An assembly strategy scheduling method for

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An assembly strategy scheduling method for human and robot coordinated cell manufacturing Fei Chen and Kosuke Sekiyama Department of Micro-Nano Systems Engineering, Nagoya University, Nagoya, Japan

Jian Huang

HRC cell manufacturing

487 Received 6 April 2011 Revised 27 April 2011 3 June 2011 Accepted 9 June 2011

Department of Control Science and Engineering, Huazhong University of Science and Technology, Wuhan, China

Baiqing Sun School of Electrical Engineering, Shenyang University of Technology, Shenyang, China, and

Hironobu Sasaki and Toshio Fukuda Department of Micro-Nano Systems Engineering, Nagoya University, Nagoya, Japan Abstract Purpose – The purpose of this paper is to propose a model of assembly strategy generation and selection for human and robot coordinated (HRC) cell assembly. High-Mix, Low-Volume production in small production manufacturing industry, tends to employ more flexible assembly cells. The authors propose innovative human and robot coordinated assembly cells to solve the problem of persistent growing cost for human resources and occasional changes in programs and configurations for robots. The first issue is to find out an optimal way to allocate the assembly subtasks to both humans and robots. Design/methodology/approach – A dual Generalized Stochastic Petri Net (GSPN) model is theoretically studied and then off line built based on a practical assembly task for human and robot coordination. Based on GSPN, Monte Carlo method is carried out to study the time cost and payment cost or possible strategies, and Multiple-Objective Optimization (MOOP) method related Cost-effectiveness analysis is adopted to select the optimal ones. Findings – It is discovered that human and robot coordinated assembly can reduce the assembly time and meanwhile reduce the assembly cost. The authors demonstrate the effectiveness of this approach by comparing the simulation and experimental results. Originality/value – The novelty with this work is that the human and robot coordinated flexible assembly cell, as the authors proved, is the main stream in small production in future due to the higher human source pressure from society and cost pressure upon the company. Based on this innovative work, the authors proposed a dual GSPN model to model the assembly task allocation process for human and robot, the model of which is also effective in modeling the possible robot and human behaviors. Keywords Robots, Assembly, Process efficiency, Stochastic Petri-net, Human and robot coordination, Assembly cell, Monte Carlo, Multiple-objective optimization Paper type Research paper

International Journal of Intelligent Computing and Cybernetics Vol. 4 No. 4, 2011 pp. 487-510 q Emerald Group Publishing Limited 1756-378X DOI 10.1108/17563781111186761

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1. Introduction During the past tens of year development in manufacturing industry, this area has been divided into three main streams: massive production, medium production and small production. In small production, usually fully with robots, cannot keep step with the growing social demanding for high-mix, low-volume manufacturing. For the manufacturing fully with human workers, the labor cost is also increasing, and so that production system has to face the growing cost pressure in future. When addressing this problem, it will be more useful and beneficial to make efforts in the cooperation and coordination between them, consider their trade-off and improve the overall assembly effectiveness and efficiency. It can also provide the factory with certain sufficient flexibility in production, guaranteeing the quality and meanwhile reducing the cost. From the respect of human workers: the labor-intensive industry forces the human workers in assembly lines work in a not so human friendly way by repeating some certain action for long time. If robot can help the human worker doing some repetitive and boring work, the human worker can concentrate on more creative tasks, produce more novel productions, and also lower the possibility of work fatigue (Baines et al., 2004). Meanwhile, the salary for human workers is getting much higher than years ago, therefore it can also help to increase the profit space for companies. From the respect of robots: currently in electronic manufacturing industry, for instance, the connector insertion on circuit board is entirely done by robotic manipulators. Some simplified models for connector insertion are already studied to help robotic manipulators recover from insertion error (Huang et al., 2008a, 2010). However, when confronting complicated shapes of connectors or assembly parts, the extension of existed connector insertion algorithms worksnot so well. If the robot wants to detect the error type, it has to try several times by contacting the male connector with the female connector to look for some predefined models. To solve it, usually adopted way is to build more intelligent robotic grippers with multiple sensors and advanced controlling algorithms. But robots equipped with many sensors, such as vision sensor, laser range finder and force sensor, still cannot cope with all the assembly cases in industry effectively and efficiently. Moreover, in industry, one does not want to use too much complicated robots for assembly time consuming. However, human beings are highly intelligent, and therefore are cognitive of the working environment. Many researchers currently dedicate into the research of teaching robots to do assembly (Yuan, 2002) and evaluation the cooperation (Hoc, 2001) according to human workers’ experience. However, this off-line training teaches robot how to do without teaching the robot what to do and which is the first needed to be solved for human and robot coordinated (HRC) assembly work. Furthermore, if human workers can directly coordinate with robot and help the robot in certain assembly procedure when it costs much more time for the robot, such as error recovery in connector insertion, the assembly procedure can be shortened. However, due to the complication of the sensor system and potential worries of the safety issue, seldom research has been involved within the HRC cell assembly domain, even though it is explicit that HRC can bring much advance into current manufacturing industry, and consequently solve the gradually upcoming various cost pressure upon industry.

In small production (high-mix, low-volume), “human in loop” production is the trend. When addressing the modeling issue for human, there are some issues should be considered first. The first issue is hardware supporting. A general HRC working cell or related devices should be built. In Duan et al. (2008), a human and robot hybrid manufacturing cell is build, and some safety strategy is studied within this framework. In Bannat et al. (2011), a cognitive factory is introduced. In this factory, multiple sensors are equipped, and therefore, direct HRC (dHRC) and physical HRC (pHRC) is realized. In Malosio et al. (2009), a robotic system for small medium enterprises is also built by removing the fences around the robot to provide this system with sufficient flexibility, and the effectiveness method is evaluated through simulation. In the past few years, some representative robotic devices are also developed to assist human with the cooperation with robots in N.N. (2005) and Schraft et al. (2005). The second issue is the safety of human. Some theoretical researches about testing and evaluating the potential safety issue for human worker are already studied in Duan et al. (2009) and Oberer and Schraft (2007). In Kruger et al. (2005), a PMD named camera is developed to detect the interested certain position of a multiple objects (human beings and robots) and their relevant areas. With the help of PMD, the potential collision between human and robot can be identified and therefore safety of human workers is guaranteed. The third issue is the control scheme for HRC system. In Mayer et al. (2009), a cell’s numerical control named CCU is used for the high-level information processing based on the cognitive architecture named SOAR. Hence to a certain extent the CCU is able to simulate rule-based behavior of the human operator and improve the manufacturing system with direct dHRC. The last issue is the task modeling and scheduling. In Duan et al. (2009), the author describes the task modeling method in context expression. Moreover, safety strategies and information supporting devices are also developed for HRC cell assembly. They evaluate HRC work from the respect of safety and riskiness. However, their method is not described in a uniformly mathematical way, and also is specific task dependent. Therefore, it is difficult to bring this method to the general use. In Dai et al. (2011), a game theory-based queueing model is built to study at which situation the robot should handle the current unsolved issue to human workers by considering various issues, such as current human worker skills, trade-off of human interaction and performance time, and so on. In Singer and Akin (2010), the schedules developed meet all of the mission constraints while minimizing overall task list completion time and minimizing the wait time between agents. In our study, we also build an intelligent assembly cell (Chen et al., 2009). Removing the physical barrier devices, and therefore the human worker and robot can share the both working time and working space to achieve the real human and robot coordination and collaboration. Human worker and robots position are monitored by multiple cameras and artificial potential method is used to ensure the safety for human. In Kruger et al. (2009), the author theoretically studied the system architecture for human and robot cooperation in assembly lines based on the recent development in manufacturing. But now, the role that robot plays is still in doubt. In the examples listed in the paper and mentioned before, robot plays a passive device role controlled by human to play the assembly together. The robot just memories ever step during the guidance and paly it afterwards. In other examples listed, the cooperation is that human or robot holds something for the other, and the other does the assembly work. The robot is still not so active. In our research, the robot is an active assistant. It does assembly tasks that are assigned beforehand, and coordinates

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with human worker. During the assembly, the robot can monitor the human status and coordinate with human properly. Another important issue is that appropriate model should be built for both human and robot. In manufacturing industry, Petri net is widely used for modeling the assembly sequence event flow of manufacturing system (Yee and Ventura, 1999) and also some evolutionary Petri net methods are used to model flexible manufacturing (Tuysuz and Kahraman, 2010). When building the model for human workers, one obvious feature of human beings – work fatigue is considered. Fatigue is a lack of energy and motivation. The definition of fatigue for human beings varies largely from the exhaustion resulting from chronic fatigue syndrome to the weariness resulting from working all day long. In this study, it is supposed that all the human workers participated in the cell assembly work are within the same physiological level, such as healthy, quality of sleep, and only fatigue caused by the long time working (work fatigue) is considered. This fatigue can produce a decline in performance such as slower reaction times, failure to respond to changes, and the inability to concentrate and make reasonable judgments. Human workers will make mistakes or be absent minded and take some action unconsciously, and prolong the whole task finishing time. Generalized stochastic Petri net (GSPN) model is adopted to describe this human model (Chiola et al., 1993). This paper is organized as follows. After a brief introduction of Petri net, GSPN, and also time related parameter definition and calculation, we introduce and discuss the dual GSPN for modeling HRC assembly task procedure in Section 2. In Section 3, a case study is carried out. Based on GSPN, Monte Carlo method is carried out to study the time cost and payment cost for each way of subtasks allocation to human and robot. In Section 4, we demonstrate the effectiveness of this approach by comparing the simulation and experimental results. Multiple-objective optimization (MOOP) analysis related methods are adopted to select the optimal strategies. Conclusions and future works are presented in Section 5. 2. Dual GSPN model for HRC cell assembly 2.1 Petri net A Petri net is a five-tuple, PN ¼ (P, T, I 2 , I þ , M0) where: P ¼ {p1, p2, pn} is a finite and non-empty set of places. T ¼ {t1, t2, tm} is a finite and non-empty set of transitions, P > T ¼ B. I 2 , I þ :P £ T ! N0 are the backward and forward incidence weights, respectively. M0: P ! N0 is the initial marking. In the graphical representation of a PN net, I 2 ( p, t) weight specifies that a transition leading from p to t is enabled only when at least as many tokens as given by the arc weight are located on the place p. Firing will destroy exactly this number of tokens from p. Similarly I þ ( p, t) specifies the number of tokens created on place p in case of firing t. 2.2 Generalized stochastic Petri net GSPN has two different types of transitions: immediate transition (denoted as black rectangular bar) and timed transition (denoted as white rectangular bar).

When enabled, immediate transition fires at once, and timed transition fires after a random exponentially distributed enabling time (Chiola et al., 1993). A GSPN is a four-tuple, GSPN ¼ (PN, T1, T2, W) where: PN ¼ (P, T, I 2 , I þ , M0) is a Place-transition net. T1 , T is the set of timed transitions, T1 – B. T2 , T is the set of immediate transitions, T1 > T2 ¼ B, T ¼ T1 < T2. W ¼ (w1, w2, . . . , wjTj) is an array whose entry wi [ R þ : . is a rate of an negative exponential distribution specifying the firing delay, when transition ti is a timed transition, i.e. ti [ T1; or . is a firing weight, when transition ti is an immediate transition, i.e. ti [ T2. Define I ¼ I þ 2 I 2 , if I( p, t) , 0, the transition t will consume I( p, t) tokens from place p, and if I( p, t) , 0, the transition t will generate I( p, t) tokens on place p. In the GSPN model for HRC cell assembly, a transition t denotes a happening event carried by an agent referring to human or robot, and it is time delayed when t is a timed transition. In this study, when modeling an event happening, this delayed firing time can also be treated as the event taken time. Therefore, five transition t-related time parameters are defined as follows: . ta – the time an event arriving transition t. . tb – the time an event leaving transition t. . tv – the agent response time to this event. . tg – the agent process time for this event. Usually, tg is exponentially distributed. . ts – the average time taken within this transition t, usually: ta ¼ tv þ tg :

ð1Þ

Note that ;ti [ T(0 # i # jTj), if the firing time for ti is exponential distributed, the average time taken for transition ti is derived according to: ti ¼ 1/wi. 2.3 Task finish time calculation on dual GSPN model Based on the GSPN for HRC cell assembly definition, it is obvious to find out that tb ¼ ta þ ts for timed transition t, and tb ¼ ta, ts ¼ 0 for immediate transition t. In GSPN, because both timed transition and immediate transition exist, the calculation is depending on which current transition it is. 2.3.1 Task finish time definition. When we use the dual GSPN to model the HRC assembly process, one of the performance characteristics of GSPN one are interested with is the time cost for a specific task. If a transition ti [ T represents the last transition within the dual GSPN model, tib, referring to event leaving time of transition ti, represents this HRC task finish time (Tf) as well. If tia, referring to event leaving time of transition ti, is calculated, tib can be calculated according to equation (1). coloredSo, the event leaving time of the last transition within GSPN model is the task finish time. In order to calculate tib, some parameters are defined as follows. (a) pre-transition of t i. If ;t i [ Tð0 # i # jTjÞ, ;t j [ Tð0 # j # jTjÞ, pk [ Pð0 # k # jPjÞ, s. t. I þ ð pk ; t j Þ . 0 and I 2 ð pk ; ti Þ . 0, define t j as a pre-transition of ti.

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If based on I, pre-transition is defined as follows: ;t i [ Tð0 # i # jTjÞ, ;t j [ Tð0 # j # jTjÞ, pk [ Pð0 # k # jPjÞ, s. t. I ð pk ; t j Þ . 0, I ð pk ; t i Þ , 0 and jI ð pk ; t j Þj $ jI ð pk ; t i Þj, tj is a pre-transition of ti. It implicates that tj generates I( pk, tj) tokens on place pk, and ti consumes jI ð pk ; t i Þj tokens from place pk. (b) pre-transition set U(ti). ;ul [ U ðti Þð0 # l # jU ðt i ÞjÞ, if ul is a pre-transition of ti, then U(ti) is a pre-transition set of ti. In order to calculate the leaving time of a transition, the last transition of a GSPN model should be found out first. The first step is to find out the pre-transition set of each transition based on the token consumption and generation matrix I, and then based on the two situation happened during HRC assembly as follows, calculate the leaving time for each transition recursively. 2.3.2 Task finish time calculation. Not like a normal GSPN, when a dual GSPN is modeled for HRC cell assembly process, tia is calculated based on the following situations (Figure 1). Theoretically, t ia ¼ maxðu1b ; u2b ; . . . ; ujU ðti Þjb ÞðjU ðti Þj denotes the maximum number of elements in U ðt i Þ, and t ib ¼ t ia þ t is for timed transition or tib ¼ t ia for immediate transition. As shown in Figure 1, based on the definition as described, t3 (the last transition of GSPN) is a time-related task end transition, and we can get U ðt 3 Þ ¼ {t1 ; t2 } where jU(t3)j is larger than one. If t 3a is calculated, the task finish time t3b (Tf as well) can be calculated according to equation (1). There are two situations associated with the concurrence and sequential occurrence features of GSPN within HRC process when calculating the transition arriving time t3a. (a) Situation a. If both events t1 and t2 are carried out by heterogeneous agents, such as human or robot, t1 and t2 can be carried out concurrently. Therefore, the transition t3 arriving time is calculated based on the following equations: t 3b ¼ maxðt1b ; t2b Þ subject to: t 1b ¼ t 1a þ t 1s t 2b ¼ t 2a þ t 2s

HUMAN-Event/Robot-Event p1

p0

Figure 1. A dual GSPN model demonstration

t1

p2

t3

t0

p3

t2

HUMAN-Event/Robot-Event

p4

(b) Situation b. If both events t1 and t2 are carried out by homogeneous agents, such as human and human, or robot and robot. When there are only one human and one robot in the assembly cell, in this case shown in Figure 1, both of the two events t1 and t2 cannot happen concurrently, but one after the other. When there is one token on p1 and p2, respectively, t1 and t2 will be enabled, and they will compete with each other to be enabled. Based on the time delay distribution defined in t1 and t2, a random number is generated for each of them. These two random numbers are counted down, and the one reaches zero is enabled first. If t1 fires first, and then t2 continues to compete to fire. If the former elapsed counted down enabling time of t2 is forgotten, it is a race enable rule, otherwise if it is remembered, it is the race age rule. Whatever rule used, if t1 fires first, the transition t3 arriving time t3a is calculated based on the following equations: t 3b ¼ maxðt1b ; t 2b Þ subject to: t 1b ¼ t 1a þ t 1s t 2b ¼ t 1b þ t 2a þ t 2s And obviously, t2b . t1b, so t3a ¼ t2b. Whichever situation a or b it is, t 3b ¼ t 3a þ t3s . 2.4 Calculate the human and robot participation time Th, Tr It is also important to know the time human and robot spend, respectively, because the task finish time is not the sum of human and robot participation time, due to the concurrent and serial assembly features of the human and robot cooperation process: . T h : ;t i [ Tð0 # i # jTjÞ; T h ¼ sumðt is Þ iff. ti is carried out by human workers. . Tr:;tj [ T(0 # j # jTj, j 8 i, i þ j ¼ jTj), Tr ¼ sum(tjs) iff. tj is carried out by robots.

2.5 Assembly strategy selection process Figure 2 shows the procedure to select the best time and payment tradeoff assembly strategy based on the dual GSPN model. When there is an assembly task, the first step is to describe the assembly task process and define the final objective. After decomposing the whole task into subtasks, the subtask will allocate to human and robot, the way of allocation, of course, is numerous, according to the exhaustion listing method. Build a dual GSPN model for each way of allocation, and calculate the time cost and associated payment cost for accomplishing this assembly task. Monte Carlo method is used when calculating the task finish time based on the generated dual GSPN model. After we get the task finish time and payment cost, a MOOP method is carried out to select the best trade-off assembly strategies.

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An Assembly Task (Suitable for H&R Coordinated Assembly)

Appropriate Assumptions along with Experts in Industry

494

Subtasks Allocation to H&R (List by Exhaustion)

Criterion: Requirements that Industries Concerned

Figure 2. Research procedure architecture

Task Objective and Procedure Description and Subtask Decomposition

Dual GSPN Generation for Each Allocation (Automatically or Manually)

Appropriate Assumptions from Researches of Experts in Psychology

Monte Carlo Simulation, Calculation (Task Finish Time, Payment Cost, ...)

Data: Time Cost based on H&R Behavioral Features Analysis

Allocations Optimization (Multiple Objective Optimization)

Semi-optimal Allocation Generated and Experiment

3. Multiple connector insertion task 3.1 A HRC cell A HRC assembly is that both of human and robot work within the same room, sharing the working time and working space, with no physical barriers (Figure 3). The utilization of their merits will help to achieve the best manufacturing efficiency. 3.2 Task objective and procedure description A simplified assembly task from an industrial power supply module assembly is introduced to test the proposed approach. In electronic manufacturing, multiple types of connectors are assembled totally by robots. However, the unpredictable errors during the insertion process results in long time gesture search and adjustment and consequently low successful ratio, and even fatal unrecoverable errors triggering the stop of the manufacturing system. Therefore, in order to fix the shortcomings for fault

Figure 3. HRC manufacturing

detection and diagnosis in this area, necessary human involved assembly with robot can help the robot work effectively, and accomplish the whole assembly task efficiently. The goal of this assembly task is to assemble all the connectors to the right position within this power supply module (Figure 4). 3.3 Subtask decompostion and allocation to human and robot There are three types of connectors within this assembly. C1 denotes the connector with one head, so as C2 and C3. After discussion with the assembly experts, the whole assembly procedure is decomposed into six subtasks as shown in Table I. Moreover, two basic assembly rules are conclude due to the limitation of the product construction, which are the serial assembly rule and parallel assembly rule: (1) serial assembly rule: C1-C3 connectors should be assembled one after another; and (2) parallel assembly rule: within C2 and C3, subtask Nos 2, 3 and Nos 4-6 can be assembly parallel by human and robot, respectively.

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Based on the rules, it is assumed that there is no difference for human or robot to insert any head of C2 or C3 (the position of task Nos 2 and 3 is equal, so as the same with

Connectors-2

Connectors-3

Connectors-1

Connectors

Connector fix area

Assembly area

Robotic manipulator

Human and Robot Coordination

Human worker

Figure 4. Task and HRC scenario

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Table I. Task decomposition

task Nos 4-6). After a combinatorial mathematics calculation, there are 21 configurations for the subtasks allocation for human and robot (Table II), and the aim is to find out the best tradeoff configurations, which bring the best performance tradeoff for the cost effectiveness in the human and robot cell assembly system. 3.4 Dual GSPN model generation for each allocation (e.g. allocation No. 11) In this allocation, human does task Nos 1, 2, 4 and robot does task No. 3, 5, 6. The following dual GSPN model (Figure 5) distributes the subtasks to human and robot. Only after human inserts connector C1(t1), human can assemble C2 2 1(t2) and robot can assemble C2 2 2(t4). Only after C2(t2, t4) is inserted, human can insert C3 2 1(t3), and robot can insert C3 2 2(t5), and C3 2 3(t6) sequentially. Therefore, the serial assembly rule and parallelassembly rule are fully guaranteed.

Task no.

Task

1 2 3 4 5 6

Pick up connector Pick up connector Pick up connector Pick up connector Pick up connector Pick up connector

Configuration no.

Table II. Task decomposition

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21

C1 2 1, and C2 2 1, and C2 2 2, and C3 2 1, and C3 2 2, and C3 2 3, and

insert at P1 2 1 insert at P2 2 1 insert at P2 2 2 insert at P3 2 1 insert at P3 2 2 insert at P3 2 3

Human tasks (task no.)

Robot tasks (task no.)

1, 2, 3, 4, 5, 6 2, 3, 4, 5, 6 1, 2, 4, 5, 6 1, 2, 3, 4, 5 2, 4, 5, 6 2, 3, 4, 5 1, 4, 5, 6 1, 2, 3, 4 4, 5, 6 1, 4, 5 1, 2, 4 1, 2, 3 4, 5 2, 4 2, 3 1, 4 1, 2 4 2 1 –

– 1 3 6 1, 3 1, 6 2, 3 5, 6 1, 2, 3 2, 3, 6 3, 5, 6 4, 5, 6 1, 2, 3, 6 1, 3, 5, 6 1, 4, 5, 6 2, 3, 5, 6 3, 4, 5, 6 1, 2, 3, 5, 6 1, 3, 4, 5, 6 2, 3, 4, 5, 6 1, 2, 3, 4, 5, 6

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Human is ready

Human inserts C1

Human finishes C1

Human inserts C2-1

Human finishes C2-1

Human inserts C3-1

t1

p2

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p3

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p1

Human tasks are done p4

ρ1 = 1 Assembly parts are ready

p0

Robot: Human finishes C1

p9

T0

p5

p6

t5

Robot finishes C2-2

Robot inserts C3-2

t1

Robot is ready

Robot inserts C2-2

ROBOT-PN

Robot: Human finishes C2-1

p10

p11

p7

t6

Robot finishes C3-2

Robot inserts C3-3

Human: Robot finishes C2-2

t7 ρ2 = 1

p8 Robot tasks are done

Figure 5. A dual GSPN example

According to the definition of I, I þ and I 2 , they are deduced as follows: 2

I2

6 6 p0 6 6 6 p1 6 6 6 p2 6 6p 6 3 6 6p 6 4 6 6 ¼ 6 p5 6 6 p6 6 6 6 p7 6 6 6 p8 6 6 6 p9 6 6 6 p10 4 p11

t0

t1

t2

t3

t4

t5

t6

1

0

0

0

0

0

0

0

1

0

0

0

0

0

0

0

1

0

0

0

0

0

0

0

1

0

0

0

0

0

0

0

0

0

0

0

0

0

0

1

0

0

0

0

0

0

0

1

0

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0

0

0

0

0

1

0

0

0

0

0

0

0

0

0

0

0

1

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0

0

0

0

0

0

1

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0

0

0

1

0

0

0

t7

3

7 07 7 7 07 7 7 07 7 07 7 7 17 7 7 07 7 7 07 7 7 07 7 7 17 7 7 07 7 7 07 5 0

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6 6 p0 6 6 6 p1 6 6 6 p2 6 6p 6 3 6 6p 6 4 6 6 ¼ 6 p5 6 6 p6 6 6 6 p7 6 6 6 p8 6 6 6 p9 6 6 6 p10 4 p11 2

I ¼I

þ

2I

2

6 6 p0 6 6 6 p1 6 6 6 p2 6 6p 6 3 6 6p 6 4 6 6 ¼ 6 p5 6 6 p6 6 6 6 p7 6 6 6 p8 6 6 6 p9 6 6 6 p10 4 p11

t0

t1

t2

t3

t4

t5

t6

0

0

0

0

0

0

0

1

0

0

0

0

0

0

0

1

0

0

0

0

0

0

0

1

0

0

0

0

0

0

0

1

0

0

0

1

0

0

0

0

0

0

0

0

0

0

1

0

0

0

0

0

0

0

1

0

0

0

0

0

0

0

1

0

1

0

0

0

0

0

0

0

1

0

0

0

0

0

0

0

0

1

0

0

t7

3

7 17 7 7 07 7 7 07 7 07 7 7 07 7 7 07 7 7 07 7 7 07 7 7 07 7 7 07 7 7 07 5 0

ð3Þ

t0

t1

t2

t3

t4

t5

t6

21

0

0

0

0

0

0

1

21

0

0

0

0

0

0

1

21

0

0

0

0

0

0

1

21

0

0

0

0

0

0

1

0

0

0

1

0

0

0

21

0

0

0

0

0

0

1

21

0

0

0

0

0

0

1

21

0

0

0

0

0

0

1

0

1

0

0

21

0

0

0

0

1

0

0

21

0

0

0

0

21

1

0

0

t7

3

7 0 7 7 7 0 7 7 7 0 7 7 0 7 7 7 21 7 7 7 0 7 7 7 0 7 7 7 0 7 7 7 21 7 7 7 0 7 7 7 0 7 5 0

ð4Þ

According to the definition of pre-transition set, based on I, we can find out all the pre-transition sets of ti(0 # i # 7) as shown in Table III. Within this table, {t1, t2, t3} are the events carried out by human, while {t4, t5, t6} are the events carried out by robot, and t0, t7 are immediate transitions. {t1, t2, t3} and {t4, t5, t6} are heterogeneous, respectively. Therefore, the task finish time Tf is as well the event leaving time of t7(t7b), which is derived as follows:

Transition ti

Direction pre-transition set of ti

t0 t1 t2 t3 t4 t5 t6 t7

{B} {t0} {t1} {t2, t4} {t0, t1} {t4} {t5} {t3, t6}

T f ¼ t 7b ¼ t 7a ¼ maxðt 3b ; t 6b Þ

499 Table III. Pre-transition set

¼ maxðt 3a þ t3s ; t6a þ t 6s Þ

¼ maxðmaxðt 2b ; t4b Þ þ t 3s ; t5b þ t 6s Þ ¼ maxðmaxððt1b þ t 2s Þ; t4b Þ þ t 3s ; t4b þ t 5s þ t6s Þ ¼ maxðmaxððt0a þ t 1s þ t2s Þ; ðmaxðt0b ; t1b Þ þ t4s ÞÞ þ t3s ; maxðt 0b ; t 1b Þ þ t4s þ t 5s þ t6s Þ ¼ maxðmaxððt0a þ t 1s þ t2s Þ; ðt 1a þ t1s þ t 4s ÞÞ þ t3s ; t1a þ t 1s þ t4s þ t 5s þ t6s Þ Therefore: T f ¼ maxðmaxððt0a þ t 1s þ t2s Þ; ðt0b þ t 1s þ t4s ÞÞ þ t3s ; t0b þ t 1s þ t 4s þ t 5s þ t 6s Þ

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where t0a ¼ t0b ¼ 0. According to equation (5), to calculate the task finish time is actually to calculate each transition average time taken tis(0 # i # jTj). The objective for modeling HRC assembly is to find out the least time consuming assembly sequence and also the lowest payment cost for human and robot. With the cost for human resource keep growing recently in industry, it is also necessary to consider the payment cost for each assembly configuration based on the working time of both human and robot, respectively. 3.5 Calculate ts In Figure 5, for each time delayed transition of human and robot, it contains another GSPN, which represents the detailed behavior transition of human or robot, when they face different situations, such as error detection, and error recovery. (a) For robot. Figure 6 shows that when robot gets a task, i.e. inserting connectors, first, it takes the insertion action t0. If it succeeds in inserting the connector, it will directly goes to successful event t6, however, it is possible (r1) that the robot fails inserting the connector with the first try. In that case, the robot has to do error recovery. Error recovery mainly contains two steps, first the robot has to search for a better gesture around the connector based on the pre-stored error algorithms, such as the spiral search (Huang et al., 2008b), probing search, etc. After robot gets a better gesture, it will take the insertion action t2. If the robot fails again, robot will repeat the error recovery strategy. Based on our former research, the error recovery strategy can

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Failing times are recorded

ρ3 t2

p1

500

ρ1

Figure 6. Detailed event transition for robot within a timed transition

t0

p0

t1

p2

Connector Search and insertion is insert again failed

ρ2

p4 Connector insertion is failed after one try, asking for help ρ6 = 1 p5

t4 ρ4

Connector is inserted

Robot inserts

Connector is inserted

t5 ρ5 = 1

t6

Connector is inserted successfully

t7

cope with 80-90 percent insertion errors. If the robot has failed once again to insert the connector, it will alert and stop, waiting for human worker’s help. Because this process contains the uncertain situation, it is significant to get the mean time distribution for ts. Monte Carlo method is used to study average of ts. Monte Carlo methods (Metropolis, 1987) are useful for modeling phenomena with significant uncertainty in inputs. Algorithm 1 shows the process to calculate the average time of ts for the event shown in Figure 6. We get the data from the sampling of practical experiments, all the parameters are shown in the Table IV. Mt0k denotes the average time for transition t0 for task No. k while Vt0k denotes its variance. According to the practical situation in experiments, robot failure ratio r1 and r3 are set as 0.1 percent. From our former research, the search time for the robot when error occurs is around 5 s. The robotic manipulator used in the experiment is a Mitsubishi RV 2 1A industrial manipulator with a mass of 19 kg and repeatability of ^ 0.02 mm. In order to protect the safety of human workers, according to ISO (2006) standards (ISO13849), RV-1A moves with safe reduced speed (less than 250 mm/s) and monitored position: Algorithm 1. Monte Carlo method for robot transition Input: Mt0k, Vt0k, Mt2k, for each subtask k, r1, r3 and PI ¼ 3.14. 1: for n ¼ 1 until n ¼ N ¼ 10000 do

Task no. (k)

Table IV. Parameters for human event transition

1 2 3 4 5 6

By human (s) By human (s) Mt4k, Mt6k

Human operation Variance (s) Vt4k, Vt6k

Human buffer time ratio r2

Human recover time (s) Mt2k

Human action repeating ratio r4

5 4.2 4.4 3.8 3.9 5.3

0.67 0.19 0.49 0.18 0.1 0.23

0.5 percent

0.5

2 percent

Notes: ar2 þ r3 ¼ 1; br4 þ r5 ¼ 1

ttemp ¼ 0 Generate a random number k between 0 and 1. ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi qPr pffiffiffiffiffiffiffiffi  jPr k j 4: t snk ¼ t emp M t0k þ ð21Þ 22V t0k log PV t0k 2PI . where pffiffiffiffiffiffiffi  P ¼ 1= V t0k 2PI . 5: if Prk # r1 // insert fails then 6: t snk ¼ t snk þ M t2i þ t emp , // insert again. 7: if Prk # r3 then 8: Robot alerts. // insert fails again, robot quits. 9: end if 10: end if 11: n¼nþ1 12: end for 13: Calculate average trk within N loops. Output: trk // time taken within this transition for subtask k.

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(b) For human. Figure 7 shows the possible behavior transition for human worker during the assembly. When human worker is facing an assembly task, It is possible (r2) that it takes a short time (t2) for him to response. This is human worker’s fatigue related, the longer the human works, the longer time it will take for him to response. However, in this model and experiment, it is assumed that human work is under a good psychological status, and we do not have to concern the performance declining (possibility ri changes) because of the fatigue with working time growing. After the short response time, human worker starts to work t4. There is a possibility (r4) that human worker fails to accomplish the task, according to the human nature, he will keep trying (t6) until he succeeds. It is assumed that, human worker usually will succeed after one try. Monte Carlo method is also used to calculate ts for each subtask taken by human in Algorithm 2: Algorithm 2. Monte Carlo method for human transition Input: Mt4k, Mt6k, Vt4k, Vt6k, Mt2k, for each subtask k, r1, r4 and PI ¼ 3.14, where Mt4k ¼ Mt6k, Vt4k ¼ Vt6k 1: for n ¼ 1 until n ¼ N ¼ 10000 do 2: ttemp ¼ 0 3: Generate a random number Prk between 0 and 1. 4: if Prk # r1 // it takes time for human to response then p1 ρ2 ρ1 = 1 t0

P0

p4

t2

Human is mind Human takes unconcentrated time to recover or or adjust environmental constraints Time for human Human starts p2 to work to work

ρ4

t1

t5

ρ6 = 1 t4

p3

Connector is inserted

Human inserts ρ3

t3

t6

Connector is not Human inserts properly again inserted

ρ5

t7

p5

t8

Connector is inserted successfully

Figure 7. Detailed event transition for human within a timed transition

tsnk ¼ tsnk þ Mt2k end if qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi pffiffiffiffiffiffiffiffi  tsnk ¼ tsnk þ M t4k þ ð21ÞjPrk j 22V t4k log PV t4k 2PI : where pffiffiffiffiffiffiffiffiffi P ¼ 1=ðV t4k 2PI Þ. 8: if Prk # r2 , // insert fails then 9: tsnk ¼ tsnk þ ttemp // insert fails again, robot quits. 10: end if 11: n¼nþ1 12: end for 13: Calculate average trk within N loops. Output: trk // time taken within this transition for subtask k.

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We get the data from the sampling of practical experiments, all the parameters are shown in the Table V. According to equation (2), the assembly time for configuration 11 could be calculated. The Gantt diagram chart for this configuration is shown in Figure 8.

Task no. (k)

Table V. Parameters for robot event transition

Figure 8. Gantt diagram chart for configuration No. 11

1 2 3 4 5 6

By robot (s) Mt0k 250 mm/s

Robot operation variance (s) Vt0k

Robot failure ratio r1, r3

Search time (s) Mt2k

0.1

0.1%

5 Spiral search Probing search

17 16 16 16 16 16

Notes: ar1 þ r2 ¼ 1; br3 þ r4 ¼ 1

3.6 Calculate payment P Beside the task finish time, the payment cost for human and robot based on the time they spent within this task are also considered as criterions. To calculate the payment cost for human and robot, the payment cost per unit time for human and robot should be defined and calculated. Payment cost per unit time for human: Ph. According to the data from the World Bank, we can get the general payment distribution for human worker per hour. The payment cost per second we choose for human is $0.057. Payment cost per unit time for robot: Pr. The depreciation is considered when calculating the cost per hour for robots, including the cost for equipment, electricity, and maintenances. In the case studied, first, it is assumed that one human worker is partnered with one small sized industrial robotic manipulator, which is supposed to be cost US$10,000 for hardware. The electricity, second, the robot consumes also contributes to the basic cost every hour, which is supposed to be 1 kwh/h. Third, because the robot should be carefully instructed, programmed and calibrated beforehand and maintained afterwards, which are usually done by specialized experts or programmers. In common sense, we assume that it will cost a human worker six months of working time to program and maintain for the robot during its whole useful life. According to the standards adopted around the world, the useful life for mechanical equipment is set to be ten years. The most basic form of depreciation is known as straight-line depreciation (Kieso et al., 2006). Using this method, the cost of the asset is spread out equally over the expected life of the asset according to the equation below: C dep ¼

C equi þ C hum N hm N d N h þ C elec N rm N d N h

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ð6Þ

Where (in Table VI). According to equation (6), we can get the cost per second for robot is $0.0005. The overall payment is calculated according to equation (7): P ¼ P hTh þ P r T r

ð7Þ

4. Experiments and results 4.1 Allocation simulation and experiment results Table VII shows the experimental results for each way of subtask allocation for human and robot. Parameter

Description

Cdep Cequi Chum Celec Nhm Nrm Nd Nh

The depreciation cost per hour to be calculated The one-off cost for the equipment (robot) The cost per hour for human to maintain the robot The cost per hour for electricity consumed by the robot, a constant value 0.1 in this case Set to be 6, referring the total month cost to maintain the robot during its useful life Set to be 120, referring the total month during the useful life of the robot Set to be 20, referring the total working days within a month Set to be 8, referring the total working hours within a day

Table VI. Parameter description in equation (6)

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Table VII. Experiment results

Configuration no. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21

Task execution time Tf (s)

Human execution time Th (s)

Robot execution time Tr (s)

33 34 33 28 42 37 49 44 57 49 49 56 60 59 67 63 64 73 75 78 88

33 19 22 22 18 18 20 17 14 12 14 12 7 9 8 8 9 5 7 6 0

0 15 14 15 30 28 29 30 43 45 44 44 60 59 58 59 58 73 75 72 88

The simulation and experiment results are shown in Figure 9. Figure 9 shows the simulation (in dash line) and experimental (in solid line) results. The simulation results basically agree with the experimental results, proving the validation and effectiveness of proposed dual GSPN model. In Figure 9, from the respect 1.4

1 0.8

50

0.6 0.4

Figure 9. Simulation and experiment results of task finish time and payment cost

0.2 0

21 18 19 20 15 13 14 16 17 9 12 10 11 7 5 8 6 2 3 4 1 Configuration No.

0

Total Money Cost: $

Total Assembly Time: s

1.2

of macroscopic view, with the assembly task finish time decrease (in red), due to the growing participation of human workers, the payment cost increase (in blue), also due to the higher payment cost for human workers. From the respect of microscopic view, due to the discrete subtask division, there occurs that when robot finish one subtask, it cannot immediately start the next subtask, but wait for the human worker. Consequently, the task finish time line is not strictly going down, and the payment cost line is not strictly going up. The cross-shape of these two lines requires that a tradeoff study should be carried out to study the optimal subtask allocation selection method based on MOOP, which is to shorten the task finish time, while to reduce the payment cost. But this two lines are greatly affected by process of human and robot interactivities, which leads to a result that the results, especially the assembly strategies selected, are also decided by how accurately this model can present. Here, basically, two issues are listed as follows. Human features: human worker performance is not a stable value. Within a working day, the performance of human worker varies at different segments of working time. In the modeling for human behavior, the possibility that human tires is set as fixed value. Long working time-related work fatigue which will cause the estimate results and construction of dual GSPN changing over time should be taken into consideration. Consequently, the system can dynamically adjust the construction of dual GSPN when the human worker performance declines. Dialogue from human to robot: not only is the robot position monitored by the safety system, but also the position of human worker is monitored by the robot. In the modeling and experiment, the robot takes the action just after human finish his subtask. The truth is that robot has to estimate the state of human worker and do corresponding action. Better sensor system design can help to reduce the time cost for robot to process the information, and therefore reduce the overall time cost for HRC cell assembly. 4.2 MOOP method comparison In this human and robot cooperated cell assembly, the decision maker always prefer the low time consuming and payment cost assembly strategy, which is a MOOP problem. In this research, the MOOP is to find out a group of allocation A: A ¼ a1 ; a2 ; . . . ; ai ; i [ I ¼ 1; 2; . . . ; 21 ( 0; ai is not one of the optimal selections ai ¼ 1; ai is one of the optimal selections Therefore, the task finish time Tf is related with a certain way of task allocation A indicated as Tf(A), while it is similar with the payment cost P as P(A). The MOOP is modeled as follows: minðT f ðAÞ; PðAÞÞ s. t. A ¼ {a1 ; a2 ; . . . ; ai };

1 # i # 21

ai ¼ 0; 1:

There are two common used MOOP method to choose the optimal alternatives.

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(a) Weighted method. Because there are two criterions Tf and P are used to judge one way of allocation, different weights are assigned to them to generate an overall judgment. First, Tf and P are uniformed to [0,100], respectively. Then, assign a weight to both criterion, denoted as wt and wp , with a constraint that wt þ wp ¼ 1. Then, the judgment parameter J is calculated according to equation (8):

506

J ¼ wt T f þ wp P

ð8Þ

The J under different setting of wt and wp are shown in Figure 10. If the factory demands a shorter manufacturing time, set wt . wp. If the factory demands a shorter manufacturing time, set wt , wp. For a certain setting up of wt and wp (as shown in Figure 10 where wt ¼ wp ¼ 0.5), a threshold value can be assigned to filter out the bad allocations based on J. In this case, if this acceptable threshold value is set as 44, the acceptable allocation group is A ¼ {a1, a2, . . . , ai} where: ( 0; i [ I 2 {4; 6; 10; 11; 13; 14} ai ¼ 1; i [ {4; 6; 10; 11; 13; 14}

80 70

Overall Value

60 50 40 30 20 10

Overall Value:wT = 0.2, wC = 0.8 Overall Value:wT = 0.4, wC = 0.6 Overall Value:wT = 0.5, wC = 0.5 Overall Value:wT = 0.6, wC = 0.4 Overall Value:wT = 0.8, wC = 0.2

21 18 19 20 15 13 14 16 17 9 12 10 11 7 (a)

5

8

6

2

3

4

1

6

2

3

4

1

52 Overall Value

50 48 46 44 42 Overall Value:wT = 0.5, wC = 0.5

40

Figure 10. The overall judgment of J

21 18 19 20 15 13 14 16 17 9 12 10 11 7 5 Configuration No. (b)

8

(b) skyline method (Morse et al., 2007). In an n-dimensional space, there is a point set p(jpj ¼ n). one point P t ¼ ðP t1 ; P t2 ; P tn ÞðP t [ pÞ strongly dominates another point P :t ¼ ðP :t1 ; P :t2 ; ¡; P :tn ÞðP :t [ pÞ iff. ;i [ {1; 2; . . . ; n}, P ti is better (more effective, lower cost, shorter execution time) than P :ti . If a point P t cannot be dominated by any P :t [ p 2 P t . P is on the skyline. Therefore, the points within a skyline are those cannot be ruled out by each other, and they are consequently the best choice for one specific MOOP problem. In this case, P t ¼ ðT f ; PÞ represents an allocation criterion pair, and p is the set of all the 21 allocations ðP t [ p; n ¼ 21Þ. P t ¼ ðT f ; PÞ strongly dominates another point P :t ¼ ðT :f ; P : Þ iff. ;i [ {1; 2; · s; n}, T fi , T :fi and P i , P :i . Figure 11 shows the skyline of this assembly subtask allocation set p. The skyline for this case is shown in Figure 11. From Figure 11, it is easy to figure out that the acceptable allocation group within in the skyline (in red) is A ¼ {a1 ; a2 ; . . . ; ai } where: ( ai ¼

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0; i [ I 2 {1; 2; 3; 7; 9; 11; 15; 17; 18; 21} 1; i [ {1; 2; 3; 7; 9; 11; 15; 17; 18; 21}

Note that different choose of payment cost per second for human and robot will lead to different results using above comparison methods. The results are shown in Table VIII. 1.5

21

Total Money Cost $

18

1 15 17 9

11

0.5

7 2 3 1

0 20

30

40

50

60

70

80

90

100

Total Assembly Time: s

Method Weighted method Skyline method

Figure 11. Ths Skyline

Selected alternative allocations {4,6,10,11,13,14} {1,2,3,7,9,11,15,17,18,21}

Table VIII. Result comparison of different methods

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Table VIII shows that the configuration No. 11 occurs in the selected allocations of both the weighted method and skyline method, therefore configuration No. 11 is one of the preferred selection. According to the practical situation, the decision maker can adjust the task finish time and payment cost by switch between the selected alternative allocations. 5. Conclusion This paper has presented a methodology in selecting the optimal assembly strategy by building HRC cell assembly model. A dual GSPN model is used to better describe all the possible situations during HRC assembly, including the possibility that human or robot makes mistakes. By using Monte Carlo method, a general task finish time and payment cost of each assembly strategy configuration is obtained, based on which, MOOP analysis is conducted to study the trade-off of each configuration. Finally, a set of better configurations is derived. Strategy makers in factory could choose the best solution among them by considering the practical requirements and environments. In small production manufacturing, it is difficult to maintain the HRC in an effective way. This method has provided the decision maker a general and mathematical method that by analyzing the behavior of robot and, especially human, get the time distribution for each behavior and then build a dual GSPN. Based on the simulation results of dual GSPN, one can better estimate time and payment cost for one specific configuration of the subtasks for human and robot. References Baines, T., Mason, S., Siebers, P.O. and Ladbrook, J. (2004), “Humans: the missing link in manufacturing simulation?”, Simulation Modelling Practice and Theory, Vol. 12 Nos 7/8, pp. 515-26. Bannat, A., Bautze, T., Beetz, M., Blume, J., Diepold, K., Ertelt, C., Geiger, F., Gmeiner, T., Gyger, T., Knoll, A., Lau, C., Lenz, C., Ostgathe, M., Reinhart, G., Roesel, W., Ruehr, T., Schuboe, A., Shea, K., Stork genannt Wersborg, I., Stork, S., Tekouo, W., Wallhoff, F., Wiesbeck, M. and Zaeh, M.F. (2011), “Artificial cognition in production systems”, IEEE Transactions on Automation Science and Engineering, Vol. 8 No. 1, pp. 148-74. Chen, F., Di, P., Huang, J., Sasaki, H. and Fukuda, T. (2009), “Evolutionary artificial potential field method based manipulator path planning for safe robotic assembly”, International Symposium on Micro-NanoMechatronics and Human Science, Nagoya, pp. 92-7. Chiola, G., Marsan, M.A., Balbo, G. and Conte, G. (1993), “Generalized stochastic Petri nets – a definition at the net level and its implications”, IEEE Transactions on Software Engineering, Vol. 19 No. 2, pp. 89-107. Dai, T., Sycara, K. and Lewis, M. (2011), “A game theoretic queueing approach to self-assessment in human-robot interaction systems”, IEEE International Conference on Robotics and Automation, Shanghai, pp. 58-63. Duan, F., Morioka, M., Tan, J. and Arai, T. (2008), “Multi-modal assembly-support system for cell production”, International Journal of Automation Technology, Kobe, Vol. 2 5 pp. 384-9. Duan, F., Morioka, M., Tan, J. and Arai, T. (2009), “Task modeling approach to enhance man-machine collaboration in cell production”, IEEE International Conference on Robotics and Automation, pp. 152-7.

Hoc, J. (2001), “Towards a cognitive approach to human-machine cooperation in dynamic situations”, International Journal of Human-Computer Studies, Vol. 54 No. 4, pp. 509-40. Huang, J., Fukuda, T. and Matsuno, T. (2008a), “Model-based intelligent fault detection and diagnosis for mating electric connectors in robotic wiring harness assembly systems”, IEEE ASME Transactions on Mechatronics, Vol. 13 No. 1, pp. 86-94. Huang, J., Di, P., Fukuda, T. and Matsuno, T. (2008b), “Fault-tolerant mating process of electric connectors in robotic wiring harness assembly systems”, Proceedings of the 7th World Congress on Intelligent Control and Automation, Chogging, pp. 2339-44. Huang, J., Di, P., Fukuda, T. and Matsuno, T. (2010), “Robust model-based online fault detection for mating process of electric connectors in robotic wiring harness assembly systems”, IEEE Trans on Contr. Syst. Technol., Vol. 18 No. 5, pp. 1207-15. ISO (2006), Safety of Machinery – Safety-related Parts of Control Systems – Part 1: General Principles for Design, International Organization for Standardization (ISO), pp. 13849-51. Kieso, D.E., Weygandt, J.J. and Warfield, T.D. (2006), Intermediate Accounting, Chapter 11, Wiley, Hoboken, NJ. Kruger, J., Lien, T.K. and Verl, A. (2009), “Cooperation of human and machines in assembly lines”, CIRP Annals – Manufacturing Technology, Vol. 58 No. 2, pp. 628-46. Kruger, J., Nickolay, B., Heyer, P. and Seliger, G. (2005), “Image based 3D surveillance for flexible manCrobot-cooperation”, CIRP Annals, Vol. 54 No. 1, pp. 19-23. Malosio, M., Pedrocchi, N. and Tosatti, M. (2009), “Safe obstacle avoidance for industrial robot working without fences”, IEEE International Conference on Intelligent Robots and Systems, St. Louis, MO, pp. 3435-40. Mayer, M., Odenthal, B., Faber, M., Neuho¨fer, J., Kabuß, W., Kausch, B. and Schlick, C. (2009), “Cognitive engineering for direct human-robot cooperation in self-optimizing assembly cells”, Human Centered Design, Vol. 5619, pp. 1003-12. Metropolis, N. (1987), “The beginning of the Monte Carlo method”, Los Alamos Science, No. 15, pp. 125-30 (Special Issue dedicated to Stanislaw Ulam). Morse, M., Patel, J.M. and Grosky, W.I. (2007), “Efficient continuous skyline computation”, Information Sciences, Vol. 177 No. 17, pp. 3411-37. N.N. (2005), “Intelligent assist devices”, Stanley Cobotics, available at: www.stanleya.ssembly.com Oberer, S. and Schraft, R.D. (2007), “Robot-dummy crash tests for robot safety assessment”, IEEE International Conference on Robotics and Automation, Rome, pp. 2934-9. Schraft, R.D., Meyer, C., Parlitz, C. and Helms, E. (2005), “PowerMate – a safe and intuitive robot assistant for handling and assembly tasks”, IEEE International Conference on Robotics and Automation, pp. 4074-9. Singer, S.M. and Akin, D.L. (2010), “Scheduling robot task performance for a cooperative human and robotic team”, Acta Astronautica, Vol. 66 Nos 1/2, pp. 102-16. Tuysuz, F. and Kahraman, C. (2010), “Modeling a flexible manufacturing cell using stochastic Petri nets with fuzzy parameters”, Expert Systems with Applications, Vol. 37 No. 5, pp. 3910-20. Yee, S.T. and Ventura, J.A. (1999), “A Petri net model to determine optimal assembly sequences with assembly operation constraints”, Journal of Manufacturing Systems, Vol. 18 No. 3, pp. 203-13. Yuan, X. (2002), “An interactive approach of assembly planning”, IEEE Transactions on Systems Man and Cybernetics Part A-Systems and Humans, Vol. 32 No. 4, pp. 522-6.

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About the authors Fei Chen received the BS Degree in Computer Science from Xi’an Jiaotong University (XJTU), China, in 2006, and the MS Degree in Computer Science from Harbin Institute of Technology (HIT), China, in 2008. He is currently a Doctor candidate in the Department of Micro-Nano System Engineering and the Department of Mechano-informatics and Systems, Nagoya University, Japan. His current research interests include robotic assembly and human and robot cooperation. Fei Chen is the corresponding author and can be contacted at: [email protected] Kosuke Sekiyama received his BE, ME and Dr Eng. Degrees from Nagoya University in 1992, 1994, and 1997, respectively. Currently, he is an Associate Professor of Department of Micro-Nano Systems Engineering, Nagoya University. His main research interests are distributed autonomous systems, in particular, self-organizing systems in the various system levels and distributed manufacturing systems. Jian Huang received PhD Degrees in Control Theory and Control Engineering from Huazhong University of Science and Technology (HUST), Hubei, China in 2005. Currently, he is an Associate Professor in the Department of Control Science and Engineering, HUST and his current research interests include robotic assembly, networked control systems, and bioinformatics. Baiqing Sun received his PhD Degree from Kochi University of Technology (KUT), Japan in 2006 and since November 2010 he has been an Associate Professor at the Electrical Engineering School of SUT. His main research interests include flexible control of assembly robots, design and control of intelligent actuators, and artificial intelligent systems. Hironobu Sasaki received the Doctor of Engineering Degree from Tokyo Metropolitan University in 2009. He had been a Researcher on GCOE at the Department of Micro-Nano System Engineering in Nagoya University until June 2009. His main research areas are robotics engineering, intelligent system engineering, sensor networks and image processing. Toshio Fukuda received the BS Degree from Waseda University, Tokyo, Japan, in 1971, the MS Degree in 1973, and the PhD Degree with a dissertation entitled “Malfunction diagnosis and application of stable adaptive schemes for a nuclear reactor system”, in 1977, both from the University of Tokyo, Japan. From 1977 to 1982, he was with the National Mechanical Engineering Laboratory, Tsukuba, Japan. From 1982 to 1989, he was with the Science University of Tokyo. Since 1989, he has been with Nagoya University, Japan, where he is currently a Professor in the Department of Micro-Nano Systems Engineering. His current research interests include intelligent robotic systems, cellular robotic systems, mechatronics, and micronanorobotics. Dr Fukuda was the President of the IEEE Robotics and Automation Society (1998C1999), the Director of the IEEE Division X, Systems and Control (2001C2001), and the Editor-in-Chief of the IEEE/American Society of Mechanical Engineers Transactions on Mechatronics (2000-2002), and the President of the IEEE Nanotechnology Council (2002-2005). He is the AdCom President of the IEEE Nanotechnology Council. To purchase reprints of this article please e-mail: [email protected] Or visit our web site for further details: www.emeraldinsight.com/reprints