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Automating Serious Games 73.2 Application Examples 1305

the proﬁt earned and to the quality of decision-making.

Figure 73.4 describes the relationship between proﬁt

and points for one of the scenarios. For illustration pur-

poses, a locally weighted regression line is shown. The

point calculations, however, are based on linear func-

tions as described below.

The proﬁt range is divided into three segments:

upper, middle, and bottom. In each segment, a lin-

ear function describes the relationship between proﬁt

earned and points given. This function is steep in the

middle and relatively ﬂat at the tails, so the differentia-

tion among the top/low performers is less than among

those who are in the middle. The ﬁgure also shows

that submissions with the same proﬁt received different

point scores; these ﬂuctuations are due to differences in

the quality of decision-making scores.

In many ways, the simulation’s virtualworld mimics

real life. First, like in real life, there are multiple factors

with which students must interact. There are many po-

tential problems and no prepared solutions to counter

these. With the MBE Simulator software, what at ﬁrst

appears to be a very simple scenario raises many com-

plicated interactions that are not as straightforward as

the student might think. Students have to understand

and get an overview of the whole supply-chain pic-

ture, which can be quite an overwhelming experience

formostofthem.

Also, in MBA programs, there is ever-increasing

pressure on getting better grades (and later convert-

ing better grades into better jobs), thus increasing the

temptation for cheating. As a result, much copying and

information exchange goes on between current and past

students. With traditional case studies, students can eas-

ily obtain the whole discussion to see how it was led

and managed. To try to prevent this, professors have to

regularly change, revise, and update teaching materials

just toprevent cheating. However, the principalproblem

with cheating, if we put aside the ethical considerations

and the added work to prevent it, is that students learn

nothing from it.

The simulator offers a solution to this problem by

providing an ongoing measurement of how much time

each student has spent on the simulator. Furthermore,

the professor can recreate the work done by the stu-

dents and see exactly what path they took and what

decisions they made. The students are also required

to submit an executive summary that describes their

management philosophy, tactics, and decisions. This as-

signment challenges students to reveal the way in which

they managed and why they did so, just like real-life

boards of directors are required to do. In class, stu-

120000–55000 –30000 –5000 20000 45000 70000 95000

Points

Profit

160

120

Fig. 73.4 Relationship between proﬁt and points for one of the

scenarios. A LOWESS/LOESS locally weighted regression line is

shown

dents are asked why they made each decision. This is

completely different from the usual executive summary,

where only managerial slogans are used. Here the sum-

mary (10–12 pages total) provides real insights into the

company, and is a good indicator of the extent to which

the students understood the problem.

In sum, in the simulation most of the cheating-

related problems are eliminated, because it takes far

more effort to copy someone’s notes (and then answer

questions during the presentation of the results) than to

learn while running the simulator. In addition, the com-

plexity of the scenario allows for more than a single,

well-deﬁned solution. Rather, the simulation provides

an ongoing search for solutions.

Learning with the simulator is ideal because it can

be used to illustrate, for example, inventory modeling

embedded in practice. In traditional problems (at the

end of textbook chapters), one paragraph describes the

problem, which is basically a mathematical problem,

and the students simply plug in the numbers and get the

answer. In the simulator, you can actually see (through

the graphs and through the mechanism itself) the im-

pact of changing a parameter on other parameters (such

as changing the order point or order quantity). Other

teaching topics that can be illustrated by the simulator

are: forecasting, master production scheduling, MRP,

bill of material, rough cut capacity planning, purchas-

ing, and choosing suppliers. In short, you can cover

every topic included in any operational management

course.

Part G 73.2

1306 Part G Infrastructure and Service Automation

MBE Simulations has also developed and cus-

tomized some virtual worlds for various organiza-

tions, including SAP, Amdocs, Kodak, Motorola, Bank

Leomi, Biosense (Johnson and Johnson), Lucent,

KPMG, Cellcom, Tnuva, Israel Military Industries, and

Orbotech.

73.2.2 L’Oreal e-Strat

L’Oreal e-Strat, the second example, is the biggest on-

line game used by universities and corporations. During

6months (=six decision rounds), which corresponds to

3years in real time, students in teams of three assume

the responsibilities of the chief executive ofﬁce (CEO)

in a virtual cosmetics company. In order to achieve the

highest share price index, teams have to make decisions

about pricing, production volumes and capacity, logis-

tics, research and development, marketing, advertising,

and brand positioning after analyzing data and charts of

competitors and the market situation.

The students have to analyze a periodical report

after every round which contains information about rev-

enues, gains and losses, production quantity, capacity

utilization, market research studies, competitors, brand

perceptions, and benchmarking with other competitors.

Based on the information in the report, the students

have to make decisions in the following areas of the

company’s value chain: research and development, pro-

duction planning, brand management, distribution, and

corporate social responsibility. The decisions that the

students can make are limited by a budget, which is

determined by the performance of the last round.

Each area has its own additional, area-speciﬁc deci-

sions:

Research and development decisions have an im-

pact on customer satisfaction as well as on customer

demand.

In the production planning section, by changing

policies in terms of pricing, advertising, distribution or

even research and development, it is likely that demand

for certain products will vary strongly over time. High

prices for products or low prices for competing prod-

ucts normally lead to a decrease in demand. In order to

satisfy the demand of all customers and in order not to

produce products to stock, the management has to fore-

cast market demand and include it into the production

planning. If students assume too low a level of produc-

tion capacity, demand will not be met, which will have

a negative impact on the revenues of the company and

negatively affect the share price index (SPI).

In thee-Strat world, there are four distribution chan-

nels. Management has to allocate distribution and trade

marketing budget to each retail channel depending on

the shopping habits of the target group.

Among other considerations, the e-Strat structure

includes corporate social responsibility. Management

can decide if the company wants to invest in CSR

activities (e.g., water reduction, career tracking, and

kindergartens for employees). These investments will

lead to better company reputation and can have a posi-

tive impact on the SPI.

E-Strat is a complex business simulation that cov-

ers all areas of a company’s value chain. The game is

based on decision rounds that have to be submitted to

a central server that evaluates the results. However, al-

though thesimulation claimsto show allresponsibilities

of the CEO, there seems to be an emphasis on strategy,

marketing, and ﬁnance and less on operations.

73.3 Guidelines and Techniques for Serious Games

73.3.1 The Serious Game Solution

for e-Learning Characteristics

A serious game realm dictates a close to reality replica-

tion of a reduced size business that needs to be managed

by the trainee. The nature of learning business by doing

provides a unique characteristic of this virtual world.

The MERP and e-Strat solutions described above repre-

sent different approaches to serious games.

The comparison in Table 73.3 lists and explains the

differences between the two different types of serious

games: MERP and strategic business games such as

e-Strat.

MERP and e-Strat address Gee’s [73.22] ﬁve con-

ditions for learning through experience in the following

way.

73.3.2 The Virtual World

for Learning by Doing Characteristics

The main attribute of a serious game for teaching

business acumen is its holistic world view aimed at pro-

viding the learning by doing nature of gaming. As such,

the program should be able to simulate complex man-

agement situations that call for decision-making based

on detailed and transparent analysis of the situation at

Part G 73.3

Automating Serious Games 73.3 Guidelines and Techniques for Serious Games 1307

Table 73.3 MERP versus strategic business games such as e-Strat

Comparison criteria Dynamic management workshop

(MBE Simulations)

Business games (strategic or ﬁnance)

Decision type Realistic, daily-based events that affect the

organization’s results

Strategic (investments, penentration of

a new market, etc.)

Decision making Dynamic decision making. Choosing when

and which decision to make in parallel with

the evolving KPI status

Mandatory periodical set of decisions

(i.e., quarterly)

Competitive nature Teams are competing based on actual bench-

mark results. Scenarios are stochastic, reﬂect-

ing turbulent daily business life

Direct competition based on proﬁt or

share price index reﬂected. The dynamic

is limited to competition cycles only.

Learning effectiveness Reinforced learning: Repeated runs of the

same organization

The game runs once, cannot be repeated

Learning scope Aimed at improving the learner’s effective

strategy execution abilities. Managerial and

business insights built upon tactical daily ex-

perience gained: Cause and effect, system

thinking, and accepting and dealing with un-

certainty

Focusing on strategic learning

hand. Such a program should incorporate the following

approaches to learning and education:

•

A pool of live dynamic case studies. These are

descriptions and scenarios which depict certain

management situations which often occur in real

life; for example, in the simulated world of MERP

and e-Strat, the trainees are given detailed data

on the virtual manufacturing organization includ-

ing: income statement, descriptions and details

of the products and their market segmentation,

details of the operational system, characteristics

of the raw-material suppliers, cash and credit

lines available, and various policies and ad hoc

decision-making capabilities. A wide variety of or-

ganizational settings in marketing, operational, and

ﬁnance can be described by different scenarios;

for example, a scenario’s cover story may read like

this: the company appears to be well managed; it

is doing all the right things. The order book is full;

it has good customer relations in that it delivers on

time and is therefore considered to be a reliable

vendor. Thecompany hasa goodmanagement infor-

mation system which provides detailed and varied

management reports. It has an MRP scheduling and

order system. It has reliable raw material suppliers.

It has the most modern and reliable machinery in

its operations division. It has everything, and is do-

ing everything, that a good manufacturing business

should be doing. However, it is losing money.

•

It should cover a virtual-world presentation of the

entire company and its environment. When the

trainee starts the simulated dynamic case study, the

trainee should be able to watch his/her strategies

unfold. The important point to note with this type

of management simulation is that the trainee is in

control and can set the rules, i.e., he/she can deter-

mine product prices, lead-time commitments made

to customers, operational policies, inventory levels,

suppliers to buy from, and so forth. In fact, ev-

erything that the trainee would normally plan and

control in his own business is available for planning

and control in the simulation.

•

It should be equipped with modern information

technology (IT) managerial solutions. As today’s

turbulentbusinesses are relyingheavilyon advanced

integrated information systems, the trainee should

be able to use such enablers in his/her practice. This

need dictates a presentation of an integrated infor-

mation system as well as a set of key performance

indicators (KPIs) for each department of the orga-

nization. An example of such a KPI presentation is

showninFig.73.1. These IT enabler tools present

dynamic changes in the company’s situation, and

trainees should respond to these changes by execut-

ing effective strategies.

Part G 73.3

1308 Part G Infrastructure and Service Automation

Table 73.4 Five conditions for learning through experiences

MERP e-Strat

Each MERP scenario is structured around very spe-

ciﬁc goals. Some of these goals serve as order

qualiﬁers (a certain level must be reached but going

beyondthe requiredlevel doesnot provide extra value)

and some as order winners (the more, the better).

The game is structured around one goal: winning the

contest, mainly by maximizing share price index.

In workshops and lectures, the simulation runs are in-

terpreted and the lessons learned extracted.

In between the various cycles, the results of the previ-

ous round are interpreted.

Throughout each scenario, learners can rely on a wide

range of key performance indicators (KPIs) to help

them recognize and evaluate their errors.

Throughout each cycle run, learners can rely on

a wide range of management reports to help them

recognize and evaluate their errors.

This game is aimed at improving learners’ effec-

tive execution rather than strategic decision making.

Therefore, the learners have a practically unlimited

number of runs in each scenario to apply and reapply

their previous experiences.

Focusing on strategic learning, the learners have six

decision rounds which correspond to 3years in real

time. The learning is gained upon the progress of their

results impacted by competitors’ decisions and not

only on their own effective execution.

While the simulation is run individually, the learners

are assigned to groups where the members’ experi-

ences can be shared. Also, independently of group

membership, the results of all individual runs are

displayed in a Top 100 list that provides further moti-

vation and feedback.

Learners are grouped into teams of three. The teams

have to decide on pricing, production volume and

capacity, logistics, research and development, market-

ing, advertising, and brand positioning after analyzing

charts on competitors and the market situation in order

to gain the highest share price index.

•

The decision-making nature of such a solution

should mimic that of real life. At any given point

in time, a large number of potential decisions are

available to the trainee and the challenge is to de-

cide not only on the decision to make but also on

the timing of the decision (that is, when to make

a decision). This feature distinguishes MERP from

traditional business games.

•

It should reﬂect a complex integrative and dynamic

environment: This realism is enhanced by the pro-

gram’s ability to introduce contingencies, such as

the uncertainty of the market, variations in the sup-

ply chain, maintenance downtime, and machinery

and plant breakdowns, to mention but a few. The

impact of these contingencies is coupled with the

impact of the strategic decisions already taken, and

integrated with the results to date, in exactly the

same way as in real life.

•

The program should describe a generic view of

a managed environment. In order to support real

insightful learning, a serious game should provide

a virtual-world description different from informa-

tion system solutions readily available from vendors

(i.e., packages from SAP, Oracle, etc.). This will

lead the trainee into the content path of learning

rather than focusing on is it real? type questions.

73.3.3 Supporting the Learning

by Doing Characteristics

Serious games for education/training should enable

learning by doing. This need dictates the following

main characteristics:

•

The role of the manager should be realistic. In

the assignment, the trainee should take the role of

a manager who has been called in to take over

a bankrupt company. He has lots of historical data

which can be used to develop strategic plans for

improving the situation, as well as to introduce

tactical and operational control mechanisms. The

game should provide all decisions and presentations

needed for carrying out the strategy developed.

•

It should be fully interactive and integrative. The

trainee should be able to stop the simulation when-

ever he/she feels the need to review the situation and

Part G 73.3

Automating Serious Games 73.4 Emerging Trends, Open Challenges 1309

think. The trainee should be able to change the poli-

cies, rules or decisions at any time and watch the

effect. The program also allows the trainee to se-

lect andsimulate a number of special activities, such

as expediting and order consolidation. The game

should describe a whole set of cause and effect

chains for each decision made, allowing the trainee

to explore and learnabout theinterrelations between

decisions and their outcomes as well as about their

impact on other business entities.

•

Ease of learning. The game should lead the trainee

along a short, relatively simple, path. Learning

should be about content rather than about the

mechanics of managing the simulation (as often

happens with traditional games).

•

It should reinforce learning. The solution should al-

lowthe traineeto repeat the same scenarioagain and

again, trying a different decision path every time.

In other words, this sensitivity analysis allows the

trainee to learn from his/her own experience.

•

Putting into practice things learned. The game

should guide and steer trainees in the right direc-

tions needed for gaining real experience and making

them learn through these experiences.

•

Simulate events that are close to reality. The game

should describe events in business and manage-

ment that are affected by uncertainties; for example,

product cost, product price, inventory level, and

product demand by customers are all events that

are dynamically and randomly changed in the back-

ground during simulation sessions. Consequently,

these events cause varied and sometimes unex-

pected situations to which each trainee must react.

•

Provide monitoring and evaluating. The game

should track trainee decisions and operating per-

formance as well as evaluate the decisions taken.

MERP, for example, monitors and displays the

decisions made by trainees. The consequences of

these decisions (e.g., cash ﬂow, inventory levels,

and service levels) are continuously shown to group

members (running the same assignment), making

learning a shared experience. Additionally, a game

should provide ways to test the quality of the

trainee’s decisions and their effectiveness.

•

Team learning and collaboration. The game should

provide built-in platforms for chat and team com-

munication in a networked environment where

trainees can interactively communicate with each

other to share opinions and experiences about de-

tails and aspects of the simulator program in general

or about the speciﬁc program scenarios.

•

Learning from the previous experience. The game

should allow saving of simulation sessions as run

ﬁles. These run ﬁles should show the history of the

simulation sessions and should be replayed later by

the trainee and by professor. Session history exe-

cutable ﬁles contain all decisions, whether simulator

and/or trainee generated, exactly as in the originally

run simulation session, for trainees to review and

analyze separately.

73.4 Emerging Trends, Open Challenges

A signiﬁcant trend in learning is the paradigm shift in

the role of trainee (Iverson, in [73.2]), from passive

to accountable active participation. Serious games offer

a solution to support this trend.

Unliketraditional business learning approaches,like

business games based on one size ﬁts all solutions, there

is a need for tangible, content-speciﬁc business edu-

cation for motivated and engaged trainees. This need

calls for more content about the various business skill

areas and about industry-speciﬁc needs. Michael and

Chen [73.2] listed the required skills as follows: peo-

ple skills, job-speciﬁc skills, organization skills (that

is, how to utilize resources effectively, collaborate,

and focus on results, etc.), communication skills, and

strategy skills aimed at effective execution of strategy

needs.

Over time the generational structure of management

will change; the younger generation is becoming more

and more game-oriented. This new gamer generation is

more accustomed to calculated risk taking and needs

more challenging learning solutions (Beck and Wade,

in [73.2]).

Veen and Vrakking [73.23] argued that schools

of the future will have longer sessions (4 h periods)

and interdisciplinary themes rather than subject-speciﬁc

content. This trend in management learning will present

a challenge to provide more collaboration-based seri-

ous games that reﬂect and mimic the holistic realm of

a learning subject, and teachers should also be prepared

for this new environment [73.24]. Freitas [73.25]wrote

that training and education, both physically and con-

ceptually, will shift to new opportunities beyond the

Part G 73.4

1310 Part G Infrastructure and Service Automation

traditional classroom. Diverse communities with differ-

ent needs and cultures will present a new challenge for

serious games, learners and educators as well [73.26].

The last challenge in serious game learning is

the need to assess learning outcomes. Serious games

should be able to show that learning has occurred

and the extent to which the learning objectives have

been achieved [73.2]. This will elevate the character-

istics of the learning game to be directed implicitly

and explicitly toward learning goals. It will also re-

quire the development of assessment tools for serious

games.

73.5 Additional Reading

•

D. Cheek, H. Kelly: Designing an online vir-

tual world for learning and training, Fifth IEEE

Int. Conf. Wirel. Mob. Ubiquitous Technol. Educ.

(2008) pp. 208–209

•

T. Connolly, M. Stansﬁeld: Games-based e-

Learning: Implications and challenges for higher

education. In: Social Implications and Challenges

of e-Business, ed. by F. Li (IGI Global, Hershey

2007)

•

B. Sawyer, P. Smith: Serious games taxonomy, The

Serious Games Summit at the Game Developers

Conference, San Francisco (2008)

References

73.1 T. Susi, M. Johannesson, P. Backlund: Serious

Games – An Overview (2007) http://www.his.se/

upload/48173/SeriousGames_overview.pdf

(accessed June 13, 2008)

73.2 D. Michael, S. Chen: Serious Games: Games that

Educate, Train and Inform (Thomson Course Tech-

nology, Boston 2006)

73.3 P. Breslin, C. McGowan, B. Pecheux, R. Sudol: Se-

rious gaming, Health Man. Technol. 28(10), 14–17

(2007)

73.4 H. Kelly, K. Howell, E. Glinert, L. Holding, C. Swain,

A. Burrowbridge, M. Roper: How to build serious

games, Commun. ACM 50(7), 44–49 (2007)

73.5 M. Zyda: From visual simulation to virtual reality to

games, Computer 38(9), 25–32 (2005)

73.6 B. Gros: Digital games in education: The design

of games-based learning environments, J. Res.

Technol. Educ. 40(1), 23–38 (2007)

73.7 P.M. Greenﬁeld: Video games as cultural artifacts.

In: Interacting with Video, ed. by P.M. Greenﬁeld,

R.R. Cocking (Ablex, New York 1996) pp. 35–46

73.8 P. Backlund, H. Engström, M. Johannesson: Com-

puter gaming and driving education, Proc. Work-

shop Pedagogical Design of Educational Games

afﬁliated to the 14th International Conference

on Computers in Education (ICCE 2006), Beijing

(2006)

73.9 M. Koepp, R. Gunn, A. Lawrence, V. Cunning-

ham, A. Dagher, T. Jones, D. Brooks, C. Bench,

P. Grasby: Evidence for striatal dopamine release

during a video game, Nature 393(21), 266–268

(1998)

73.10 M.J. Mayo: Games for science and engineering ed-

ucation, Commun. ACM 50(7), 30–35 (2007)

73.11 K. Corti: Serious Thinking,E.learningAge,March,

20-21 (2008)

73.12 M. Zyda: Creating a science of games, Commun.

ACM 50(7), 26–29 (2007)

73.13 D.W. Shaffer, K.D. Squire, R. Halverson, J.P. Gee:

Video games and the future of learning, Phi Delta

Kappan 87(2), 104–111 (2005)

73.14 D. Salomon, D.N. Perkins, T. Globerson: Partners in

cognition: Extending human intelligent technolo-

gies, Educ. Res. 20(3), 2–9 (1991)

73.15 A. Agostino: The relevance of media as artifact:

Technology situated in context, Educ. Technol. Soc.

2(4) (1999)

73.16 D. Shaffer: Epistemic games, Innovate 1(6) (2005),

http://www.innovateonline.info/index.

php?view=article&id=79 (accessed April 18, 2008)

73.17 D.A. Schon: Educating the Reﬂective Practitioner:

Toward a New Design for Teaching and Learning in

the Professions (Jossey-Bass, San Francisco 1987)

73.18 J. Gee: Situated Language and Learning: A Critique

of Traditional Schooling (Rutledge, London 2004)

73.19 K. Squire: Game-Based Learning: Present and Fu-

ture State of the Field (Univ. Wisconsin-Madison

Press, Madison 2005)

73.20 J. Bransford, A.L. Brown, R.R. Cocking: How People

Learn: Brain, Mind, Experience and School (Na-

tional Academy, Washington 2000)

73.21 R.K. Sawyer: Analyzing collaborative discourse. In:

The Cambridge Handbook of the Learning Sciences,

ed. by R.K. Sawyer (Cambridge Univ. Press, Cam-

bridge 2006) pp. 187–204

73.22 J.P. Gee: Learning and games. In: The Ecology of

Games: Connecting Youth, Games, and Learning,

Foundation Series on Digital Media and Learning,

Part G 73

Automating Serious Games References 1311

ed. by K. Salen (MIT Press, Cambridge 2008) pp. 21–

73.23 W. Veen, B. Vrakking: Homo Zappiens: Reshaping

Learning in the Digital Age (Network Continuum,

London 2006)

73.24 K. Becker: Digital game-based learning once re-

moved: Teaching teachers, Br. J. Educ. Technol.

38(3), 478–488 (2007)

73.25 S. de Freitas: Learning in immersive worlds, A re-

view of game-based learning, http://www.jisc.ac.

uk/whatwedo/programmes/elearning_innovation/

eli_outcomes.aspx(accessed April 28, 2008)

73.26 L.Pannese,M.Carlesi:Gamesandlearningcome

together to maximise effectiveness: The challenge

of bridging the gap, Br. J. Educ. Technol. 38(3),

438–454 (2007)

Part G 73

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1313

Automation i

74. Automation in Sports and Entertainment

Peter Kopacek

A service robot has to be intelligent, mobile, and

able to cooperate with other robots and devices.

We are on the way towards multirobot systems

in which several robots, called multiagent sys-

tems (MAS), will act in a cooperative way together

a common task. One of the newest application ar-

eas of service robots and especially MAS is the ﬁeld

of entertainment, leisure, and hobby. People have

more free time, and modern information technolo-

gies lead to loneliness of humans (teleworking,

telebanking, teleshopping, etc.). Entertainment

robots are expected to be one of the real frontiers

of the next decade.

In this chapter a short description of such

robots will be given, including some application

examples. Due to the broad range of possible

applications of robots in entertainment, leisure,

and hobby, the following classiﬁcation has been

made in order to give this contribution a basic

74.1 Robots in Entertainment, Leisure,

and Hobby ...........................................1315

74.1.1 Deﬁnitions ..................................1315

74.1.2 Categories ...................................1315

74.1.3 Examples ....................................1315

74.2 Market.................................................1330

74.3 Summary and Forecast ..........................1330

74.4 Further Reading ...................................1331

References ..................................................1331

structure: robot construction sets, sports assistants,

promotion and public relations, robots in the en-

tertainment industry, personal robots, humanoid

robots, and competition robots.

As an example, robot soccer competitions will

be described in more detail. Finally an outlook on

future development trends will be given.

There are three starting points for the development of

intelligent robots (Fig.74.1):

•

Conventional stationary robots

•

Autonomous guided vehicles (AGVs)

•

Walking machines.

Stationary industrial robots equipped with external sen-

sors are used today, e.g., for assembly and disassembly

operations, fueling of cars, etc. and were the ﬁrst intel-

ligent robots.

Mobile platforms with external sensors (AGVs)

have been commercially available for some years and

cover a broad application ﬁeld. Mobile platformsare the

real roots of service robots.

Walking machines or mechanisms have been well

known for some decades. Usually they have four to six

legs (multiped) and only in some cases two legs (biped)

– from the viewpoint of control engineering walking on

twolegsis averycomplex(nonlinear) stability problem.

Biped walking machinesequipped withexternal sensors

are the basis for humanoid robots. Some prototypes of

such robots are available today.

To give an idea about further developments,

Fig.74.1 shows possible developmenttrends inrobotics.

We are now on the way from unintelligent industrial

robots via intelligent industrial robots to intelligent mo-

bile – including humanoid – robots to third-generation

advanced robotsable to interact and work symbiotically

with us.

Robots in the 21st century will be used in all areas

of modern life. The major challenges are:

•

To develop robotic systems that can sense and inter-

act usefully with humans

•

To design robotic systems able to perform complex

tasks with a high degree of autonomy.

Part G 74

1314 Part G Infrastructure and Service Automation

Advanced

industrial

robots

(Assembly)

External

sensors

Human–robot

interaction

Service

robots

(fixed base)

Service robots

(mobile

manipulators)

(Fueling)

(Fetch & carry)

Industrial

robots

(Pick & place)

Intelligent

AGVs

(Flexible

transportation)

Service

robots

(mobile platforms)

(Intell. wheelchair,

helpmate)

AGVs

ManipulationLocomotion

(Transportation)

Stability

Multiped

walking

mechanisms

Biped

walking

mechanisms

External

sensors

Networking

Humanoid

robots

Walking

mechanisms

Factory environment Public and home environment

Tele-

robotics

Fig. 74.1 From industrial to service robots (after [74.1])

In the same way that mobile phones and laptops

have changed our daily lives, robots are poised to be-

come a part of our everyday life. The robot systems of

the next decades will thus be human assistants, help-

ing people do what they want to do in a natural ad

intuitive manner. These assistantswill include:robot co-

workers in the workplace, robot assistants for service

professionals, robot companions in the home, robot ser-

vants and playmates, and robot agents for security and

space.

First generation

Industrial robot Personal robot

1960

Second generation

2000

Third generation

2010

• Manufacturing

& automation

• Robust, fast, precise

• Edutainment,

welfare, etc.

• Intelligence, human–

robot interaction (HRI),

mobility

Ubiquitous robot

• U-services

• Networked, calm,

seamless,

context-aware

Fig. 74.2 Development trends in robotics [74.2]

The role of these robots of the future could be

improved by embedding them into emerging infor-

mation technology (IT) environments characterized by

a growing spread of ubiquitous computing and commu-

nications and of ad hoc networks of sensors forming

what has been termed ambient intelligence.

Currently available robots are far from this vision

of the third generation, being able to understand their

environments, their goals, and their own capabilities or

to learn from their own experiences.

Part G 74