JPWO2020021643A1 - End effector selection method and selection system - Google Patents

Abstract

The method of selecting the end effector that is interchangeably attached to the robot that performs the work on the work and grips the work includes the acquisition step of acquiring information including the shape and work contents of the work, and the shape and work of the work in the known work. A construction step that learns the relationship between the content and the type of end effector selected in the work to build a model, and when at least one of the work and the work content is a new target work, in the target work It includes a selection step of selecting an end effector suitable for the target work from a plurality of types of end effectors based on the shape and work content of the work and the model.

Description

The present specification discloses an end effector selection method and a selection system.

Conventionally, in a robot provided with a hand (end effector) for gripping a work, a selection method of selecting an appropriate hand according to the work to be worked from a plurality of types of hands has been proposed. For example, Patent Document 1 describes that the suitability of a work and a hand is evaluated based on the information about the work to be worked and the information about the hand, and a more appropriate hand is automatically selected.

WO2015 / 178377A1

If a selection error occurs in the automatic selection of the hand as described above, the robot cannot properly grip the work, and a work error or a malfunction occurs. As described above, since the selection of the hand has a great influence on the workability of the robot, further improvement is required so that the selection can be made more appropriately.

A main object of the present disclosure is to more appropriately select an end effector that grips a work.

The present disclosure has taken the following steps to achieve the above-mentioned main objectives.

The method of selecting an end effector of the present disclosure is a method of selecting an end effector that is interchangeably attached to a robot that performs work on a work and grips the work, and acquires information including the shape and work contents of the work. A construction step of learning the relationship between the acquisition step to be performed, the shape and work content of the work in a known work, and the type of the end effector selected in the work to build a model, and the work and work content. When at least one of the new target work is executed, the end effector suitable for the target work is selected from a plurality of types of the end effectors based on the shape and work content of the work in the target work and the model. The gist is to include a selection step to select.

In the method of selecting an end effector of the present disclosure, a model is constructed by learning the relationship between the shape and work content of a work in a known work and the type of end effector selected in the work. Then, when at least one of the work to be worked and the work content is a new target work, the target work is targeted from a plurality of types of end effectors based on the shape and work content of the work in the target work and the constructed model. Select the end effector that is suitable for your work. As a result, even if the work to be worked on or the work content is a new target work, the end effector can be selected more appropriately considering the shape of the work and the work content by using the model constructed by learning the known work. It can be carried out.

The block diagram which shows the outline of the structure of the robot system 10. The block diagram which shows the outline of the structure of the robot 20. The block diagram which shows the electrical connection relation of the robot system 10. A block diagram showing a function related to automatic selection of end effectors. Explanatory drawing which shows an example of work DB 83. Explanatory drawing which shows an example of a selection model. A flowchart showing an example of automatic selection processing. Explanatory drawing which shows an example of the list display screen 89a of an end effector. The flowchart which shows an example of the end effector confirmation processing. A flowchart showing an automatic selection process of a modified example.

Next, a mode for carrying out the present disclosure will be described with reference to the drawings.

FIG. 1 is a configuration diagram showing an outline of the configuration of the robot system 10, FIG. 2 is a configuration diagram showing an outline of the configuration of the robot 20, and FIG. 3 is a block diagram showing an electrical connection relationship of the robot system 10. be. The left-right direction in FIG. 1 is the X-axis direction, the front-back direction is the Y-axis direction, and the up-down direction is the Z-axis direction.

The robot system 10 includes a robot 20, a robot control device 70, and a management device 80. The robot system 10 is configured as, for example, a pick-and-place system, and picks (holds) work W such as mechanical parts and electronic parts supplied by the work supply device 12, and the tray is conveyed by the tray transfer device 14. Work such as arranging and placing on T is performed. Further, work such as picking another work W such as a washer with respect to the work W such as a bolt aligned on the tray T and placing the work W so as to be inserted into the shaft portion of the bolt is also performed. The robot system 10 is not limited to the pick-and-place system, and can be applied to any work system as long as the robot 20 is used to perform work on the work W.

The robot 20 includes a 5-axis vertical articulated arm (hereinafter referred to as an arm) 22. The arm 22 has six links 31-36 and five joints 41-45 that rotatably or swivelly connect each link. Each joint 41 to 45 is provided with servomotors 51 to 55 for driving the corresponding joints and encoders 61 to 65 for detecting the rotational position of the corresponding motor.

A plurality of types of end effectors E as work tools are interchangeably attached to the link 36 at the tip of the arm 22. The end effector E1 * is an electromagnetic chuck that attracts a work W made of a magnetic material by an electromagnet, and has a plurality of types (E11, E12, ...) With different sizes and shapes. Further, the end effector E2 * is a mechanical chuck (hereinafter referred to as a mechanical chuck) having a pair of clamp claws that can be opened and closed so as to hold and release the work W, and the size and shape of the clamp claws are different. It has a plurality of types (E21, E22, ...). Further, the end effector E3 * is a suction nozzle that sucks the work W by a negative pressure, and has a plurality of types (E31, E32, ...) With different diameters and lengths of the suction nozzles. The end effector E attached to the link 36 at the tip is selected by an automatic selection process described later.

A camera 24 is attached to the link 35 of the arm 22. The camera 24 images the work W in order to recognize the position and orientation of each work W supplied by the work supply device 12, and the tray T in order to recognize the position of the tray T conveyed by the tray transfer device 14. Image T.

The link 31 at the base end of the arm 22 is fixed to the work table 11. A work supply device 12, a tray transfer device 14, and the like are arranged on the work table 11. In the present embodiment, the work supply device 12 includes a conveyor belt 12a spanned by a drive roller and a driven roller arranged apart from each other in the front-rear direction (Y-axis direction). A plurality of work Ws are separately placed on the conveyor belt 12a, and the work supply device 12 supplies the plurality of work Ws on the conveyor belt 12a from the rear to the front by rotationally driving the drive rollers. As the work supply device, a supply device may be provided in place of the conveyor belt 12a or in parallel with the conveyor belt 12a to supply a plurality of works housed in the case (parts box) together with the case. The tray transport device 14 is composed of a conveyor belt, transports the tray T in a direction (X-axis direction) orthogonal to the supply direction of the work W, and positions and holds the tray T at a substantially central position.

Although not shown, the robot control device 70 includes a CPU, ROM, HDD, RAM, input / output interface, communication interface, and the like. Detection signals from encoders 61 to 65 and the like are input to the robot control device 70. Further, the robot control device 70 outputs control signals to the work supply device 12, the tray transfer device 14, the servomotors 51 to 55, the actuator 56, and the like. The actuator 56 is for driving the end effector E mounted on the robot 20. The robot control device 70 drives and controls each of the servomotors 51 to 55 of the robot 20 to cause the robot 20 to pick the work W or place the picked work W.

Although not shown, the management device 80 includes a CPU, a ROM, an HDD, a RAM, an input / output interface, a communication interface, and the like. An image signal from the camera 24, an input signal from the input device 88, and the like are input to the management device 80. Further, the management device 80 outputs a drive signal to the camera 24, a display signal to the display device 89, and the like. Here, the input device 88 is, for example, a keyboard or a mouse, and the display device 89 is, for example, a liquid crystal display. The management device 80 is communicably connected to the robot control device 70, and exchanges control signals and various information with each other. The management device 80 transmits a control signal to the robot control device 70 to move the arm 22 (camera 24) to the image pickup point of the work W supplied by the work supply device 12, and drives the camera 24 to image the work W. Then, input the captured image. Subsequently, the management device 80 processes the input image and recognizes the work W in the image. Then, the management device 80 extracts the pickable work W from the recognized work W, determines the target position and the target posture of the end effector E for picking the work W, and transmits the target position and the target posture to the robot control device 70.

Further, in the management device 80 and the robot control device 70, the end effector E can be automatically selected according to the shape of the work W and the work content. FIG. 4 is a block diagram showing a function related to automatic selection of end effectors. As shown in FIG. 4, the management device 80 has an information input unit 80A for inputting various information and a selection processing unit 80B for selecting the end effector E from the input information. The information input unit 80A inputs various information including the work information 81a relating to the work W and the work information 81b relating to the work content performed on the work W. The work information 81a includes information such as the shape, dimensions, and type of the work W, and for example, three-dimensional CAD information of the work W is input. The shape and dimensions of the work W obtained by processing the image captured by the camera 24 may be input as the work information 81a. Further, the work information 81b includes information on the work content such as the target shape and the target dimension of the target on which the work W is placed, and whether the work W is aligned on the tray T or inserted into the bolt shaft. The information input unit 80A may acquire the work information 81b from a work instruction or the like input by the operator via the input device 88.

The selection processing unit 80B includes a parameter extraction unit 82, a work DB 83, a model construction unit 84, a score calculation unit 85, a list display unit 86, and a candidate determination unit 87. The parameter extraction unit 82 extracts characteristic parameters from the work information 81a and the work information 81b input by the information input unit 80A. For example, in the case of work of inserting a washer into a bolt shaft, the parameter extraction unit 82 uses an end effector such as the shape of the work W (washer), the inner and outer diameter dimensions of the washer, the bolt shaft diameter, and the work content (shaft insertion). The information necessary for selecting E is extracted as a parameter. The parameters extracted by the parameter extraction unit 82 are registered in the work DB 83 and output to the score calculation unit 85. FIG. 5 is an explanatory diagram showing an example of the work DB 83. As shown in the figure, the work DB 83 corresponds to the work information and work information of the known work, the information of the end effector E selected in the work, and the judgment result (evaluation) of the quality of the selected end effector E. It is registered with. The parameters extracted by the parameter extraction unit 82 are registered in the work information and the work information of the known work, but information other than these may be registered. The result of the pass / fail judgment will be described later.

The model building unit 84 builds a selection model for automatically selecting the end effector E. The model construction unit 84 constructs a selection model grouped and classified for each type of end effector E by machine learning using characteristic parameters of the information registered in the work DB 83. Machine learning may be performed by using a known method of classifying into a plurality of groups by, for example, discriminant analysis or the K-nearest neighbor method. Further, deep learning or the like may be used. FIG. 6 is an explanatory diagram showing an example of the selection model. In FIG. 6, for convenience of illustration, a selection model in which three types of end effectors E * 1 (Δ), E * 2 (□), and E * 3 (◇) are selected from two characteristic parameters X and Y. Shows the image of. Further, in FIG. 6, representative values such as the center of gravity of the values of each group (class) of the end effectors E * 1 (△), E * 2 (□), and E * 3 (◇) are represented values ▲, ■, respectively. It is shown by ◆, and an example of the boundary of each group is shown by a dotted line.

When the parameter that is the selection condition of the end effector E is input from the parameter extraction unit 82, the score calculation unit 85 calculates an approximate score indicating the relationship with each group of the end effector E in the selection model. For example, the score calculation unit 85 calculates an approximate score as a distance between the corresponding position (⊚ in FIG. 6) on the selection model based on the input parameter and the representative values ▲, ■, and ◆ of each group. .. When the work W and the work contents in the new work are obtained in this way, the score calculation unit 85 calculates an approximate score for each of the various end effectors E selected in the known work. The list display unit 86 displays a list of a plurality of types of end effectors E on the display device 89 in descending order of approximate score based on the calculation result by the score calculation unit 85. Further, the candidate determination unit 87 determines one end effector E selected by the operation of the input device 88 by the operator from the list of end effectors E as a candidate. The candidate determination unit 87 transmits the type of the end effector E determined as a candidate and the identification information of the work such as the work number to the robot control device 70.

The robot control device 70 includes a replacement mounting unit 71, an operation confirmation unit 72, and a work execution unit 73. The replacement mounting unit 71 mounts the end effector E determined by the candidate determination unit 87 on the link 36 at the tip. If the robot 20 is configured to automatically replace the end effector E, the replacement mounting unit 71 is automatically replaced, and if the robot 20 is not automatically replaceable, the replacement instruction is displayed to the worker to replace the end effector E. Let me. The operation confirmation unit 72 confirms the operation such as picking the work W to be worked by using the candidate end effector E determined by the candidate determination unit 87. The operation confirmation result by the operation confirmation unit 72 is transmitted to the management device 80 and registered as an evaluation in the work DB 83. The work execution unit 73 performs the actual work by using the end effector E whose confirmation result by the operation confirmation unit 72 is good.

Next, the process when the robot system 10 automatically selects the end effector E by using these functions will be described. FIG. 7 is a flowchart showing an example of the automatic selection process. This process is executed by each of the above-described functions of the management device 80. When this process is started, the management device 80 first determines whether at least one of the shape of the work W and the work content is the timing at which a new work is started (S100), or the operation check unit of the robot control device 70. It is determined whether it is the timing when the result notification is received from 72 (S105).

When the management device 80 determines that it is the timing to start a new work (target work) in S100, the management device 80 inputs work information 81a and work information 81b (S110), and extracts characteristic parameters from the information. Together with (S115), it is registered in the work DB 83 (S120). Next, the management device 80 calculates an approximate score of each end effector E for a new work using the selection model (S125), lists the end effectors E on the display device 89 in order of score (S130), and the end effector by the operator. Wait for the selection operation of the candidate of E to be performed (S135).

FIG. 8 is an explanatory diagram showing an example of the end effector list display screen 89a. As shown, the end effectors E are displayed side by side in the order of scores, and a schematic diagram of the end effectors E pointed to by the cursor is displayed. On the list display screen 89a, each time the operator performs an up / down operation on the input device 88, the cursor moves up or down and the end effector E on which the schematic diagram is displayed changes. Further, when the operator performs the selection operation on the input device 88, the operator can select the end effector E pointed to by the cursor on the list display screen 89a.

When the selection operation is performed by the operator, the management device 80 determines the selected end effector E as a candidate (S140), and sends a candidate determination notification including the determined end effector E type and work identification information to the robot. It is transmitted to the control device 70 (S145) to end the automatic selection process. Hereinafter, the process of the robot control device 70 will be described by suspending the description of the automatic selection process of the management device 80.

FIG. 9 is a flowchart showing an example of the end effector confirmation process. This process is executed by the robot control device 70. In this process, the robot control device 70 first waits for the determination that the candidate determination notification has been received (S200), and if it determines that the candidate determination notification has been received, the robot control device 70 causes the end effector E of the type included in the candidate determination notification. Replace (S205). In S205, automatic replacement by the robot 20 or replacement by the operator is performed. Next, the robot control device 70 checks the operation such as picking the work W using the end effector E (S210). In S210, the robot control device 70 picks the work W to be worked on by using the candidate end effector E, and determines whether or not the work W can be picked normally. The robot control device 70 determines that the work W cannot be picked normally when the work W cannot be picked or the work W falls immediately after being picked. Further, in S210, the robot control device 70 may determine not only picking the work W but also whether or not it can be placed normally.

Then, the robot control device 70 determines whether or not the result of the operation check is good (S215), and if it is determined that the result is good, the robot control device 70 transmits a result notification to the effect that the operation is good (S220). .. In this case, since there is no problem in working with the candidate end effector E, the robot control device 70 starts the work using the current end effector E as it is (S225), and ends the end effector confirmation process. do. On the other hand, when the robot control device 70 determines that the result of the operation check is defective, the robot control device 70 transmits a result notification to the effect of the malfunction to the management device 80 (S230), returns to S200, and performs processing. In this case, if the work is performed using the candidate end effector E, work mistakes such as dropping of the work W and re-picking of the work W may occur frequently, and the work efficiency may decrease. Therefore, the work is started. It will wait until a new candidate decision notification is received without doing so.

In this way, when the result notification is transmitted from the robot control device 70 to the management device 80, the management device 80 determines that the result notification has been received in S105 of the automatic selection process of FIG. 7, and the pass / fail result corresponds to the parameter. Attach and register in the work DB 83 (S150). In S150, if the operation confirmation result in the result notification is good, the fact that it is good is registered in association with the job number, and if the operation confirmation result in the result notification is bad, the fact that it is defective is associated with the job number. be registered. Further, the management device 80 builds (updates) a selection model based on the work DB 83 after registration (S155). In S155, the relationship between the end effector E in which the pass / fail result is newly registered and the parameter can be reflected in the selection model, so that the reliability of the selection model can be improved. Note that work number ** 2 in FIG. 5 illustrates a case where the confirmation result of the previously selected end effector E * 2 is poor and the confirmation result of the reselected end effector E * 1 is good. .. Further, in FIG. 6, a defective result when the end effector E of E * 2 (□) is selected is indicated by adding a cross. In the present embodiment, since the selection model that reflects both the good result and the bad result of the operation check result is constructed, the boundary of each group can be set more accurately. Then, the management device 80 determines whether or not the operation confirmation result is defective (S160), and if it is determined that the operation confirmation result is not defective and is good, the automatic selection process ends as it is.

On the other hand, when the management device 80 determines in S160 that the operation check result is defective, the management device 80 recalculates the approximate score of each end effector E using the selection model constructed in S155 (S165), and selects the end effector E. Are listed in order of score except for (S170). In the example of FIG. 8, when the end effector E22 having the score order 1 is selected but the operation check result is poor, the management device 80 recalculates the other end effectors E excluding the selected end effector E22. The list will be displayed in the order of the score. Then, the management device 80 executes the processes of S135 to S145 for transmitting the end effector E selected by the operator to the robot control device 70. As a result, it is possible to reselect an appropriate end effector E from the other end effectors E excluding the end effector E which has been selected once but whose operation confirmation result is poor. Since the approximate score of each end effector E is recalculated using the selection model constructed by reflecting the operation confirmation result, it is possible to let the operator select from the list of each end effector E more appropriately. It will be possible.

Here, the correspondence between the components of the present embodiment and the components of the present disclosure will be clarified. The robot 20 of the present embodiment corresponds to a robot, the end effector E corresponds to an end effector, S110 of the automatic selection process of FIG. 7 corresponds to an acquisition step, and S155 of the same process corresponds to a construction step. S115 to S140 correspond to the selection step. Further, S210 of the end effector confirmation process corresponds to the confirmation step. Further, the information input unit 80A that executes S110 of the automatic selection process of FIG. 7 corresponds to the acquisition unit, the model construction unit 84 that executes S155 of the same process corresponds to the construction unit, and S115 to S140 of the same process are executed. The parameter extraction unit 82, the score calculation unit 85, the list display unit 86, and the candidate determination unit 87 correspond to the selection unit.

The robot system 10 described above selects an end effector E suitable for a new work from a plurality of types of end effectors E based on the shape and work contents of the work W in the work and the constructed selection model. As a result, the end effector E can be more appropriately selected in consideration of the shape of the work W and the work content by using the model constructed by learning the known work.

Further, the approximate scores of a plurality of types of end effectors are calculated and displayed in a list on the display device 89 in the order of scores, and the end effector E is selected according to the selection by the operator. The selection of the end effector E can be made more appropriately.

In addition, since the operation of the selected end effector E is confirmed and the selection model is constructed by associating the quality of the confirmation result with each other, the reliability of the selection model is improved and the accuracy of selection of the end effector E is further improved. be able to.

Further, if the confirmation result is poor, the re-selection can be performed from a plurality of types of end effectors E excluding the already selected end effector E by using the reconstructed selection model. The accuracy can be further improved.

It goes without saying that the present invention is not limited to the above-described embodiment, and can be implemented in various aspects as long as it belongs to the technical scope of the present invention.

For example, in the above-described embodiment, when the confirmation result of the operation of the selected end effector E is poor, the approximate score is recalculated using the updated selection model, and then the end effector E is reselected. However, it is not limited to this. For example, the end effector E may be reselected from the already selected end effector E excluding the already selected end effector E without recalculating the approximate score.

In the above-described embodiment, the end effectors E are listed in the order of scores and the operator is allowed to select them. However, the present invention is not limited to this, and the management device 80 may select the end effectors E based on the order of scores. FIG. 10 is a flowchart showing an automatic selection process of a modified example. In FIG. 10, the same process as in FIG. 7 is assigned the same step number, and the description thereof will be omitted. When the management device 80 calculates the approximate score using the selection model in S125, the management device 80 determines the end effector E having the highest score order as a candidate (S140a). Therefore, in the example of FIG. 8, the end effector E22 is automatically determined as a candidate. Further, when the management device 80 determines in S160 that the result received from the robot control device 70 is defective, the management device 80 determines the end effector E having the highest score order except for the already selected end effector E (S168). .. Therefore, in the example of FIG. 8, the end effector E21 is automatically determined as a candidate. In FIG. 10, as in FIG. 7, the approximate score may be recalculated using the updated selection model, and then the process of S168 may be executed.

In the above-described embodiment, the quality of the end effector E is determined by confirming the operation of the end effector E selected as a candidate, and then the work is started. However, the present invention is not limited to this, and the quality of the end effector E is not limited to this. The work may be started without determining. In that case, the end effector E may not be reselected when the confirmation result is poor. However, in order to prevent work mistakes such as dropping of the work W and re-picking of the work W, it is preferable to determine the quality of the end effector E selected as a candidate before starting the work.

In the above-described embodiment, the approximate score for each type of end effector E in the selection model is calculated from the characteristic parameters, and the end effector E having a higher approximate score is preferentially selected, but the present invention is limited to this. It's not a thing. A selection model constructed by learning the relationship between the shape and work content of the work W in a known work and the end effector E selected in the work is used to select the end effector E in a new work. If so, the end effector E may be selected by any process.

In the above-described embodiment, the work information 81a includes dimensions, types, and the like in addition to the shape of the work W, but at least the shape of the work W may be included. Further, although the work information 81b includes the target shape, the target dimension, and the like in which the work W is placed, at least the work content of what kind of work is to be performed may be included.

Here, the computer-based end effector selection method and selection system of the present disclosure may be configured as follows. For example, in the method of selecting an end effector of the present disclosure, in the selection step, the relationship between the plurality of types of the end effectors and the target work is based on the shape and work content of the work in the target work and the model. The score indicating the sex may be calculated, listed in the order of scores, and the end effector may be selected according to the selection by the operator. In this way, it is possible to select the end effector by adding a judgment based on the experience of the worker in the order of the score of the end effector, so that the end effector can be selected more appropriately.

In the method of selecting an end effector of the present disclosure, in the selection step, the relevance of a plurality of types of the end effector to the target work is determined based on the shape and work content of the work in the target work and the model. The indicated score may be calculated, and the end effector having a high score may be preferentially selected. In this way, the end effector can be selected more appropriately by a simple process using the constructed model.

In the method for selecting an end effector of the present disclosure, a confirmation step for confirming the quality of the gripping operation of the work by the end effector selected in the selection step is included, and the confirmation result of the confirmation step is obtained in the construction step. The model may be constructed by associating the quality of the confirmation result with the relationship between the shape and work content of the work in the target work and the type of the end effector selected in the selection step. In this way, the reliability of the model can be improved, and the accuracy of end effector selection can be further improved.

In the method of selecting an end effector of the present disclosure, in the selection step, when the confirmation result of the confirmation step is poor, it is suitable for the target work from a plurality of types of the end effectors excluding the already selected end effector. The end effector may be reselected. By doing so, it is possible to reselect the end effector using the model constructed by associating the quality of the confirmation result with each other, so that the accuracy of the reselection can be further improved.

The end effector selection system of the present disclosure is an end effector selection system that is interchangeably attached to a robot that performs work on a work and grips the work, and acquires information including the shape and work contents of the work. A construction unit that learns the relationship between the acquisition unit, the shape and work content of the work in a known work, and the type of the end effector selected in the work, and builds a model, and the work and work to be worked on. When at least one of the contents is a new target work, the work suitable for the target work from a plurality of types of end effectors based on the shape and work contents of the work in the target work and the model. The gist is to include a selection unit for selecting an end effector.

Similar to the selection method described above, the end effector selection system of the present disclosure uses a model constructed by learning a known work, even if the work to be worked or the work content is a new target work. The end effector can be selected more appropriately in consideration of the shape and the work content. In this selection system, a function that realizes each step of the selection method described above may be added.

The present invention can be used in the manufacturing industry of robot systems and the like.

10 robot system, 11 workbench, 12 work supply device, 12a conveyor belt, 14 tray transfer device, 20 robot, 22 arm, 24 camera, 31-36 links, 41-45 joints, 51-55 servo motor, 56 actuator, 61-65 encoder, 70 robot control device, 71 replacement mounting unit, 72 operation check unit, 73 work execution unit, 80 management device, 80A information input unit, 80B selection processing unit, 81a work information, 81b work information, 82 parameter extraction Unit, 83 work DB, 84 model construction unit, 85 score calculation unit, 86 list display unit, 87 candidate determination unit, 88 input device, 89 display device, 89a list display screen, E, E1 * to E3 *, E11, E12 , E21, E22, E31, E32 end effector, T tray, W work.

Claims (6)

It is a method of selecting an end effector that is interchangeably attached to a robot that executes work on a work and grips the work.
An acquisition step for acquiring information including the shape and work content of the work, and
A construction step of learning the relationship between the shape and work content of the work in a known work and the type of the end effector selected in the work to build a model, and
When at least one of the work and the work content is a new target work, the target work is performed from a plurality of types of the end effectors based on the shape and work content of the work in the target work and the model. A selection step to select the end effector suitable for
How to select end effectors including. The method for selecting an end effector according to claim 1.
In the selection step, based on the shape and work content of the work in the target work and the model, scores indicating the relevance of the plurality of types of the end effectors to the target work are calculated and listed in order of score. An end effector selection method for selecting the end effector according to the selection by the operator. The method for selecting an end effector according to claim 1.
In the selection step, based on the shape and work content of the work in the target work and the model, a score indicating the relevance of the plurality of types of end effectors to the target work is calculated, and the score is high. Preferentially select end effectors How to select end effectors. The method for selecting an end effector according to any one of claims 1 to 3.
Including a confirmation step for confirming the quality of the gripping operation of the work by the end effector selected in the selection step.
In the construction step, when the confirmation result of the confirmation step is obtained, the quality of the confirmation result is related to the relationship between the shape and work content of the work in the target work and the type of the end effector selected in the selection step. A method of selecting an end effector that constructs the model by associating with. The method for selecting an end effector according to claim 4.
In the selection step, if the confirmation result of the confirmation step is poor, the end effector that reselects the end effector suitable for the target work from a plurality of types of end effectors excluding the already selected end effector. How to select. An end effector selection system that is interchangeably attached to a robot that performs work on a work and grips the work.
An acquisition unit that acquires information including the shape and work content of the work, and
A construction unit that learns the relationship between the shape and work content of the work in a known work and the type of the end effector selected in the work, and constructs a model.
When at least one of the work and the work content is a new target work, the target work is performed from a plurality of types of the end effectors based on the shape and work content of the work in the target work and the model. A selection unit that selects the end effector suitable for
End effector selection system with.

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