Automation of robotic assembly processes on the basis, MAYER 2011.pdf

ASSEMBLY

Automation of robotic assembly processes on the basisof an architecture of human cognition

Marcel Ph. Mayer Christopher M. Schlick

Daniel Ewert Daniel Behnen Sinem Kuz

Barbara Odenthal Bernhard Kausch

Received: 16 February 2011 / Accepted: 1 April 2011 / Published online: 21 April 2011

German Academic Society for Production Engineering (WGP) 2011

Abstract A novel concept to cognitive automation of

robotic assembly processes is introduced. An experimental

assembly cell with two robots was designed to verify and

validate the concept. The cells numerical controltermed

a cognitive control unit (CCU)is able to simulate human

information processing at a rule-based level of cognitive

control. To enable the CCU to work on a large range of

assembly tasks expected of a human operator, the cognitive

architecture SOAR is used. On the basis of a self-developed

set of production rules within the knowledge base, the CCU

can plan assembly processes autonomously and react to ad-

hoc changes in assembly sequences effectively. Extensive

simulation studies have shown that cognitive automation

based on SOAR is especially suitable for random parts

supply, which reduces planning effort in logistics. Con-

versely, a disproportional increase in processing time was

observed for deterministic parts supply, especially for

assemblies containing large numbers of identical parts.

Keywords Cognitive automation SOAR Assembly Joint cognitive systems

1 Introduction

In high-wage countries many manufacturing systems are

highly automated. The main aim of automation is usually to

increase productivity and reduce personnel expenditures.

However, it is well known that highly automated systems are

investment-intensive and often generate a non-negligible

organizational overhead. Although this overhead is man-

datory for manufacturing planning, numerical control pro-

gramming and system maintenance, it does not directly add

value to the product to be manufactured. Highly automated

manufacturing systems therefore tend to be neither efficient

enough for small lot production (ideally one piece) nor

flexible enough to handle products to be manufactured in a

large number of variants. Despite the popularity of strategies

for improving manufacturing competitiveness like agile

manufacturing [1] that consider humans to be the most

valuable factors, one must conclude that especially in

high-wage countries the level of automation of many pro-

duction systems has already been taken far without paying

sufficient attention to the specific knowledge, skills and

abilities of the human operator.

According to the law of diminishing returns that kind of

naive increase in automation will likely not lead to a sig-

nificant increase in productivity but can also have adverse

effects. According to Kinkel et al. [2] the amount of

process errors is on average significantly reduced by

automation, but the severity of potential consequences of a

single error increases disproportionately. These ironies of

automation which were identified by Lisanne Bainbridge

as early as 1987 can be considered a vicious circle [3],

where a function that was allocated to a human operator

due to poor human reliability is automated. This automa-

tion results in higher function complexity, ultimately

increasing the cognitive loads of the human operator for

M. Ph. Mayer (&) C. M. Schlick S. Kuz B. Odenthal B. Kausch

Institute of Industrial Engineering and Ergonomics,

RWTH Aachen University, Aachen, Germany

e-mail: [email protected]

D. Ewert

Institute of Information Management in Mechanical

Engineering, RWTH Aachen University, Aachen, Germany

D. Behnen

Laboratory for Machine Tools and Production Engineering,

RWTH Aachen University, Aachen, Germany

123

Prod. Eng. Res. Devel. (2011) 5:423431

DOI 10.1007/s11740-011-0316-z

planning, teaching and monitoring, and hence leading to a

more error-prone system. To reduce the error potential one

could again extend automation and reinforce the vicious

circle. During the first iteration it is quite likely that the

overall performance of an automated system will increase,

but the potential risk taken is often severely underesti-

mated. Additional iterations usually deteriorate perfor-

mance and lead to poor system robustness.

The novel concept of cognitive automation by means of

simulation of human cognition aims at breaking this

vicious circle. Based on simulated cognitive functions,

technical systems shall not only be able to (semi-) auton-

omously carry out manufacturing planning, adapt to

changing supply conditions and be able to learn from

experience but also to simulate goal-directed human

behavior and therefore significantly increase the confor-

mity with operator expectations. Clearly, knowledge-based

behavior in the true sense of Rasmussen [4] cannot be

modeled and simulated, and therefore the experienced

machining operator plays a key architectural role as a

competent problem solver in unstable and non-predictable

situations.

2 Experimental assembly cell

One of todays challenges in manufacturing is the

increasing complexity of assembly processes due to an

increasing number of products that have to be assembled in

a large variety in production space [5]. Whereas in con-

ventional automation each additional product or variant

significantly increases the organizational overhead, cogni-

tively automated assembly cells are theoretically able to

autonomously plan, execute and replan the expected tasks

on the basis of a digital model of the product to be

assembled in conjunction with a set of production rules. No

explicit knowledge on how to solve the assembly problem

is needed. Therefore, these systems allow for flexible, cost-

effective and safe assembly.

Due to their design for assembly, many of todays

industrially processed components in mass and medium lot

size production are purposefully constrained so that their

assembly is only possible in a particular sequence or only a

few procedural variations are allowed (see [6]). The

assembly of these components is too simple to fully dem-

onstrate the flexibility and effectiveness of cognitive

automation. To fully develop and validate the novel con-

cept, mountable assemblies were chosen that can be gen-

erated in an almost unlimited number of variants in small

series production. One of the requirements for the build-

ing blocks is that they allow arbitrary configuration and

are completely interchangeable. LEGO building bricks,

from the Danish company of the same name, fulfill this

requirement and were therefore used for system design and

evaluation. Unlike complex free forming components (e.g.

interior elements in an automobile), the bricks are also easy

to describe mathematically. Nevertheless they allow for

very complex work processes because of the huge number

of permutations of assembly steps. This is easily shown by

a simple example: Building a small pyramid of only five

LEGO bricks with a foundation of two by two bricks can

be done using 24 different assembly sequences.

In order to study cognitive automation, an experimental

assembly cell was designed and a manufacturing scenario

was developed [7]. The scenario is as follows: An engineer

has designed a mechanical part of medium complexity with

a CAD system. The part can contain an arbitrary number of

bricks. The task for the assembly cells cognitive control

unit (CCU) is to autonomously develop and execute a time

and energy efficient assembly sequence on the basis of the

CAD model using the available technical resources in

terms of robots, manipulators, grippers and clamping

devices, as well as supplied bricks, etc. The supply of

bricks can change dynamically.

In our assembly scenario (see Fig. 1), two robots carry

out a predefined repertoire of coordinated pick and place

operations. One robot is stationary (robot 1), the other

robot sits on a linear track (robot 2). A conveyor system

equipped with four individually controlled transfer lines,

pneumatic track switches and light barriers completes the

experimental system. The transfer lines are arranged so that

the parts can cycle around the working area. First, the

stationary robot grasps the bricks from a pallet and puts

them on a conveyor belt. The second robot, which is

waiting on the linear track for the part, has to identify the

brick, i.e. match it to a known library of bricks with respect

to color and shape. If the brick is included in the final state

of the product to be assembled, the robot will pick it from

the conveyor (which in a later step will comprise the task of

tracking the unknown position and synchronizing the robot

to a running track) and will put it on the working area

either at the corresponding position in the assembly or in a

buffer area for further processing. Otherwise, the brick can

keep circulating on the conveyor belt to reappear later or to

be removed.

3 Simulation of human cognition

An important foundation of cognitive automation is a

suitable simulation model of human cognition. Such a

model is also termed a cognitive architecture. In order to

simulate cognitive functions in a robotic assembly cell,

distinct criteria must be met.

When a function that was allocated to a human operator

has to be automated due to frequent human errors, the

424 Prod. Eng. Res. Devel. (2011) 5:423431

123

reliability of the automated function clearly is the most

important technical criterion. We distinguish between two

aspects of reliability: reliability of the execution of the

assembly processes and reliability of the cognitive simu-

lation model controlling the process. Concerning the for-

mer aspect, we do not aim at high-fidelity modeling of

human cognition including (solely from a technical point of

view) inherent weaknesses like oblivion or decision bias,

but rather want to plan and execute predictable processes

on the basis of robust symbol processing. Hence, when

accessing knowledge in the artificial memory, access

should be unlimited, so that even rarely used knowledge

can be retrieved quickly and will not be forgotten.

Concerning the latter aspect of reliability we regard the

level of maturity of a cognitive architecture as an important

criterion. Even though no absolute measure is known for

the level of maturity of such a symbolic processor, the

amount of applications, the existence of a large and active

user community and the time the architecture has been

under continuous development are all taken as sub-indi-

cators. Moreover, since the automated assembly cell should

be controlled directly via the cognitive simulation model,

another criterion is the availability of suitable interfaces for

sensors and actuators.

There are many cognitive simulation models that can be

used to automate assembly processes. A systematic review

was carried out by Chong et al. [8]. The most popular are

ACT-R [9], ICARUS [10] and SOAR [11].

In the framework of a robotized assembly cell, SOAR

was chosen as a suitable simulation model because it sat-

isfies most of the aforementioned criteria. The design of the

CCU based on SOAR as well as selected simulation results

will be presented in Sects. 4 and 5.

There are applications for SOAR in other domains. In

the military domain, TACAIR-SOAR is used for training

[12]. The system is capable of executing most of the air-

borne missions that the U.S. military flies in fixed-wing

aircraft. A speech-enabled agent is used for indirect fire

training for a Forward Observer by providing fire direction

center support using the SOARSpeak voice interface [13].

An unmanned air vehicle controlled onboard by SOAR was

developed and tested by Putzer [14]. A detailed overview

of using SOAR for the control of unmanned vehicles can

be found in Onken and Schulte [3].

In the field of mobile robotics, a gait control system

based on SOAR was developed for a six-legged robot that

is able to move on unlevel terrain, avoid obstacles and walk

to a pre-specified GPS location [15].

However, the only application of SOAR that can be

related to manufacturing systems is the system called

ROBO-SOAR [16]. It is able to solve the three blocks

problem with outside guidance from a human operator.

The system incorporates camera surveillance and a robot

performing pick-and-place operations. No explicit knowl-

edge on how to solve the problem has to be input to the

system beforehand. This also holds true for the self-

developed CCU, which will be presented in the next

section.

4 Architecture of cognitive control unit

Cognitive systems for the automation of production pro-

cesses have to meet many functional and non-functional

requirements [17] through the design of the software

architecture. The system has to work on different levels of

Fig. 1 Design of theprototypical assembly cell [7]

Prod. Eng. Res. Devel. (2011) 5:423431 425

123

abstraction. This means, for instance, that the reasoning

mechanism cannot work on the raw sensor readings.

Instead an intermediate software component is required to

fuse and aggregate the sensor data. To meet the require-

ments, a multilayer software architecture [18] was devel-

oped, as depicted in Fig. 2.

The software architecture is separated into four layers

which incorporate the different mechanisms needed to

simulate human cognition. The presentation layer includes

the humanmachine interface and an interface for the

modification of the knowledge base. The planning layer is

the deliberative layer in which the actual decision for the

next action in the assembly process is made. The coordi-

nation layer provides services to the planning layer that can

be invoked by the latter to start action execution. The

reactive layer is responsible for a low response time reac-

tion of the whole system in case of an emergency situation.

The knowledge module contains the necessary domain

knowledge of the system in terms of production rules.

At the beginning the human operator assigns the desired

goal g* to the CCU via the presentation layer. The desired

goal is compiled and enriched with additional assembly

information, which will be discussed in more detail in the

following section. It is then transferred to the planning

layer where the reasoning component derives the next

action u* based on the actual environmental state y* and

the desired goal g*. The actual environmental state is

estimated on the basis of sensor readings from a technical

application system (TAS). In the coordination layer the raw

sensor readings y are fused and aggregated into an envi-

ronmental state y*. Hence, all decisions in the planning

layer are based on the environmental state y* at a given

time. The decision process must therefore be short, because

the state of the TAS may have changed significantly. The

next best action u* derived in the planning layer is sent

back to the coordination layer, where the abstract

description of the next best action u* is translated into a

sequence of actuator commands u, which are sent to the

TAS. In the TAS, the sequence of commands is executed

and the changed environmental state is measured again by

the sensors. If the new vector y of sensor readings indicates

an emergency situation, the reactive layer processes the

sensor data directly and sends the corresponding actuator

commands to the TAS.

4.1 Development of the reasoning component

As shown by Mayer et al. [19], it is crucial for the human

operator to understand the subgoals and the planned actions

of the CCU to supervise the robotic assembly cell. This

raises the question of how the symbolic representation of

the knowledge base of the CCU must be designed to ensure

conformity with the operators expectations. Proprietary

programming languages that are used in conventional

automation have to be learned for each domain and do not

necessarily match the mental model of the human operator.

In terms of a human-centered description for matching the

procedural knowledge to the mental model, one promising

approach is the use of motion descriptors, since motions are

familiar to the human operator from manual assembly

tasks. These motions are also easy to anticipate in human-

robot interaction. In mass production it is best practice to

break down complex tasks into fundamental motion ele-

ments. To do so, the MTM method [20] as a library of

fundamental movements is often used in industry. This

method was chosen to define the motion descriptors that

can be used by the CCU to plan and execute the robotic

assembly processes also used in small lot production [21].

Based on this concept, we followed the so-called Cog-

nitive Process method (CP method [14]). This method is

able to integrate software engineering and cognitive sys-

tems engineering. To do so, the structure of the behavioral

model is retained and the software code is developed on the

basis of a cognitive process. The a priori knowledge that is

needed to control the assembly cell was implemented in

SOAR following the four steps of the static model of the

CP method. Moreover, in the actual executable it is pos-

sible that a production rule contains elements that can be

Fig. 2 Software architecture ofthe cognitive system [17]


123

related to the different steps in the CP process. The a priori

knowledge of the reasoning component consists of a set of

42 production rules.

4.1.1 Achievable model

First, the achievable model for the cognitive system has to

be defined as a desired goal, for all further actions depend

on this model. The desired goal in terms of the product to

be assembled is specified using a CAD software package.

In our particular scenario, the desired goal is the buildup of

an arbitrary structure of LEGO bricks, e.g. a pyramid of

identical bricks. Since SOARs internal representation is

solely symbolic, the desired goal has to be compiled within

the presentation layer to meet formal requirements. Addi-

tionally, the desired goal is not only compiled but enriched

with meta-information. This meta-information can be seen

as the key to our concept of cognitive automation. Besides

information on position and rotation of each brick in the

desired goal, information about the relations of each brick

to its adjacent neighbors is included in the compiled

desired goal. We call these relations neighborhood rela-

tions. The neighborhood relations are solely symbolic. In

other words, if two bricks are nearest neighbors, we only

know about the fact and the direction of the neighboring

relationship. We do not know about a possible overlap in

Cartesian space. The achievable goal as used in SOAR

contains position, rotation, color, type and the neighboring

relations of each brick in the product to be assembled.

4.1.2 Procedural model

In the second step the knowledge about procedures to

achieve the desired goal has to be considered. Based on the

neighboring relationship of the achievable model and

additional constraints, the buildup of the product is planned

by elaboration rules. For example, a brick can only be

positioned if it is on the ground or if all of its neighbors

below have already been assembled.

When using SOAR as a cognitive simulation model, one

also has to consider the operational model for designing the

procedural model according to SOARs execution cycle

[22]. For all operations that should later be executed in the

application phase of SOAR, procedures have to be pro-

posed that fire in the proposal phase of the execution

cycle. Rules that propose a motion are part of the proce-

dural model but are strongly connected to rules that apply

the motion.

4.1.3 Operational model

The operational model puts the generated plans of the

procedural model into action. The basic fundamental

movements of the MTM-1 system were used to control

the movement of the robot on the linear track (see

Fig. 1). These movements are encoded as production

rules in the operational model. The motion operators are

REACH, GRASP, MOVE (including TURN), POSITION

and RELEASE (including APPLY PRESSURE). A par-

ticular rule can only be applied if the corresponding

motion operator was selected by the procedural model.

The five motion operators are the only action primitives

in this scenario that can manipulate the assembly.

4.1.4 Environmental model

In the fourth step of the CP-method all elements that are

needed in the previous steps have to be mapped onto an

environmental model that can be used by the CCU. In the

developed scenario the gripper of the robot, the conveyer

belt, the brick feeder, the working area and the buffer are

modeled. These elements are transmitted to the cognitive

simulation model during initialization along with the goal

state.

4.2 Integration into assembly cell

Manufacturing systems like the experimental assembly

cell require robust, real-time-capable control hardware.

Although PC-based hardware and software is often used

for human supervisory control and for high level con-

trollers, embedded systems with real-time operating sys-

tems prevail as machine controllers. In the assembly cell

robot controllers supplied by the robot manufacturer are

used to control the handling robots. A motion controller

controls the conveyer belt as well as the track switches.

Additionally, a PC-based controller is connected to a

hand-like robot gripper with three fingers. Each controller

is able to execute control programs to perform movements

of the attached components and to interact with other

controllers via field bus or internet protocols. Action

primitives covering the basic features of the robots, con-

veyor belts, track switches and grippers were imple-

mented on the controllers to be remotely activated. These

action primitives, as well as the input signals from the

sensors, were made available to the reactive layer of the

CCU.

The assembly process requires the incoming LEGO

bricks to be identified and grasped before they can be

assembled in the working area. A computer vision system

with one camera is therefore used to detect the shape and

orientation of the bricks. The information it gathers is used

to track the bricks and to grasp one brick in real-time from

the moving conveyer belt. The real-time coupling between

the vision system and the robot is coordinated by the

reactive layer.


123

The reactive layer connects the TAS to the high-level

cognitive functions introduced. However, the reactive layer

must also provide capabilities for real-time reaction to

safetycritical events. In order to be able to ensure mini-

mum response time, conventional compiled software

code is used. This poses no restriction on higher cognitive

functions since the rule-based behavior is determined by

the layers above the reactive layer. Actuator commands

received from the super-ordinate coordination layer are

either interpreted by and executed within the reactive layer

or passed to the TAS, where the robots, the motion con-

trollers or PC-based gripper controllers execute them.

Sensor readings from the TAS are also either passed on to

the coordination layer or processed within the reactive layer.

For instance, video streams from the camera are usually too

complex to be interpreted by SOAR at a symbolic level. In

this case the video streams are processed in the reactive

layer and only the extracted image information about the

bricks size and shape are transmitted to higher layers.

5 System evaluation

5.1 Reasoning component

In the following, only simulation results regarding the

reasoning component of the CCU are presented due to space

limitations. The depending variables in the simulation study

are the processing time and the number of required pick and

place operations (termed MTM-1 cycles).

To evaluate the effect of the independent variables

on the dependent variables, we carried out independent

simulation runs for workpieces assembled from identical

bricks. The independent variables are (1) size of the

product to be assembled (six levels: four to 24 bricks in

steps of four), (2) number of bricks provided at the queue

(seven levels: one, four to 24 in steps of four) and feeding

regime (two levels: deterministic supply of needed bricks

and random supply including unneeded bricks). For each

combination of the levels of the independent variables 100

simulation runs were calculated. Self-developed simulation

software was used. The runs were scheduled for parallel

processing on the high-end Compute Cluster in the Center

for Computing and Communication at RWTH Aachen

University.

The simulation results show that the desired target state

was assembled correctly by the CCU in all 8,400 runs.

Assembly errors or deadlocks did not occur. Regarding the

number of required MTM-1 cycles for a workpiece of a

given size and a queue of a given length, all simulated

sequences conform to expected number of cycles. This is

shown in Fig. 3 for both feeding regimes.

The corresponding results for processing time are shown

in Fig. 4. The simulation results unambiguously show a

disproportional increase in processing time with increasing

part size and queue length for deterministic part feed.

Conversely, a stochastic part feed surprisingly leads to a

decrease in processing time over the queue length. This

counter-intuitive result can be explained by the way SOAR

processes production rules: Each needed brick in the queue

is matched to all possible positions within the target state.

Positive matches lead to proposals that have to be com-

pared. Hence, for deterministic part feed the amount of

comparisons increases disproportionally due to the known

exponential worst-case runtime behavior of SOARs

embedded RETE algorithm.

5.2 Design for humanmachine compatibility

In order to be able to use the full potential of cognitive

automation, one ultimately has to expand the focus from a

traditional humanmachine system to joint cognitive

Fig. 3 Required MTM-1 cyclesof the reasoning component of

the CCU as a function of part

size and number of bricks

available at the queue (leftdeterministic brick feed; rightstochastic brick feed)


123

systems [23, 24]. In these systems both the human operator

and the cognitive technical system cooperate safely and

effectively at different levels of cognitive control to

achieve a maximum of humanmachine compatibility.

Engineering methods like the presented CP method

[3, 14] primarily aim at technical design of cognitive sys-

tems. When developing joint cognitive systems that have to

conform to operator expectations, it is important to acquire

additional knowledge about the rules and heuristics

humans use in manual assembly.

To do so, two independent experimental trials with a

total of 36 subjects were carried out. Based on the data

three fundamental assembly heuristics could be identified

and validated [25]: (1) humans begin an assembly at edge

positions of the working area; (2) humans prefer to build in

the vicinity of neighboring objects; (3) humans prefer to

assemble in layers.

To develop a humanoid mode for cognitively auto-

mated assembly systems similar to the horse-metaphor

for automated vehicles [26], the identified assembly

heuristics where formulated as production rules. When the

reasoning component is enriched with these rules, a sig-

nificant increase in the predictability of the robot when

assembling the products can be achieved [19]. In other

words, if the knowledge base is extended by the rules and

heuristics humans use, the system can be better anticipated

by the human operator because it is compatible with his/her

mental model of the assembly process. Hence, an increase

in predictability leads to more intuitive human-robot

cooperation and therefore increases safety significantly.

6 Summary and outlook

Especially in highly automated manufacturing systems that

are aiming at producing products in almost any variety in

product space, an increase in conventional automation will

not necessarily lead to a significant increase in productiv-

ity. Therefore, novel concepts towards proactive, agile and

versatile manufacturing systems have to be developed.

Cognitive automation is a promising approach to improve

proactive system behavior and agility. In cognitively

automated systems, the experienced machine operator

plays a key architectural role as a competent solver of

complex planning and diagnosis problems. Moreover, he/

she is supported by cognitive simulation models which can

quickly, efficiently and reliably solve algorithmic problems

on a rule-based level of cognitive control and take over dull

and dangerous tasks.

A very interesting finding is that the system is especially

efficient for stochastic part feed with a large variety in

product space. The CCU is therefore able not only to

reduce planning effort with autonomous assembly planning

but also to reduce preparatory work in logistics.

To be able to accomplish complex assembly tasks

without impairing the CCU with calculations that cannot be

solved in polynomial time, future investigations will focus

on a hybrid approach [27] where the predefined planning

problem is solved prior to the assembly by generating a

state graph [28] that describes all possible assembly

sequences for the intended product. This graph can also be

updated during assembly. The reasoning component within

SOAR uses this state graph to adapt the plan to the actual

state of the assembly and part supply.

To assist the human operator within this novel auto-

mation concept, additional laboratory studies of a self-

developed augmented vision system for error detection in

assembled parts were carried out [29]. However, the aug-

mented vision system has to be extended by a real-time

decision-support function based on SOAR.

In order to validate the introduced concepts and proto-

types, future investigations also have to focus on real

Fig. 4 Processing time in [s] ofthe reasoning component of the

CCU as a function of part size

and number of bricks available

at the queue (left deterministicbrick feed; right stochastic brickfeed)


123

industrial products. As stated before, many industrially

processed components allow only a few procedural varia-

tions. Hence, cognitive automation can be like breaking a

butterfly on a wheel. Therefore, as a first step, a modular

model of an engine was developed (see Fig. 5). The model

allows for arbitrary sequences of the assembly process but

provides sufficient complexity to demonstrate the flexibil-

ity and effectiveness of cognitive automation.

Acknowledgments The authors would like to thank the GermanResearch Foundation (DFG) for its kind support of the research on

cognitive automation within the Cluster of Excellence IntegrativeProduction Technology for High-Wage Countries.

References

1. Zhang Z, Sharifi H (2000) A methodology for achieving agility in

manufacturing organizations. Int J Oper Prod Manage

20(4):496512

2. Kinkel S, Friedwald M, Husing B, Lay G, Lindner R (2008)

Arbeiten in der Zukunft, Strukturen und Trends der Industriear-

beit. Studien des Buros fur Technikfolgen-Abschatzung bei De-

utschen Bundestag, 27th edn. Sigma, Berlin (in German)

3. Onken R, Schulte A (2010) System-ergonomic design of cogni-

tive automation. Studies in computational intelligence, vol 235.

Springer, Berlin

4. Rasmussen J (1986) Information processing and human-machine

interaction. An Approach to Cognitive Engineering, North-

Holland

5. Wiendahl HP, ElMaraghy HA, Nyhuis P, Zah MF, Wiendahl HH,

Duffie N, Brieke M (2007) Changeable manufacturing. Classifi-

cation, design and operation. Ann CIRP 56(2):783809

6. Eversheim W (1998) Organisation in der Produktionstechnik, Bd.

Konstruktion. Springer, Berlin

7. Kempf T, Herfs W, Brecher C (2008) Cognitive control tech-

nology for a self-optimizing robot based assembly cell. In:

Proceedings of the ASME 2008 international design engineer-

ing technical conferences & computers and information in engineer-

ing conference, America Society of Mechanical Engineers, US

8. Chong HQ, Tan AH, Ng GW (2007) Integrated cognitive archi-

tectures: a survey. Artif Intell Rev 28:103130

9. Anderson JR, Bothell D, Byrne MD, Douglass S, Lebiere C, Qin

Y (2004) An integrated theory of the mind. Psychol Rev

111:10361060

10. Langley P, Cummings K, Shapiro D (2004) Hierarchical skills

and cognitive architectures. In: Proceedings of the twenty-sixth

annual conference of the cognitive science society. Chicago

11. Lehman J, Laird J, Rosenbloom P (2006) A gentle introduction to

soar, an architecture for human cognition: 2006 update. Retrieved

17 May 2010 from http://ai.eecs.umich.edu/soar/sitemaker/docs/

misc/GentleIntroduction-2006.pdf

12. Jones RM, Laird JE, Nielsen PE, Coulter KJ, Kenny P, Koss FV

(1999) Automated intelligent pilots for combat flight simulation.

AI Magazine 20:2741

13. Stensrud B, Taylor G, Crossman J (2006) IF-Soar: a virtual,

speech-enabled agent for indirect fire training. In: Proceedings of

the 25th army science conference, Orlando, FL

14. Putzer HJ (2004) Ein uniformer Architekturansatz fur kognitive

Systeme und seine Umsetzung in ein operatives Framework.

Koster, Berlin (in German)

15. Janrathitikarn O, Long LN (2008) Gait control of a six-legged

robot on unlevel terrain using a cognitive architecture. In: Pro-

ceedings of the IEEE aerospace conference

16. Laird JE, Yager ES, Hucka M, Tuck CM (1991) Robo-soar: an

integration of external interaction, planning, and learning using

soar. Robot Auton Syst 8:113129

17. Hauck E, Ewert D, Schilberg D, Jeschke S (2010) Design of a

knowledge module embedded in a framework for a cognitive

system using the example of assembly tasks. In: Proceedings of

the 3rd international conference on applied human factors and

ergonomics. Taylor & Francis, Miami

18. Gat E (1998) On three-layer architectures. In: Kortenkamp D,

Bonnasso R, Murphy R (eds) Artificial intelligence and mobile

robots, pp 195211

19. Mayer M, Odenthal B, Faber M, Kabu W, Kausch B, Schlick C

(2009) Simulation of human cognition in self-optimizing

assembly systems. In: Proceedings of 17th world congress on

ergonomics IEA 2009. Beijing

20. Maynard HB, Stegemerten GJ, Schwab JL (1948) Methods-time

measurement. McGraw-Hill, London

21. Mayer M, Odenthal B, Grandt M, Schlick C (2008) Task-oriented

process planning for cognitive production systems using MTM,

In: Karowski W, Salvendy G (eds) Proceedings of the 2nd

international conference on applied human factors and ergonomic

(AHFE). USA Publishing, USA

22. Laired JE, Congdon CB (2006) The soar users manual version

8.6.3

23. Hollnagel E, Woods DD (2005) Joint cognitive systems: foun-

dations of cognitive systems engineering. Taylor & Francis

Group, Boca Raton

24. Norros L, Salo L (2009) Design of joint systems: a theoretical

challenge for cognitive system engineering. Cogn Tech Work

11:4356

25. Mayer M, Odenthal B, Faber M, Kabu W, Jochems N, Schlick C

(2010) Cognitive engineering for self-optimizing assembly sys-

tems. In: Karwowski W, Salvendy G (eds) Advances in human

factors, ergonomics, and safety in manufacturing and service

industries. CRC Press, USA

26. Flemisch FO, Adams CA, Conway SR, Goodrich KH,

Palmer MT, Schutte PC (2003) The H-metaphor as a guideline

for vehicle automation and interaction. NASA/TM2003-

212672

Fig. 5 Modular engine model


123

27. Ewert D, Mayer M, Kuz S, Schilberg D, Jeschke S (2010) A

hybrid approach to cognitive production systems. In: Proceedings

of the 2010 international conference on intelligent robotics and

applications (ICIRA 2010) Shanghai, China

28. Zaeh MF, Wiesbeck M (2008) A model for adaptively generating

assembly instructions using state-based graph. In: Mitsuishi M,

Ueda K, Kimura F (eds) Manufacturing systems and technologies

for the new frontier. Springer, Berlin

29. Odenthal B, Mayer M, Kabu W, Kausch B, Schlick C (2009) An

empirical study of assembly error detection using an augmented

vision system. In: Virtual and mixed reality, VMR 2009, held as

part of HCI international 2009. San Diego


123

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