networked sensors have been applied

to railway system to monitor the location of rolling

stocks and detect objects or obstacles on the rail in ad-

vance (Fig.20.11). Networked sensors can cover a wide

monitoring area and deliver more accurate informa-

tion.

Part C 20.2

346 Part C Automation Design: Theory, Elements, and Methods

20.3 Emerging Trends

Energy-conserving microsensor network protocols have

drawn great attention over the years. Other important

metrics such as latency, scalability, and connectivity

have also been deeply studied recently. However, it

should be realized that there are still emerging research

issues in sensor network systems. Although current

wireless microsensor network research is moving for-

ward to more practical application areas, emerging

research on the following new topics should be further

examined and will surface.

20.3.1 Heterogeneous Sensors

and Applications

In many networked sensor applications and their per-

formance evaluation, homogeneous or identical sensors

were most commonly considered; therefore network

performance was mainly determined by the geometri-

cal distances between sensors and the remaining energy

of each sensor. In practical applications other fac-

tors can also inﬂuence coverage, such as obstacles,

environmental conditions, and noise. In addition to non-

homogeneous sensors, other sensor models can deal

with nonisotropic sensor sensitivities, where sensors

have different sensitivities in different directions.

The integration of multiple types of sensors such

as seismic, acoustic, and optical sensors in a speciﬁc

network platform and the study of the overall cov-

erage of the system also present several interesting

challenges. In addition, when sensor nodes should be

shared by multiple applications with differing goals,

protocols must efﬁciently serve multiple applications si-

multaneously. Therefore, for heterogeneous sensors and

their network application development, several research

questions should be considered:

1. How should resources be utilized optimally in het-

erogeneous sensor networks?

2. How should heterogeneous data be handled efﬁ-

ciently?

3. How much and what type of data should be pro-

cessed to meet quality-of-service (QoS) goals while

minimizing energy usage?

20.3.2 Security

Another emerging issue in wireless sensor networks

is related to network security. Since the sensor net-

work may operate in a hostile environment, security

needs to be built into the network design and not as

an afterthought. That is, network techniques to pro-

vide low-latency, survivable, and secure networks are

required.

In general, low probability of communication de-

tection is needed for networks because sensors are

envisioned for use behind enemy lines. For the same

reasons, the networkshould beprotected again intrusion

and spooﬁng. For the network security, some research

questions should be examined:

1. How much and what type of security is really

needed?

2. How can data be authenticated?

3. How can misbehaving nodes be prevented from pro-

viding false data?

4. Can energy and security be traded-off such that the

level of network security can be easily adapted?

20.3.3 Appropriate Quality-of-Service (QoS)

Model

Research in QoS has received considerable attention

over the years. QoS has to be supported at media ac-

cess control (MAC), routing, and transport layers. Most

existing ad hoc routing protocols do not support QoS.

The routing metric used in current work still refers to

the shortest pathor minimumhop. However, bandwidth,

delay, jitter, and packet loss (reliability or data deliv-

ery ratio) are other important QoS parameters. Hence,

mechanisms of current ad hoc routing protocols should

allow for route selection based on both QoS require-

ments and QoS availability. In addition to establishing

QoS routes, QoS assurance during route reconﬁguration

has to be supported too. QoS considerations need to be

made to ensure that end-to-end QoS requirements con-

tinue to be supported. Hence, there is still signiﬁcant

room for research in this area.

20.3.4 Integration with Other Networks

In the near future, sensor networks may interface with

other networks, such as a Wi-Fi network, a cellular

network, or the Internet. Therefore, to ﬁnd the best

way to interface these networks will be a big issue.

Sensor network protocols should support (or at least

not compete with) the protocols of the other networks;

otherwise sensors could have dual network interface

capabilities.

Part C 20.3

Sensors and Sensor Networks References 347

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Part C 20

349

Industrial Int

21. Industrial Intelligent Robots

Yoshiharu Inaba, Shinsuke Sakakibara

It has been believed for a long time since the

birth of the industrial robot that the only task it

could perform was to play back simple motions

that had been taught in advance. At the beginning

of the 21st century, the industrial robot was born

again as the industrial intelligent robot, which

performs highly complicated tasks like skilled

workers on a production site, mainly due to the

rapid advancement in vision and force sensors.

The industrial intelligent robot has recently been

a key technology to solve issues that today’s

manufacturing industry is faced with, including

the decreasing number of skilled workers and

demands for reducing manufacturing costs and

delivery time. In this chapter, the latest technology

trends in its element technologies such as vision

andforcesensorsareintroducedwithsomeof

its applications such as the robot cell, which

has succeeded in drastically reducing machining

costs.

21.1 Current Status

of the Industrial Robot Market .............. 349

21.2 Background of the Emergence

of Intelligent Robots............................. 350

21.3 Intelligent Robots................................. 352

21.3.1 Mechanical Structure .................... 352

21.3.2 Control System ............................. 352

21.3.3 Vision Sensors.............................. 352

21.3.4 Force Sensors............................... 355

21.3.5 Control Functions ......................... 356

21.3.6 Ofﬂine Programming System.......... 357

21.3.7 Real-Time Supervisory

and Control System ...................... 358

21.4 Application of Intelligent Robots ........... 359

21.4.1 High-Speed Handling Robot .......... 359

21.4.2 Machining Robot Cell – Integration

of Intelligent Robots

and Machine Tools ....................... 360

21.4.3 Assembly Robot Cell...................... 361

21.5 Guidelines

for Installing Intelligent Robots............. 362

21.5.1 Clariﬁcation of the Range of

Automation by Intelligent Robots... 362

21.5.2 Suppression of Initial Capital

Investment Expense ..................... 362

21.6 Mobile Robots ...................................... 362

21.7 Conclusion ........................................... 363

21.8 Further Reading ................................... 363

References .................................................. 363

21.1 Current Status of the Industrial Robot Market

Industrial robots are now taking active part in vari-

ous ﬁelds, including automotive and general industries.

Today, industrial robots are used in many industries

in many countries. The operational stock of industrial

robots in major industrialized countries is shown in Ta-

ble 21.1. Current industrial robots are typically used in

such applications as spot welding, arc welding, spray

painting, and material handling, as shown in Figs. 21.1–

21.4, respectively.

Devol started the history of the industrial robot by

ﬁling the patent of its basic idea in 1954. A teaching-

playback type industrial robot was delivered as a prod-

uct for the ﬁrst time in the USA in 1961. The robot

featured two basic operations, teaching and playback,

which are adopted in almostall robots oncurrent factory

ﬂoors.

Part C 21

350 Part C Automation Design: Theory, Elements, and Methods

Table 21.1 Shipments and operational stock of multipurpose industrial robots in 2005 and 2006 and forecasts for 2007–2010.

Number of units (source: World Robotics 2007)

Yearly installations Operationalstockat year-end

Country

2005 2006 2007 2010 2005 2006 2007 2010

America 21986 17 910 21 400 24 400 143 634 154 680 167100 209000

North America

(Canada, Mexico, USA) 21 567 17 417 20500 23000 139984 150 725 162 400 200 900

Central

and South America 419 493 900 1400 3650 3955 4700 8100

Asia/Australia 76 047 61748 66000 75000 481652 479027 500 500 579 900

China 4461 5770 6600 7900 11557 17327 23900 47 000

India 450 836 1600 4500 1069 1905 3500 14 100

Japan 50 501 37393 39900 42300 373481 351658 355 000 362900

Republic of Korea 13 005 10 756 10700 11800 61576 68420 73600 94000

Taiwan,

Province of China 4096 4307 15464 19204

Thailand 1458 1102 2472 3574

Other Asia 1163 812 11095 11385

Australia/New Zealand 913 772 4938 5554

Europe 28 432 31 536 35000 39000 296 918 315 624 329 800 380 000

Austria 485 498 4148 4382

Benelux 1097 1459 9362 10128

Denmark 354 417 2661 3013

Finland 556 321 4159 4349

France 3077 3071 3300 3200 30236 32 110 34 000 38800

Germany 10075 11425 12700 13000 126 294 132 594 137900 147400

Italy 5425 6259 6900 6400 56198 60049 63800 72 000

Norway 115 181 811 960

Portugal 144 268 1542 1710

Spain 2709 2409 24141 26008

Sweden 939 865 8028 8245

Switzerland 442 458 3732 3940

Turkey 207 368 403 771

United Kingdom 1363 1220 1000 800 14 948 15082 15300 13800

Central/Eastern

European countries 1287 1322 9446 10781

other Europe 157 995 809 1502

Africa 204 426 700 900 634 1060 1700 4400

Total 126669 112 203 123 100 139 300 922838 950974 999100 1173300

Source: IFR, national robot associations and UNECE (up to 2004)

21.2 Background of the Emergence of Intelligent Robots

The use of industrial robots on the production site

started a rapid expansion in the 1980s because it came

to be known that they had the possibility of improving

productivity and making the quality of products stable.

However, this led to a situation where, for example,

dedicated equipmenthad to be prepared tosupply work-

Part C 21.2

Industrial Intelligent Robots 21.2 Background of the Emergence of Intelligent Robots 351

Fig. 21.1 Spot welding

Fig. 21.2 Arc welding

pieces to a robot. Also, human operators had to prepare

workpieces in alignment for the dedicated equipment

before the robot loaded a workpiece to such a machine

tool as a lathe. In 2001, the industrial intelligent robots

(hereafter intelligent robots) appeared on the industrial

scene mainly to automate loading workpieces to the

ﬁxtures of machine tools such as machining centers.

Fig. 21.3 Spray painting

Fig. 21.4 Material handling

Before discussing speciﬁcs, it is necessary to look

back at the history of machining process automa-

tion. The ﬁrst private sector numerical control (NC)

was developed in the 1950s, followed by the dramatic

enhancement of the NC machine tool market. The ma-

chining itself was almost completely automated by the

NC; however, loading and unloading of workpieces

to and from machine tools were still done by human

operators even in the 1990s. The intelligent robot ap-

peared in 2001 for the ﬁrst time. The term intelligent

robot does not mean a humanoid robot that walks and

talks like a human being, but rather one that performs

highly complicated tasks like a skilled worker on the

Part C 21.2

352 Part C Automation Design: Theory, Elements, and Methods

J3 axis

J2 axis

J3 motor

J1 motor

J2 motor

J1 base

J4 axis

J6 axis

J5 axis

J1 axis

J2 axis

motor

J2 arm

Reducer

J4, J5, J6 motor

Base

Wrist

flange

Fig. 21.5 Mechanical structure of an industrial robot

production site by utilizing vision sensors and force

sensors. It has ﬁrst enabled the automatic precision

loading of workpieces to the ﬁxture of the machin-

ing center and eliminated the need for dedicated parts

supply equipment, as the robot picks up workpieces

one by one using its vision sensor once the work-

pieces are delivered to a basket near the robot. This

also eliminated a burdensome process imposed upon

human operators; that of arraying workpieces for the

dedicated equipment. In addition, the automation of

several tasks, which follow the machining, such as

deburring, most of which had been difﬁcult for con-

ventional robots, was also realized by the intelligent

robot.

Thus, intelligent robots have recently been increas-

ingly introduced in production, mainly due to their high

potential for enhancing global competitiveness as a key

technology to solve issues that today’s manufacturing

industry is faced with, including the decreasing number

of skilled workers and demands for reducing manu-

facturing costs and delivery time. In this regard, the

rapid advancement in vision and force sensors and of-

ﬂine programming, and the element technologies for

intelligent robots support the trend of robotic automa-

tion.

21.3 Intelligent Robots

21.3.1 Mechanical Structure

Figure 21.5 shows a typical conﬁguration of a vertical

articulated type six-axis robot. There are no big differ-

ences between the mechanical structure of an intelligent

robot and that of a conventional robot. It comprises sev-

eral servomotors, reducers, bearings, arm castings, etc.

Motion control

CPU SRAM

Servo

Processor

Servo

motor

Force

sensor

Vision

sensor Teach pendant

6-axis

amplifier

DRAM

Flash

ROM

CPU

USB

RS232C

RS422

ethernet

I/O

unit

Communication

control

Fig. 21.6 Control system

21.3.2 Control System

AsshowninFig.21.6, the sensor interface, which en-

ables the connection of sensors such as vision sensors

and/or force sensors to the controller, features the con-

trol system of an intelligent robot compared to that of

a conventional robot. High-speed microprocessors and

communication interface also features it. Operators of

intelligent robots make robot motion programs by oper-

ating the teach pendant shown in Fig.21.7 and moving

the robot arm step by step.

The servo control of intelligent robots is similar to

that of conventional robots as shown in Fig.21.8. The

performance of several servo control functions of intel-

ligent robots, suchas theinterpolation period, are highly

enhanced compared with that of conventional robots.

21.3.3 Vision Sensors

Twotypes ofvision sensors areoften usedon the factory

ﬂoor: two-dimensional (2-D) and three-dimensional

(3-D) vision sensors. The 2-D vision sensors acquire

two-dimensional images of an object by irradiating nat-

ural light or artiﬁcial light on the object and taking the

image of the reﬂected light with a CCD or other type of

camera. This enables obtaining a two-dimensional po-

Part C 21.3

Industrial Intelligent Robots 21.3 Intelligent Robots 353

Fig. 21.7 Teach pendant

sition and rotation angle of the object. Recently, 2-D

vision sensors have been made useful under the severe

production environment at the factory ﬂoor due to en-

hanced tolerance to change in brightness and image

degradation based on improved processing algorithms

and increased processing speed. Figure 21.9 shows

Target

position

Velocity Phase current

Current

position

Motion

command

Position

control

Velocity

control

Current

control

Power

amp

Servo

motor

Current feedback

Pulse

coder

Phase information

Velocity feedback

Position feedback

Fig. 21.8 Servo control

Rotation

Original Concealment

• Robust against changes in captured images

• No need for parameter tuning leads to simple teaching

Brightness

change (totally)

Brightness

change (partially)

Out of focus

Resizing

Poor quality

CCD camera

Fig. 21.9 Images captured by 2-D vision sensor

several images captured and processed by 2-D vision

sensors.

There are two major methods for 3-D vision sen-

sors, the structured light method and the stereo method.

Of these, the structured light method irradiates the

structured light such as slit light or pattern light on

an object, and takes images of the reﬂected light by

a CCD or other type of camera to obtain images of

the object. The 3-D position and posture of the object

are calculated with high accuracy from these images.

Additional information on sensors is also provided in

Chap.20.

Part C 21.3

354 Part C Automation Design: Theory, Elements, and Methods

Object

CCD

camera

Laser slit light

projector

3-D vision

sensor

Fig. 21.10 Bin picking

Figure 21.10 shows the bin-picking function real-

ized by using the 3-D vision sensor with the structured

light method. The robot can pick up workpieces one by

one from those randomly piled in a basket using the 3-D

vision sensor.

With respect to the economic effect of the 3-D

vision sensor, this reduces the capital investment ex-

pense by simplifying the peripheral equipment such as

the workpiece feeder, and relieves the operator from

the daily burden of arraying workpieces as shown in

Fig.21.11.

Some of the vision sensors have control units

built into the robot control system, which signiﬁcantly

enhances the reliability in use in severe production en-

vironments.

The following section explains the typical se-

quences needed to achieve the bin-picking function.

3-D vision

sensor

Material delivery

Need for peripheral equipment

such as workpiece feeder and

arrangement of workpieces on

it by human operator

No need for peripheral

equipment such as workpiece

feeder and arrangement of

workpieces on it by human

operator

Conventional robot

Intelligent robot

with 3-D vision sensor

Fig. 21.11 Economic effect of the vision sensor

Overall Search for the Workpieces

The image of piled workpieces is taken from upper side

by the far-sighted-eye, and the pretaught model is de-

tected from within the image. This far-sighted-eye is

calibrated in advance. Then the approximate position

of each detected workpiece is acquired. The controller

moves the hand-eye (a vision sensor mounted on the

hand) near the work-piece based upon this 3-D position,

and measurement instructions are issued to the sensor

controller, then the trial measurement explained below

is executed.

Trial Measurement

The purpose of the trial measurement is to acquire both

the 3-D position and posture of the workpiece speci-

ﬁed by the overall search. This trial measurement uses

the hand-eye and its measurement position is calcu-

lated with the approximate position of the workpiece

obtained from the overall search.

In the trial measurement the 3-D position and pos-

ture of the workpiece are obtained by projecting the

structured light by the hand-eye sensor. Because the ap-

proximate position of the workpiece obtained from the

overall search does not include the posture of the work-

piece, the trialmeasurement positionand postureare not

always appropriate to the workpiece. This may cause

unsuitable accuracy for workpiece handling. To com-

pensate this condition, ﬁne measurement is performed

in the next step.

Fine Measurement

The hand-eye is able to come closer to the workpiece

after the overall search for the bin and the trialmeasure-

ment; thus it becomes very easy to measure the position

and posture of the workpiece accurately.

Figure 21.12 shows accuracy improvement in mea-

suring the position and posture of workpieces by

Overall search

(x, y, z, r)

Trial measurement

(x, y, z, w, p, r)

Fine measurement

(x, y, z, w, p, r)

Measurement error

Sensing step

Fig. 21.12 Accuracy improvement by repetitive measure-

ment

Part C 21.3