AMADEUS: A Mobile, Autonomous Decentralized Utility System for Indoor Transportation

Proceedings of the 1998 IEEE InternationalConference on Robotics & Automation Leuven, Belgium May 1998 AMADEUS: A Mobile, Autonomous Decentralized Utility System for Indoor Transportation i (? Toru Kamada and Koichi Oikawa Fujitsu Laboratories Ltd. 10-1 Morinosato-Wakamiya, Atsugi 243-01, Japan E-mail: tkamada@lab,fhjitsu. co.jp Abstract The authors have developed a mobile, autonomous decentralized utility system for indoor transportation, called AMADEUS. This paper explains how we put it to practical use. AkL4DEUS is a system designed around transportation agents. We eliminated centrally managed vehicle allocation and routing plans by implementing autonomous vehicle allocation negotiations and collision avoidance between agents. The transportation agent k collision avoidance function enables the hi-directional movement of vehicles on a single-track, thereb.v making efficient, space-saving transportation possible. This paper introduces the concept of AMDEUS and describes the transportation agent architecture by focusing on installing the collision avoidance function. 1. Introduction In factories producing personal information units such as personal computers and portable telephones, production orders are currently increasing because of shorter product life cycles and reduced inventories. The increase in production orders has led to frequent changes in the types and amount of products manufactured by these factories. To cope with this situation, factories are changing their production systems from conventional conveyer lines to production cells. In the cell production method, a work process is usually completed within a small U-shaped cell. Unlike conventional conveyer line methods, this method enables high productivity for multiple-product manufacturing and easy changes in the number and layout of cells that may occur due to changes in the scale of production. To support the cell production method, a transportation system for supplying parts to and removing assemblies or products from the cells is required to: Link arbitra~ cells with optimum routes at the appropriate times. Flexibly cope with changes in transportation environments such as changes in the number and layout of cells. On a shop floor using the cell production method, cells are densely arranged and passages are narrow to minimize travel distances and the required floor space, A conventional automatic guided vehicle (AGV) transportation system used with the cell production method could only have a single-track and a fixed transportation direction. With this transportation system, vehicles can only follow a predetermined route, thus leading to low transportation efficiency Moreover, conventional transportation systems must cope with potential changes in the number of AGVS and the transportation route, which require complex calculations [1,2] to allocate vehicles to the cells and determine vehicle routes. Modi@ing the complex management programs used for the transportation system requires enormous amounts of time and money. Therefore, we focused our attention on an autonomous decentralized transportation system, because it features: - AGVS having a collision avoidance (when passing each other) function on a single-track. AGVS that negotiate with cells regarding vehicle allocation, This system supports bidirectioml transportation, thus assuring a high transportation efficiency. Each AGV can autonomously move to a pre-negotiated target cell without colliding with other AGVS. This could eliminate conventional vehicle allocation and routing plans, allowing the centralized management system to be eliminated. Results on the experimental performance of autonomous decentralized robot systems have been reported, but practical systems have been difficult to contlgure [3-5]. This is because before developing practical systems, it is necessa~ to make agents autonomous enough to cope with complicated, ever-changing real work environments, and to produce agents at a practical cost. In other words, it is necessa~ to develop highly adaptive AGVS that can evade moving obstacles and are comparable in cost to conventional AGVS. The authors tried to solve this problem by introducing a behavior-based approach [6] to the hardware on the same functional level as that for ordimry AGVS, because this approach could be used to realize sophisticated behavior by means of simple low-priced sensors. However. The attempt to install behavior in collision avoidance systems involves difficulties due to the coexistence of various types of target behavior in transportation work aspects. For this reason, the need arose for a new architecture that would prepare different behavioral styles 0-7803-4300-x-5/98 $10.00 @ 1998 IEEE 2229

for each aspect and provide atransition between behavioral styles. Now that the concept of AMADEUS (A Mobile, Autonomous Decentralized Utility System) has been covered, this paper will now describe the architecture used to make AGVS autonomous. 2. Concept of AMADEUS Figure 1 shows the overview of AMADEUS. In this system, two different types of distributed agents, autonomous AGVS and cells, cooperate in transporting objects. There is no central fimction to manage and control these agents. The major configuration and fhnctions of AMADEUS are as follows: 1) AMADEUS consists of dynamic agents, called mobile agents, for transporting objects from cell to cell, and another type of agent, called cell agents, for supplying and removing objects to each cell. 2) Cell Agents and mobile agents communicate with one another via a contract net protocol [7] to allocate mobile agents to transportation tasks occurring in cells. 3) When a mobile agent is assigned a transportation task, it runs along a single-track in either direction to the target address, If it encounters an obstacle (such as a person, another mobile agent, or an object), it temporarily moves away from the track s guideline to pass the object. In areas where it is difficult to avoid collisions, such as in the vicinity of a cell station or a narrow passage, mobile agents negotiate with one another to agree on which will enter the area. Task $Iovation -d Mobile agent 0~ Mobile agent 1 Figure 1. Concept of AMADEUS. Implementing the cotilguration and fimctions stated above attains efficient, hi-directional transportation and eliminates the need to execute centrally managed vehicle allocation and routing plans. 2.1 Agents The two types of agents that comprise AMADEUS have the following fimctions and roles: 1) Mobile agents Mobile agents use a contract net protocol to negotiate with cell agents about vehicle allocation to acquire transportation tasks or target cell addresses. Once a mobile agent acquires a target address, it determines its direction of travel according to its current position indicated by a signpost on the floor and its own map and then starts moving toward the target cell. If a mobile agent encounters an obstacle, it temporarily leaves the track s guideline to pass the obstacle. After it passes the obstacle, it returns to the guideline and proceeds, Moreover, a mobile agent can avoid collisions with other mobile agents by negotiated cooperation, 2) Cell agents Whena transportationrequestoccursin a cell, a cell agent uses contract net protocol to negotiate with mobile agents to determine vehicle allocation and assigns the transportation task to the unassigned mobile agent that will most quickly be able to receive the object to be transported. 2.2 Task allocation The contract net protocol shown in Figure 2 is used to assign transportation tasks to mobile agents. It works as follows: 1) Task aqo-ncement ez<l ~,,:;~ / ~ obile agent O 0 e agent Figure 2. Task allocation with contract net protocol, 1) Task announcements by a cell agent When a transportation request occurs in cell zero, as shown in Figure 2, the cell agent in charge of cell zero presents the transportation task to all mobile agents by radio. 2) Bids by mobile agents For the presented transportation task, each mobile agent bids on the time required for it to get to cell zero by radio. If a mobile agent is not executing a transportation task, its bid is simply the time required for it to move from its current position to cell zero. If a mobile agent is currently executing a task, its bids is the total time required for it to complete the present task and that required for it to move from its completion position to cell zero. 3) Contract Cell agent zero compares all bids to select the mobile agent that can satisfy the transportation request in the shortesttime, now known as mobile agent zero, and concludes a transportation contract with mobile agent zero by sending address zero of cell zero to the mobile agent. If mobile agent zero is not executing a transportation task, it startsfor address zero. If it is already executing a task, it finishes it before starting for address zero. 2230

2.3 C-ollisiom avoidance AMADEUS does not use centrally managed routing plans to avoid collisions between mobile agents. Instead, mobile agents autonomously avoid collisions using either of two methods. The first method is used in passageways that are wide enough for at least two mobile agents to pass. The second method is used in areas where it is dtikult for two mobile agents to pass each other, such as in the vicinity of cell stations and in narrow passageways, 1) Passing each other Each mobile agent is provided with a collision avoidance fimction to anticipate situations in which it may run across an oncoming mobile agent on a single-track s guideline. With the collision avoidance fimction, the mobile agent leaves the guideline temporarily to avoid collision with the oncoming mobile agent and, after the oncoming mobile agent has passed, returns to the guideline, as shown in Figure 3, The collision avoidance function is also effective when the mobile agent encounters a person or other obstacles. Mobile agents keep to the right or left (whichever is specified) to prevent deadlocks. Research results on how to best enable mobile robots to pass each other have been reported [8,9]. However, there is no practical system that enables mobile objects to pass each other on a single-track guideline. Go along < Figure 3. Passing each other on a single track guideline. 2) Negotiation When mobile agents are in an area where it is difficult for them to pass each other, such as in the vicinity of a cell station or in a narrow passageway, they negotiate with each other by radio. In the example shown in Figure 4, when mobile agent zero attempts to return from a cell station, it sends its address to other mobile agents. If mobile agent one receives the address of mobile agent zero, it reciprocates by sending its own address and direction of travel. On receiving this information, mobile agent zero recognizes that there is an oncoming mobile agent and waits at the cell station. Mobile agent one makes a similar decision. It memorizes that it is keeping mobile agent zero waiting. After passing an area of contention, mobile agent one allows mobile agent zero to enter the area by sending information by radio. Thus, collisions can be assuredly and efficiently avoided in areas of contention. - Area of problem Mobile agent 1 Address sign Guide tine Figure 4, Negotiating in thevicinity of cell stations 3. Mobile Agent Architecture To realize collision avoidance between mobile agents as described in Section 2, it is necessary to respond to changes in situations flexibly and quickly. However, to produce mobile agents at a practical cost, we must select sensors with local sensing functions only. For mobile agents that cannot grasp their environment in a perspective to respond to unexpected changes in environments flexibly and quickly, it is necessary to act positively, precisely, and quickly by responding to the ever-changing situations around them. To meet this requirement, mobile agents use a behavior-based approach, Behavior-based architecture concurrently processes behavior modutes represented by finite state machines by setting up a suppressing relationship. So it is necessary to prepare behavior modules that suit each situation and set up a suppressing relationship specific to each behavior module. For a situation where collision avoidance is needed, for example, it is necessa~ to prepare behavior modules for avoiding collisions, for running along the guideline, and for returning to the guideline, and set up suppressing relationships according to the priority among these behavior modules. In addition to the behavior styles described so fm, mobile agents must have various other behavior styles that can support diverse aspects, such as a behavior style (a) for detecting a target address and entering the corresponding station, a behavior style (b) for receiving a new target address and leaving the station. and a behavior style (c) for detecting an intersection sign and moving in the target direction (see Figure 5). Figure 5. Behavior styles of mobile agent The types and number of behavior modules to be activated in each aspect va~ from one behavior style to another. When a mobile agent is entering a station, for ex- 2231

ample, a behavior style for collision avoidance would detect the target station as an obstacle and try to divert the mobile agent from the station rather than causing it to approach the station. When entering a station, a mobile agent must use a behavior module for stopping instead of the collision avoidance behavior module. To solve these problems, research on managing the actions of multiple mobile robots [10,11] has resulted in a hierarchical architecture in which high-order layers cooperate to solve problems. This architecture has a problem regarding real-time operation, however, because many steps are needed to exchange information among layers. Some other methods [12, 13] group behavior modules for a particular aspect into a behavior set on a higher level and share behavior modules on a lower level by imposing suppressing relationships as required. If there are many behavior modules, however, these methods degrade in real-time response due to an increase in the number of execution steps, because finite state machines must be executed to suppress the operation of behavior modules on lower levels. In addition, low-level behavior modules that are seemingly shareable among different aspects are actually difficult to be shared in real environments, because it is necessary to precisely adjust the handling of sensor signals and the operation of actuatorsfor each aspect. The architecture we developed is based on the concept that the behavior module to be activated belongs to a specific aspect, and so sharing even low-level behavior modules would be dillicult. Therefore, this architecture prepares behavior modules specific to a unit of behavior for each aspect and sets up a suppressing relationship specitlc among them. This unit of behavior is hereafter called a behavior style. Figure 6 shows the mobile agent architecture. This architecture activates a behavior style that corresponds to a specific aspect according to communication between agents and information from sensors, by using a finite state machine called an activator. To be more specific, a transition among the states of the activator activates a behavior style. Once the behavior style is activated and achieves its goal, it becomes inactive by resetting its indicator, causing another behavior style to be activated. Deactivated l-++ Behavior style n Activator,, Actuators 0 Behavior style O 1 * Figure 6. Architecture of mobile agent. This way, each behavior style can be contlgured with a minimum number of behavior modules, thus improving its real-time characteristics. This method can also make it easier to create programs in a modular structure, thus enabling suppressing relationships to be set up among behavior modules for each behavior style without affecting other behavior styles. In addition, it is possible to tailor behavior modules for individual aspects, thereby avoiding functional trade-offs that would be result from sharing behavior modules among behavior styles. 3.1 Behavior styles The mobile agent architecture features the use of multiple behavior styles, each of which is tailored to a specific aspect. No behavior module is shared among behavior styles. Instead, each behavior module has a completely independent structure. This conf@ration eliminates some control steps, thereby improving the real-time characteristics of the behavior style. Moreover, providing behavior modules spectilc to each behavior style enables the tailoring of each behavior module to meet the requirements of the corresponding behavior style. Figure 7 shows the major behavior styles necessary to AMADEUS. 1) Normal-running:In the normal-running behavior style, a mobile agent runs along the guideline and leaves it when an obstacle is encountered. After avoiding the obstacle, the mobile agent steers in the direction opposite to that taken when leaving the guideline according to its records. thereby moving back toward the guideline. Once the mobile agent returns to the guideline, it resumes its original course along the guideline (Figure 7a). 2) Approaching-station:The approaching-station behavior style is used when a mobile agent enters a station (Figure 5a). This behavior style is activated when a mobile agent detects a target address according to a signpost and diverts itself to the station. On detecting the guideline leading to the station, the mobile agent moves along the guideline and stops when it detects another signpost. When it completes its positioning, it resets the indicator of the activator to become inactive (Figure 7b). 3) Leaving-station: The leaving-station behavior style applies to when a mobile agent leaves a station (Figure 5b). On receiving a new target address. the mobile agent is activated and returns to the guideline. The mobile agent searches for the main guideline to return to it. On detecting the guideline, the mobile agent resets the indicator of the activator to become inactive (Figure 7c). 4) Changing-course:The changing-course behavior style is for when a mobile agent changes its course at an intersection (Figure 5c). Comparing information from an intersection sign with the target address to alter its course activates the mobile agent. After being diverted to a preset course, it proceeds. On detecting a guideline, the mobile agent resets the indicator of the activator to become inac- 2232

tive. If it encounters an obstacle before it finishes changing its course, the mobile agent goes back to avoid collision (Figure 7d). ~:Behavior module @: Suppression P % Avoid-front +( Go-along Sensors Memory Actuator \- J (a) Normal-running sensors sensors sensors KA # R...sr n +Yll (b) Approaching-station Break 1 ~ Search 1 ~-along(backwwls (c) Leaving-station i~} ~ Avoid(Backwards) Turn Foward (d) Changing-course Figure 7. Behavior styles of mobile agent. ~ A~ivator II > Activator II Actuator \ Activator This way, the mobile agent architecture minimizes the number of behavior modules required for each aspect. In addition, when behavior modules respond to a real environment, their functions while basically similar, differ slightly for each behavior style, as shown with Avoid in Figures 7a and d. Therefore, it is difllcult for behavior styles to share behavior modules. Hence, making each behavior style completely independent of each other is practical. 3.2 Behavior modules A behavior module is the smallest functional unit in the mobile agent architecture. It is a behavior style component that determines the function of the behavior style. How well a mobile agent can avoid collision depends on not only the performance and layout of its sensors and actuators but also on how well the behavior modules in it suit the goals of a particular behavior style and how easily they can be cofilgured. The easier the contlgmation, the easier it becomes to maintain high real-time characteristics and to follow changes in situations, The mobile agent architecture can focus on the behavior type intended by each behavior style, therefore enabling the coti@ring of highly adaptable, efficient behavior modules. Collision avoidance on a single-track guideline can be disassembled into four behavior types: go along, avoid front, avoid side, and return. These behavior modules can be represented with simple finite state machines. Each behavior module fimctions as described below: 1) Avoid-front: This behavior module is used when the mobile agent avoids an obstacle ahead. If the mobile agent detects an obstacle ahead, it moves over quickly. If it detects an obstacle in the left tlont, it moves over to the right. (This mode is used when the keep-to-the-right rule applies. It is possible to move to the left if so designed.) If the mobile agent detects an obstacle to the right or left toward the front, it moves forward slowly. 2) Go-along This behavior module is used when the mobile agent is running along the guideline. If the mobile agent detects that it is deviating to the left from the guideline, it moves to the right. Similarly, if it detects that it is deviating to the right, it moves to the left. If it is not deviating to either side, it continues moving straight ahead. 3) Avoid-side: This behavior module is used when the mobile agent avoids an obstacle on its flank. If the mobile agent detects an obstacle on the left side, it moves over to the right. 4) Return: This behavior module is used when the mobile agent returns to the guideline. If the mobile agent remembers that it has just avoided a collision, it moves to the left, (This mode is used when the keep-to-the-right rule applies. It is possible to move to the right if so designed.) The independence of the behavior styles is useful for setting up suppressing relationships among behavior modules. In the normal-running behavior style, it is only necessmy to set up specific suppressing relationships among the behavior modules stated above, as shown in Figure 7a. When this method is applied, the mobile agent should follow the guideline via the go-along behavior module, avoid collisions via the avoid-front behavior module, and pass obstacles on the right side and return to the guideline via the avoid-side and return behavior modules before returning to follow the guideline via the goalong behavior module. It is possible to conf@re behavior modules that belong to behavior styles other than the normal-running ones in a structure that meets the target behavior types of a specific behavior style and set up suppressing relationships among them. A method has been proposed which can be used to set up suppressing relationships among behavior modules [14]. With the mobile agent architecture, however, designers can set up suppressing relationships among behavior modules easily, because the role of each behavior module is defined clearly and their suppressing relationships are self-explanatory. 2233

4. Execution Example This section gives an example of using AMADEUS for transportation among cells on a shop floor. The example illustrates how behavior style transition occurs according to the mobile agent architecture to implement collision avoidance (passing each other). 4.1 Equipment AMADEUS is basically conf@red with mobile agents, cell agents, guidelines, signposts installed on the floor, and stations among which cargo is transported.the major structures of signposts installed on the floor and mobile agents areexplained below. 1) Signposts on the floor The signposts shown in Figure 8 are installed on the floor. The signposts are categorized into address signs (a), stop position signs (b), and intersection signs (c). The address signs (a) indicate street and block names, The intersection signs (c) indicate right- and left-side street names. The guide sensor detects when the mobile agent deviates to the right or left from the guideline. The reflectiontype inftared sensors are used to detect obstacles. The positions of stations and intersections are detected using address signs. Positioning the mobile agent within a station is performed using the positioning sensor to detect the deviation of the mobile agent from the stop position indicated on the floor. Control is carried out by representing behavior modules with finite state machines and executing them as one process in a multitasking mode. 4.2 Example of behavior style transition Figure 10 shows an example of behavior style and module transition in the mobile agent architecture. This example illustrates how a mobile agent moves from station one to station zero. Steps 1) to 5) below sequentially describe how the behavior style transition occurs. Figure 11 shows how the mobile agent behaves in each behavior style. Go-alon Leve12 --- Guideline Station O 0 Ti#e (s) 120 Figure 10. Transition of behavior styles and modules. Figure 8. Signposts on the floor. 2) Mobile agent structure Figure 9 shows the structure of a mobile agent. It measures 700 (W) x 850 (L) x 900 mm (H), and weighs 1000N. Ithas right andleftindependentdrivewheelsand is guidedusing magneticinductionloops. Driving wheel (left) Positioning _ sensor v Driving wheel (right) / gl?zzzl Guide sensor Address sensor Figure 9. The outline structureof a mobile agent (c)changing-course (d) Approaching-station Figure 11. Behavior of mobile agent in each behavior style. 1)Leaving-station behavior style (Figure 10a):First, the mobile agent one (Figure 8) obtains a target address (Figure8a) by negotiatingvehicleallocationwith the cell agent at station one (Figure 8). Then, the mobile agent leavesstation one accordingto the leaving-stationbehavior style(figure 11a). 2234

2) Normal-running behavior style (Figure 10b): After leaving station one, the mobile agent searches for the guideline. When it detects the guideline, a behavior style transition of leaving-station to normal-running occurs. Following the normal-mnning behavior style, the mobile agent runs along the guideline toward an intersection (Figure llb). 3) Changing-course behavior style (Figure 10c): When the mobile agent detects an intersection sign (Figure SC), the behavior style changes from normal-running to changing-course. According to the changing-course behavior style, the mobile agent turns left at the intersection (Figure 1lc). If the mobile agent encounters an oncoming mobile agent zero (Figure 8) when turning, it gives way to avoid a collision using the avoid-backward behavior module speciiic to the changing-course behavior style. 4) Normal-runningbehavior style (Figure10d): After turningat the intersection,the mobileagentsearchesfor the guideline.whenit detectsthe guideline,a changingcourse to normal-running behavior style transition occurs, according to which the mobile agent runs along the gnideline toward station zero (Figure 8). If the mobile agent encounters an on-coming mobile agent zero when running along the guideline, both mobile agents carry out collision avoidance. In this case, each mobile agent only has to execute the avoid-front, go-along, avoid-side, and return behavior modules that form the normal-running behavior style (see Section 4.3 for details). 5) Approaching-station behavior style (Figure 10e): When a mobile agent detects a target address (Figure 8a), a normal-running to approaching-station transition occurs, according to which the mobile agent enters station zero. On detecting the stop position sign (Figure 8b), the mobile agent comes to stop (Figure 1id). Use of the mobile agent architecture enables timely behavior style transitions so that the behavior style that is best suited for a specific aspect of transportation can be selected. This method executes ten or more conventional behavior modules in four or less (Figure10 Level O to 3) modules for each aspect of the transport process, thereby producing a high real-time response. Moreover, this method stipulates that onty those behavior modules necessary for a specific behavior style be executed, making it possible to tailor behaviors for each aspect. 4.3 Example of collision avoidance Figure 12 shows an example of how behavior module status transition occurs for causing mobile agents to avoid collision (passing each other) and how each mobile agent behaves. Figure 13 is a picture showing how collision is avoided. In this example, mobile agents run along a magnetic guideline at 0.5 m/s in opposite directions. The mobile agent architecture has selected the normal-running behavior style and caused only the related behavior modules to operate, Status of behavior modules I 1 I II I Return [=]; 1 J.- ~ Behavior of Mobile agent o 3 Time (s) Figure 12. Transition of behavior modules and the behavior of mobile agent. (a) Avoid-front (c) Return (d) Go-along Figure 13. View of passing each other in each behavior module. Both mobile agents first run along the guideline as directed by the go-along behavior module. When each mobile agent detects the other mobile agent, that is an oncoming mobile agent for each other, the avoid-front behavior module starts to operate and causes each mobile agent to move over to the right from the guideline (Figure 13a), Next, the avoid-side behavior module and the return behavior module, which is based on the memory that each mobile agent has moved over from the guideline, operate alternately. So, each mobile agent passes the other mobile agent on the right side by moving to the right then to the left (Figure 13b). After passing the oncoming mobile agent, each mobile agent moves toward the guideline as directed by the return behavior module (Figure 13c). After 2235

returning to the guideline, its memory that it has moved away from the guideline is erased. Next the go-along behavior module starts operating to cause each mobile agent to run along the guideline (Figure 13d). In this example, the mobile agents can pass each other within 6 seconds, because a high real-time characteristic can be maintained for control using only the behavior modules of the normal-running behavior style. In addition, the mobile agents can easily avoid collisions only by temporarily moving away from the guideline no matter what the shape or behavior of the obstacle is, because the system shown in the example has improved its performance in avoiding collisions by making each behavior module specific to the normal-running behavior style. 5. Conclusions We have developed an autonomous decentralized transportation system called AMADEUS, and applied it to transportation among cells on a shop floor. AMADEUS consists of autonomous transportation agents, or mobile agents, and agents in cells, or cell agents. AMADEUS is characterized by vehicle allocation negotiated between agents and collision avoidance performed by mobile agents, which makes it possible to abolish centrally managed allocation and routing plans. To be more specific, a mobile agent moves to a target address obtained in negotiation for autonomous vehicle allocation with a cell agent without colliding with other mobile agents. This method has superseded the conventional central management system, which required high cost and long times to cope with changes in transportation environments. Moreover, the collision avoidance function of mobile agents enables them to run along a single-track guideline in opposite directions, resulting space-saving, efficient transportation. One of the major challenging issues in realizing AMADEUS is how to install the collision avoidance function. A behavior-based approach has been used to control the mobile agents. Providing each mobile agent with a collision avoidance function would lead to an increased number of control steps and interference among behavior types because of their coexistence in various transportation aspects. To solve these problems, we have devised an architecture in which each behavior style is made of only the required basic units of functions it needs, and transitions occur among the behavior styles as required. With this architecture, it is only necessary to run an optimum basic behavior unit designed to realize the target behavior in each aspect. The best-suited behavior for each goal can be implemented without deteriorating the real-time characteristic for control in any aspect. AMADEUS is now operating at Fujitsu s Kanuma Plant (6-1 Satsuki-cho, Kanuma 322, Japan). 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