real-time agv action

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European Journal of Scientific ResearchISSN 1450-216X Vol.25 No.2 (2009), pp.310-324 EuroJournals Publishing, Inc. 2009http://www.eurojournals.com/ejsr.htm

Real-time AGV Action Decision in AD-FMS byHypothetical Reasoning

Rizauddin Ramli Department of Mechanical and Materials Engineering, Faculty of Engineering, Universiti

Kebangsaan Malaysia, 43600 UKM Bangi, Selangor, MalaysiaE-mail: [email protected]

Tel: +60-3-58917022; Fax: +60-3-89259659

Hidehiko Yamamoto Department of Human and Information Systems Engineering

Faculty of Engineering, Gifu University, 501-1193 Gifu-shi, Japan

Abu Bakar Sulong Department of Mechanical and Materials Engineering

Faculty of Engineering, Universiti Kebangsaan Malaysia43600 UKM Bangi, Selangor, Malaysia

Dzuraidah Abdul Wahab Department of Mechanical and Materials Engineering

Faculty of Engineering, Universiti Kebangsaan Malaysia43600 UKM Bangi, Selangor, Malaysia

Jaber Abu Qudeiri Department of Mechanical Engineering, Faculty of Engineering

Philadelphia University, Amman 19392, Jordan

Abstract

In this study we present an approach of hypothetical reasoning for action decisionof Automated Guided Vehicle (AGV) in Autonomous Decentralized in FlexibleManufacturing Systems (AD-FMS). The AD-FMS is characterized as being online, in real-time mode and of a short-term nature that responds to frequent changing of the productionorder. The decentralized control in AD-FMS enables to solve dynamically some typicaltask of production system without using a fixed centralized control system. We adopt ahypothetical reasoning approach that will decide the conceivable next action from thecompetition hypothesis. Simulation results show that the efficiency of AGV in AD-FMSincreased.

Keywords: Autonomous Decentralized, Flexible Manufacturing Systems, AutomatedGuided Vehicle, Hypothetical Reasoning


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Real-time AGV Action Decision in AD-FMS by Hypothetical Reasoning 311

1. IntroductionToday, in order to survive in the rapid challenging environment of the modern manufacturing era,manufacturers are forced to adopt new technologies, especially for products that are made in small

batch production. Product and process improvements are widely acclaimed to provide economics gainsand to increase flexibility, resulting in fast response and rapid adaptability to changing marketcondition. However, most of the production facilities are made as a complex dynamical environment,which is always plagued by unexpected situations. Equipments may break down and an unexpectedurgent job may suddenly be released to the production line, depends to the demands of the clients.Also, the priority of the job may be changed. Consequently, the aspect of flexibility in manufacturingsystem becomes an essential point in dealing with the unexpected situations.

Basically, flexibility is an attribute of contemporary manufacturing systems which isnecessitated by the time-based competition underlying current manufacturing strategy. As a result, aFlexible Manufacturing System (FMS) provides an alternative to improve the situation [1-4]. Later, theadvancement in numerical control (NC) and computer technology has made lightly mannedmanufacturing system possible. Conventionally, the FMS are equipped with several CNC machinestools, automated warehouses and Automatic Guided Vehicle (AGV). An AGV based material handlingsystem is designed and implemented to gain production the flexibility and efficiency [5-7]. However,even if the FMS are able to deal with the unexpected situations, most of FMS are still leaning on thecentralized control system. A host computer controls and gives instruction to each agents by a pre-decided rule or route scheduling, i.e. what is the next action they should do after performing the presenttask.

So far, the routing algorithms for AGV are often divided by either a centralized approach or adecentralized approach. For a centralized approach, the route planning of AGV systems is determined

by centralized decision making, which handles the entire system [8]. The Petri Net approaches [9-11]are a useful way to analyze the conditions to avoid deadlock in AGV systems. Dispatching algorithms[12-13] and Genetic Algorithms (GA) [14-17] have also been studied to cope with AGV routing

problems.In the autonomous decentralized system, the AGV routing is generated by several decision

making subsystems. One of the approaches is zone control [18, where the AGV system can be dividedto several non-overlapping regions, which restricts the available AGV for a time. Nishi et al . [19] have

proposed a distributed routing method for multiple mobile robots using a Lagrangian decompositionand coordination technique. The original problem is decomposed into an individual routing problemfor each AGV. Most of the conventional research on autonomous decentralized real time schedulingsystems for AGV are based on agent decision selection and object orientation method [20-23]. In themethod, the fastest action that can be finished at the existing time is selected as the action that should

be taken for the agent.Therefore, in this paper, the concept of Autonomous Decentralized [24-27] in Flexible

Manufacturing Systems (AD-FMS) is introduced. It is to realize that each agents in FMS such asmachine tools and AGVs run independently from each others. The AD-FMS architecture has thefeature that every agent has autonomy to manage itself and coordinates with the other agents.

Furthermore, we adopt a concept of hypothetical reasoning to retrieve whether the action taken by theagent is the truth or false and use the hypothesis for the next action decision.

Consequently, the proposed coordination between agents is achieved by communication withother agents through a proposed intelligent knowledge (IK), in which the information of the agentcirculates or transmits to another agent and after receiving the information; the other agents analyze itscontent to proceed for the next action decision.


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312 Rizauddin Ramli, Hidehiko Yamamoto, Abu Bakar Sulong, Dzuraidah Abdul Wahab andJaber Abu Qudeiri

2. Architecture of AD-FMS2.1. Conventional FMS

Conventionally, FMS is a computer-controlled configuration of semi-independent work stations andmaterial handling systems designed to efficiently manufacture more than one type of parts at a low to amedium volumes [28]. There are three essential physical components of FMS:

Numerical control (NC) machine tools Conveyance network or material handling system FMS control system

The NC machine tools do not only consists of NC machining centers but it also may compriseany of the machining units in the FMS such as NC lathe machines, turning machines, etc. On the other hand, the conveyance network or the material handling system such as AGV functions as a device totransfer the work piece between the parts warehouse, product warehouse and machining centers.

The FMS control system performs as a centralized host computer that controls the sequence andcoordinates all the task flow for every machine tool, parts handling system and the work pieces. Figure1 depicts the conventional FMS control system giving all instructions to other sub-level controlsystems, i.e. the machine control system, station control system and transportation control system.Then, these sub-level control systems command the equipment in the FMS to do their task based on the

pre-decided sequence or schedule. The disadvantage of this case is once the centralized host fails tocommunicate with the sub-level control system, the operation of FMS is terminated due failure of coordinating the equipment. In order to overcome this problem, a better control system which does notdepends to a centralized oriented structure is needed, i.e., a decentralized control system where everyelement in the FMS are independently and flexible to decide their own decision throughcommunication, exchanging information and cooperation among them.

Figure 1 : Schematic hierarchy of FMS control system

MC-2

FMS ControlSystem

Production ControlSystem

CAD/CAM

MC-1

Station ControlSystem

ConveyanceControl system

Part W-housestation

MC ControlSystem

AGV

AutomaticW-house

3-D measurementMC

Automatic PalletStocker

Jig& Tool station

MC-2

FMS ControlSystem

Production ControlSystem

CAD/CAM

MC-1

Station ControlSystem

ConveyanceControl systemConveyanceControl system

Part W-housestation

MC ControlSystem

AGV

AutomaticW-house

3-D measurementMC

Automatic PalletStocker

Jig& Tool station


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2.2. Concept of AD-FMS

The AD-FMS is based on the Autonomous Decentralized (AD) that was derived from the analogy of living organisms. Each living thing is composed of cells and each cell is independent in the body.These cells are totally self sufficient for the information for living and multiplication. In our proposedAD-FMS, these cells are analogous to autonomous multi-agents systems. Any communications

between these agents are carried between themselves without routing through a centralized system. In

Figure 2, a schematic view of information sharing between the autonomous agents of the AD-FMSstructure is shown. It shows the agents which are AGVs and machine tools communicate and exchangetheir information via a wireless communication system. By this way, they are able to estimate theability of themselves, which machine tools are busy and leisure; so that the AGV can decide whichmachine tools should the parts to input, or which machine tools are finished processing the parts.Furthermore, through the coordination between AGVs, they can avoid the collision betweenthemselves or to prevent deadlock that can decrease the efficiency of the FMS.

Consequently, the AD-FMS can be realized with the multi-flow of information from the anylevel of computer. For example, once a daily production command is given every control system willautomatically schedule their job scheduling based on it. By this way they transmit the informationthrough LAN system to the other computer system and to the station controller. As shown in Figure 3,the station controller is then autonomously give instruction to the agents, which are the AGVs,machine tools and automatic warehouse about the job scheduling and keep coordinating them online.

Figure 2 : Information sharing between autonomous agents

Communicate-Exchange-Cooperate

AGV AGV MCMC

Communicate-Exchange-Cooperate

AGVAGV AGVAGV MCMCMCMC

With this architecture, it can be realized that in the AD-FMS, there are no specific centralizedhost controller and no relation of master-slave among the multi-agents. However, few criterias of AD-FMS should be satisfied in order to gain a full efficiency of AD-FMS that are:

On-Line ExpansionAs the AD-FMS size increases, a step by step construction is required. Even after completion of construction, the function in the AD-FMS may be are added or removed.

On-Line MaintenanceIn the AD-FMS, it is possible that frequency of fault occurrence somewhere in the system will

be increased. Due to this, it should be ensured that the maintenance and the test procedures can be carried out without suspending the operation of the AD-FMS itself, especially in the case of on-line and real-time systems.

Fault-ToleranceThe hardware in the AD-FMS must be reliable to compete with faulty. The hardware in theAD-FMS should be more sufficiently improved in comparison with the software. However,


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even if the software includes some bugs, the system is not required to stop its entire operationto prevent the fault.

PerformanceIn AD-FMS, a high performance of control systems is required. In order to attain the high

performance control system, reducing the peak of the computer load and making the loadsmooth by improving the software processing are required.All this criteria should be met in the construction and operation of new AD-FMS. However, it

is difficult to satisfy all these criteria. In this paper, we focused at the forth criteria that is to improvethe software processing by introducing the concept of hypothetical reasoning in order to relieve theload of software processing.

2.3. Difficulties of Realizing AD-FMSIn AD-FMS, the larger scale of product demands will result to a huge combination of parts varieties,

job scheduling, maintenance and control systems of every agent. Due to this, it is necessary to optimizethe huge combination efficiently so that the productivity in the AD-FMS will be enhanced. For instance, a manufacturer without AD architecture in their FMS will face difficulties to cope with thecustomer demands if one of the AGV caused trouble. Most of the Japanese manufacturers practice theconcept of Just in Time (JIT) that ensures the needed product with the needed volume in the timescould be delivered to the customer without any delay [29]. The failure of it will cause a big problem tothe customer and lost the trust for the manufacturer. That is why a robust AD-FMS architecture isneeded.

Figure 3: Distribution of task by Controllers

Computer

Controller Controller

Controller

Controller

Computer

AutomaticWarehouse

MachineTool

MachineTool

AGV

Controller

AGV

MachineTool

Controller

Computer

Controller Controller

Controller

Controller

Computer

AutomaticWarehouse

MachineTool

MachineTool

MachineTool

AGVAGV

Controller

AGVAGV

MachineTool

MachineTool

Controller

However, it is not an easy task to develop a robust AD-FMS even now if we have the ability of an efficient computer. This is because it is considerably a difficult task to obtain the optimal solutionfor all combinations. For instance, if the scheduling job is one of the combinations problems, thecomputer can enumerate all the solution by its candidates and search for the best solution from among


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Figure 4: Model of AD-FMS

A G V 1

M C 1 - 1 M C 2-1 M C 5-1 M C 1 - 2 M C 3 - 2

A G V 2 A G V 5

M C 3 -1 M C 4-1 M C 6-1 M C 2 - 2 M C 1 - 3

M C 7 - 1 M C 8 - 1 M C 7-2 M C 2 -3 M C 4 - 2

M C 3 -3 M C 5-2 M C 4-3 M C 8 - 2 M C 6 - 3

M C 6-2 M C 5 -3 M C 8-3 M C 7-3

A G V 3 A G V 4

ProductsWa r e h o u s e

( P RW )

Par t sWa r e h o u s e

( PAW )

A G V 1

M C 1 - 1 M C 2-1 M C 5-1 M C 1 - 2 M C 3 - 2

A G V 2 A G V 5

M C 3 -1 M C 4-1 M C 6-1 M C 2 - 2 M C 1 - 3

M C 7 - 1 M C 8 - 1 M C 7-2 M C 2 -3 M C 4 - 2

M C 3 -3 M C 5-2 M C 4-3 M C 8 - 2 M C 6 - 3

M C 6-2 M C 5 -3 M C 8-3 M C 7-3

A G V 3 A G V 4

ProductsWa r e h o u s e

( P RW )

Par t sWa r e h o u s e

( PAW )

The information exchange and cooperation between each agent in this AD-FMS is described asfollows. The PAW sends the information on the names of the parts that are stored in the PAW.Meanwhile, the AGV transmits the information of the name of the parts that it brings and thedestination of where it is going. The MC gives information of the name of parts that is currentlymachined and the time remaining to finish the machining process. The PRW sends information to thePAW of what product is kept in it and which product has been sent to customers. By this way, thePAW can understand of the current logistic condition so that it can prepare the next parts that should beinput to the FMS. In other words, the information exchange between agents in the AD-FMS is used bythe needed agent as a source to decide the next action.

In AD-FMS, the full usage of information source among MC, PAW, PRW and AGVssimultaneously will result to the realization of enhancing production efficiency [30]. In theconventional FMS, the action planning of the AGV action is done by a pre-decided scheduling systemthat does not consider an unexpected problem such as MC troubles or machining delay time. Once thisunexpected trouble occurs, the production plan needs to be re-scheduled. Furthermore, in the case of anAD-FMS where many MCs and AGVs are mixed together, it is difficult to schedule an effectiveinstruction for the AGV about where it should go and which parts should be input.In this paper, the processing procedures that we adopt are described as follows;

The usage of information from each agent The inference of a few steps of AGV action The forecasting of the AD-FMS operating condition.In order to implement these procedures, we propose an algorithm which is able to forecast the

next action decision in the real-time production scheduling of the AGV that is based on hypotheticalreasoning. Hypothetical reasoning is the activity of evaluating the effect of the actions that affect agiven domain that is now an established subfield of knowledge representation [31-35. The algorithmthat we proposed is able to forecast the next action decision in the real-time production scheduling of


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AGV that we called as Future Anticipative Reasoning Algorithm (FARA) which is based onhypothetical reasoning method. The FARAs real-time production scheduling is performed by 2 typesof the hypothetical reasoning; the first hypothesis is the Action Decision Hypothetical Reasoning(ADHR) that decides where the AGV will move to and the second hypothesis is the Parts InputHypothetical Reasoning (PIHR) that decides the kinds of parts to be input onto the production floor.

Figure 5: Action Planning of AGV

MC1-1 MC1-2 MC2-1 PAW MC3-1PR W

MC1-2MC1-1

Next Action

CurrentPosition

The FollowingAction

PRW: Product W arehouse

PAW: Part Warehouse

MC1-1 MC1-2 MC2-1 PAW MC3-1PR W

MC1-2MC1-1

Next Action

CurrentPosition

The FollowingAction

PRW: Product W arehouse

PAW: Part Warehouse

The next action decision for AGV is related to many reasons, such as the existence of manyMCs with the same machining process, the transportation of product to product warehouse, the input of new parts to the AD-FMS, the existing of other AGV that are doing the same action, etc. Due to thesereasons, the action decision necessitates not only a single action decision but could be a multi action

decision that is based on the action decision selection branch. Attentively, if the AGV selects onechoice from the selection branch and then based on the selected branch, each of the agents inside theAD-FMS is given moving and working instructions.

Furthermore, when the AGV meets the condition that is required to do the selection again, thenit will re-select one of the choices from the action decision selection branch. In this way, the operatingcondition in AD-FMS is realized by the continuous process of selecting the AGV next action decision.In other words, as shown in Figure 5, the operating condition of AGV is eternally broadening like atree structure, where the node is assumed as the next AGV action.

Figure 6 shows the outline of the proposed hypothetical reasoning process. The tree structureshows the form of retrieval vertically. In every stage of the tree structure, the retrieval will be doneuntil it finds FALSE results and it will return to one stage back and start the retrieval process again at

another selection branch. The peak of the tree structure is set as the last AGV action with thehypothesis depth 0 and the hypothesis depth under this stage represents the action that is taken by theAGV. Similarly, the hypothesis with depth 1 has its own selection branch under it with all of themhave their own selection branch respectively. In Figure 6, the selection branch and the arrowsconnecting the selection branch shows the hypothetical reasoning simulation process of FARA. Thehypothetical reasoning simulation runs as if the end of the arrow is TRUTH, then by doing the task following the selection branch, it will display the result of the simulation.

The algorithm of the hypothetical reasoning simulation is performed through the followingsteps:Step1: The existing AGV hypothesis depth is set as 0.


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3.3. AGV with Intelligent KnowledgeIn this paper, we consider the AGVs as intelligent agents that are able to adopt knowledge, transmittheir information to each other and understand AGVs behaviour. If one AGV can understand the

behaviour of another AGV, it is possible to avoid their collision, and to cooperate in their task together.Here, we define each AGV as having 6 types of intelligent knowledge, i.e., Routing knowledge, Self knowledge, Sending knowledge, Others knowledge, Answer knowledge and finally Avoidance knowledge. These 6 types of knowledge are divided into 2 types of memories: long term memory andshort term memory. Sending, Answer and Avoidance knowledge are kept in the short term memory,while Routing knowledge, Self and Others knowledge are kept inside the long term memory.

Figure 7 shows the Self knowledge. The first parameter and second parameter indicates thename and emergency command of the specified AGV respectively. The third, fourth and fifth

parameter indicates the position of the location that the AGV has just passed, the next position that theAGV will go to and the next position after that. The Others knowledge has the same characteristic withthe Self knowledge.

The Answer knowledge has 4 parameters, i.e., the AGVs name, emergency command, thename of AGV opponent and the next position that the AGV will go. All of the information aretransmitted to other AGV, so that they can share and exchange their information to avoid collision

between them. By this way, all the AGV can reduce their collision and as well as can avoid deadlocks

that will decrease the production efficiency.

Figure 7: Self knowledge

AGV1st para 2nd para 3rd para 4th para 5th para

para: parameter

4. SimulationsThe production floor is a 60m 60m square. (The parts warehouse and the product warehouse arelocated outside of the floors). The distance that AGV are able to move is 5 m from the inside floor wall. It is assumed that the entrance to the parts warehouse and the product warehouse use the shortestroute from the grid. Furthermore, the positions of MCs are located at the edge of the grid of the AGVroute.

In this research, in order to facilitate the dynamic AD-FMS simulation, the following

assumptions are made. The maximum number of AGV is 5. The AGV moves on the grid of the floor, and thetravelling speed of AGV is constant, but it depends on the type of carried parts and

products. The maximum types of MCs are 8 types and the maximum numbers of same type of MC

are 3 types. The position is assumed as entrance of parts handling position. The maximum numbers of parts types are 9 types with each types can only be processed

maximum 8 times.In this paper, 3 kinds of simulation conditions were performed to ascertain the effectiveness of

our proposed algorithm with AGV-wIK. The position of parts warehouse, product warehouse, and theMCs on the production floor are configured as Figure 8, Figure 9 and Figure 10, respectively. The


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position of MCs (represents by ) and numbers of AGVs are changed in each different simulationconditions. Each simulation time is 8 hours and the numbers of AGV used in each simulation conditionare 3:4:5. In order to verify the effectiveness of AGV-wIK, we ran simulations with the same conditionwithout intelligent knowledge (i.e. Conventional Method) where the moving destination is decided andfixed.

(a). Simulation 1: AD-FMS with 9 Machine ToolsIn Simulation 1, we ran 3 types of simulations (S1-1, S1-2 and S1-3) with different numbers of AGVare performed with the following conditions: 3 types of products with the rates of each product and

production ratios as P1:P2:P3=5:6:3.

Figure 8 : MCs position in Simulation 1

PAW

PRW

(b). Simulation 2: AD-FMS with 18 Machine ToolsIn Simulation 2, we ran 3 types of simulations (S2-1, S2-2 and S2-3) with 6 types of products with thefollowing conditions: The rates of each product and production ratios as P1:P2:P3:P4:P5:P6=5:6:3:3:2:1.

Figure 9 : MCs position in Simulation 2

PAW

PRW


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(c). Simulation 3: AD-FMS with 24 Machine Tools

Figure 10: MCs position in Simulation 3

PAW

PRW

In Simulation 3, we ran 3 types of simulations (S3-1, S3-2 and S3-3) with different numbers of AGV under the following condition: 9 types of products with the rates of each product and productionratios as P1: P2: P3: P4: P5: P5: P6: P7: P8: P9 = 5: 6: 3: 3: 2:1: 4: 5: 2.

In Figure 11(a), each Simulation 1, Simulation 2 and Simulation 3 indicates that the efficiencyof AGV becomes better with AGV-wIK than using the Conventional Method. Similar results obtainedwhere the number of AGV collisions are reduced as shown in Figure 11(b). This proved that our

proposed technique works effectively.

5. ConclusionsIn this paper, we proposed a technique of forecasting the next action of AGV that includes the advance

prediction of action in few steps, which will enable to enhance the efficiency condition of theautonomous decentralized flexible manufacturing systems. The technique, Future AnticipativeReasoning Algorithm or FARA is used to forecast the next action decision of AGV. By using FARA,we adopt a hypothetical reasoning technique that will decide the conceivable next action from thecompetition hypothesis. Simulation results show that the efficiency of AGV in AD-FMS increased.The numbers of collisions are also decreased, which means we can obtain a proper navigation for theAGV. It confirmed that our technique is useful in collision avoidance.

Figure 11: Results in each simulation conditions

0

10

20

30

40

50

60

70

S1-1 S1-2 S1-3 S2-1 S2-2 S2-3 S3-1 S3-2 S3-3

Simulation Conditions

A G V e f

f i c i e n c i e s

( % )

Conventional MethodAGV-wIK

0

10

20

30

40

50

60

70

S1-1 S1-2 S1-3 S2-1 S2-2 S2-3 S3-1 S3-2 S3-3S1-1 S1-2 S1-3S1-1 S1-2S1-1 S1-2 S1-3 S2-1 S2-2 S2-3S2-1 S2-2S2-1 S2-2 S2-3 S3-1 S3-2 S3-3S3-1 S3-2S3-1 S3-2 S3-3


A G V e f

f i c i e n c i e s

( % )

Conventional MethodAGV-wIK Conventional MethodAGV-wIK Conventional MethodAGV-wIK


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(b) Comparison of number of collisions between AGVs

0

2 0 0

4 0 0

6 0 0

8 0 0

1 0 0 0

1 2 0 0

1 4 0 0

S1-1 S1-2 S1-3 S2-1 S2-2 S2-3 S3-1 S3-2 S3-3


N u m

b e r o f c o l

l i s i o n s

Conventional MethodAGV-wIK

0

2 0 0

4 0 0

6 0 0

8 0 0

1 0 0 0

1 2 0 0

1 4 0 0

S1-1 S1-2 S1-3 S2-1 S2-2 S2-3 S3-1 S3-2 S3-3

0

2 0 0

4 0 0

6 0 0

8 0 0

1 0 0 0

1 2 0 0

1 4 0 0

S1-1 S1-2 S1-3 S2-1 S2-2 S2-3 S3-1 S3-2 S3-3S1-1 S1-2 S1-3S1-1 S1-2S1-1 S1-2 S1-3 S2-1 S2-2 S2-3S2-1 S2-2S2-1 S2-2 S2-3 S3-1 S3-2 S3-3S3-1 S3-2S3-1 S3-2 S3-3


N u m

b e r o f c o l

l i s i o n s

Conventional MethodAGV-wIK Conventional MethodAGV-wIK Conventional MethodAGV-wIK

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