Gripping force estimation device and gripping force estimation method

Abstract

The gripping force estimation device (3) is configured to estimate the gripping force of a gripping tool (1) having a kirigami structure, which includes an attachment part (12) to which a pulling tool is attached and two object gripping parts (13) that are parts for gripping an object (2), and in which the two object gripping parts (13) deform so as to approach each other when the attachment part (12) is pulled up by the pulling tool. The gripping force estimation device (3) is configured to include a sensing data acquisition part (31) that acquires sensing data from a sensor that observes the deformation of the gripping tool (1), and a gripping force estimation part (32) that estimates the gripping force of the gripping tool (1) with respect to the object (2) based on the sensing data acquired by the sensing data acquisition part (31).

Description

[Technical field]

[0001] The present disclosure relates to a grip force estimation device and a grip force estimation method. [Background technology]

[0002] There is a gripping force estimation device that estimates the gripping force of a gripping tool that is gripping an object. As such a grip force estimation device, for example, Patent Document 1 discloses an estimation observer that estimates the grip force of a grip device based on the drive current of a motor that drives the grip device and the rotation speed of the motor. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2002-178281 A Summary of the Invention [Problem to be solved by the invention]

[0004] As a gripping tool that is not necessarily driven by a motor, there is a gripping tool having a kirigami structure. The gripping tool has an attachment part to which a pulling tool is attached. The pulling tool can be, for example, a manual magic wand, tongs, or a robot hand. The gripping tool is deformed when the attachment part is pulled up by the pulling tool. When the gripping tool is deformed, two object gripping parts that are part of the gripping tool come closer to each other, and as a result, the two object gripping parts are able to grip an object. As described above, the gripping tool has a gripping mechanism with a new structure called a kirigami structure, which is different from existing gripping mechanisms. The gripping tool does not necessarily need to be driven by a motor, and there is a problem in that there is no technology to detect the gripping force. Incidentally, the technology for estimating the gripping force disclosed in Patent Document 1 requires that the gripping device be driven by a motor, and is therefore not suitable for detecting the gripping force using a gripping mechanism with a kirigami structure.

[0005] The present disclosure has been made to solve the above-mentioned problems, and aims to provide a gripping force estimation device that can estimate the gripping force of a gripping tool having a kirigami structure that is not necessarily driven by a motor. [Means for solving the problem]

[0006] The gripping force estimation device according to the present disclosure is a device that estimates the gripping force of a gripping tool having a kirigami structure, which includes an attachment part to which a tensile tool is attached and two object gripping parts that grip an object, and in which the attachment part is pulled up by the tensile tool, causing the two object gripping parts to deform so as to approach each other. Detects the internal pressure of a hollow object attached to the object gripper, which changes depending on the pressure. do Contains information on the internal pressure detected by the sensor a sensing data acquisition unit that acquires sensing data; and Internal pressure information contained in and a gripping force estimation unit that estimates the gripping force of the gripping tool on the object based on the above. Effect of the Invention

[0007] According to the present disclosure, it is possible to estimate the gripping force of a gripping tool having a kirigami structure that is not necessarily driven by a motor. [Brief description of the drawings]

[0008] [Figure 1] 1 is a configuration diagram showing a gripping tool 1 to which a gripping force estimation device 3 according to a first embodiment is applied. [Diagram 2] FIG. 1 is an explanatory diagram showing a state in which a gripping tool 1 is gripping an object 2. [Diagram 3] 1 is a configuration diagram showing a gripping force estimation device 3 according to a first embodiment. [Figure 4]2 is a hardware configuration diagram showing hardware of a gripping force estimating device 3 according to the first embodiment. FIG. [Diagram 5] FIG. 11 is a hardware configuration diagram of a computer when the gripping force estimation device 3 is realized by software, firmware, or the like. [Figure 6] 4 is a flowchart showing a grip force estimation method which is a processing procedure of the grip force estimation device 3. [Figure 7] FIG. 11 is a configuration diagram showing a gripping force estimation device 3 according to a second embodiment. [Figure 8] FIG. 11 is a hardware configuration diagram showing hardware of a gripping force estimating device 3 according to a second embodiment. [Figure 9] FIG. 11 is a configuration diagram showing a gripping force estimation device 3 according to a third embodiment. [Figure 10] FIG. 11 is a hardware configuration diagram showing hardware of a gripping force estimating device 3 according to a third embodiment. [Figure 11] Figure 11A is an explanatory diagram showing the state before the gripping tool 1 grips the object 2, Figure 11B is an explanatory diagram showing the state when the object gripping portion 13 lightly touches the object 2, and Figure 11C is an explanatory diagram showing the state when the gripping tool 1 grips the object 2. [Figure 12] FIG. 13 is a configuration diagram showing a gripping force estimation device 3 according to a fourth embodiment. [Figure 13] FIG. 11 is a hardware configuration diagram showing hardware of a gripping force estimating device 3 according to a fourth embodiment. [Figure 14] Figure 14A is an explanatory diagram showing the position of marker 4 when the gripping tool 1 is in a state before gripping object 2, Figure 14B is an explanatory diagram showing the position of marker 4 when the object gripping portion 13 is lightly touching object 2, and Figure 14C is an explanatory diagram showing the position of marker 4 when the gripping tool 1 is gripping object 2. [Figure 15] FIG. 13 is a configuration diagram showing a gripping force estimation device 3 according to a fifth embodiment. [Figure 16] FIG. 13 is a hardware configuration diagram showing hardware of a gripping force estimating device 3 according to embodiment 5. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0009] In order to describe the present disclosure in more detail, embodiments of the present disclosure will be described below with reference to the accompanying drawings.

[0010] Embodiment 1 FIG. 1 is a configuration diagram showing a gripping tool 1 to which a gripping force estimation device 3 according to the first embodiment is applied. FIG. 2 is an explanatory diagram showing a state in which the gripping tool 1 is gripping an object 2. As shown in FIG. The gripping tool 1 shown in Fig. 1 has a cutout portion 11, two mounting portions 12, and two object gripping portions 13. The gripping tool 1 shown in Fig. 1 has two mounting portions 12. However, this is merely an example, and the gripping tool 1 may have one mounting portion 12, or three or more mounting portions 12. The gripping tool 1 is realized by, for example, an elastic body. Examples of the elastic body include rubber and silicon. The gripping tool 1 has a kirigami structure.

[0011] The cutout portion 11 is a cutout provided approximately in the center of the gripping tool 1 in order to form a kirigami structure. The two attachment parts 12 are portions to which a pulling tool is attached. The pulling tool is not limited to one driven by a motor, and may be, for example, a manual hand-operated magic wand or tongs. In addition, the pulling tool may be a robot hand driven by a motor. The two object gripping portions 13 are portions that grip the object 2. By providing a notch in approximately the center, a pulling tool is attached to each of the two mounting parts 12, and when the pulling tool pulls up each mounting part 12, the two object gripping parts 13 approach each other. As a result, the two object gripping parts 13 are able to grip the object 2 as shown in FIG. 2. The object 2 may be any object, and examples of the object 2 include fried chicken, fruit, electronic parts, and medical instruments. FIG. 2 shows an example in which the object 2 is fried chicken. Also, FIG. 2 shows an example in which the pulling tool is a robot hand.

[0012] FIG. 3 is a configuration diagram showing the gripping force estimating device 3 according to the first embodiment. FIG. 4 is a hardware configuration diagram showing the hardware of the gripping force estimating device 3 according to the first embodiment. In FIG. 3, the hollow body 21 is realized by, for example, an air pouch or an elastic member such as rubber. The hollow body 21 is attached to the object gripping portion 13, which is a portion of the gripper 1 that grips the object 2. The internal pressure of the hollow body 21 changes as the gripping tool 1 is deformed. Specifically, the internal pressure of the hollow body 21 when the tension tool is pulling up the mounting portion 12 becomes higher than the internal pressure of the hollow body 21 before the tension tool pulls up the mounting portion 12. Also, the closer the tensioning tool brings the two attachment parts 12 closer to each other, the higher the internal pressure of the hollow body 21 becomes. One end of the hollow body 21 is closed, and the other end of the hollow body 21 is connected to one end of the tube 22 . 3, the hollow body 21 is attached so as to cover almost the entire object gripping portion 13. This is because if the hollow body 21 is attached so as to cover only a small part of the object gripping portion 13, the internal pressure of the hollow body 21 does not change much even if the gripper 1 is deformed.

[0013] The tube 22 is realized, for example, by a rubber pipe. One end of the tube 22 is connected to the other end of the hollow body 21 . The other end of the tube 22 is connected to a pressure sensor 23 . When the internal pressure of the hollow body 21 increases, the air that was present inside the hollow body 21 flows into the inside of the tube 22, and the internal pressure of the tube 22 increases.

[0014] The pressure sensor 23 is a sensor that observes the deformation of the gripping tool 1 that accompanies gripping of the object 2 by the gripping tool 1. Specifically, the pressure sensor 23 is a sensor that observes the internal pressure of the hollow body 21, which changes as the gripping tool 1 is deformed. The pressure sensor 23 outputs sensing data indicating the internal pressure of the hollow body 21 to the gripping force estimation device 3.

[0015] The gripping force estimation device 3 shown in FIG. The sensing data acquiring unit 31 is realized by, for example, a sensing data acquiring circuit 41 shown in FIG. The sensing data acquisition unit 31 acquires the sensing data of the pressure sensor 23 . The sensing data acquisition unit 31 outputs the sensing data of the pressure sensor 23 to the gripping force estimation unit 32 .

[0016] The grip force estimating unit 32 is realized by, for example, a grip force estimating circuit 42 shown in FIG. The gripping force estimation unit 32 acquires the sensing data of the pressure sensor 23 from the sensing data acquisition unit 31 . The gripping force estimating unit 32 estimates the gripping force of the gripping tool 1 on the object 2 based on the sensing data of the pressure sensor 23. Specifically, the internal memory of the gripping force estimating unit 32 stores a table indicating the correspondence between the internal pressure of the hollow body 21 and the gripping force of the gripping tool 1 on the object 2. The gripping force estimation unit 32 estimates the gripping force of the gripping tool 1 by acquiring, from the table, information indicating the gripping force of the gripping tool 1 corresponding to the internal pressure indicated by the sensing data. The gripping force estimating unit 32 outputs information indicating the gripping force of the gripping tool 1 to, for example, a control device (not shown) of the robot. 3, the table is stored in an internal memory of the grip force estimation unit 32. However, this is merely an example, and the table may be stored in an external memory of the grip force estimation unit 32.

[0017] In Fig. 3, it is assumed that the sensing data acquisition unit 31 and the grip force estimation unit 32, which are components of the grip force estimation device 3, are each realized by dedicated hardware as shown in Fig. 4. That is, it is assumed that the grip force estimation device 3 is realized by a sensing data acquisition circuit 41 and a grip force estimation circuit 42. Each of the sensing data acquisition circuit 41 and the grip force estimation circuit 42 may be, for example, a single circuit, a composite circuit, a programmed processor, a parallel programmed processor, an ASIC (Application Specific Integrated Circuit), an FPGA (Field-Programmable Gate Array), or a combination of these.

[0018] The components of the grip force estimation device 3 are not limited to those realized by dedicated hardware, and the grip force estimation device 3 may be realized by software, firmware, or a combination of software and firmware. The software or firmware is stored as a program in the memory of a computer. The computer means hardware that executes the program, and includes, for example, a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a central processing unit, a processing unit, an arithmetic unit, a microprocessor, a microcomputer, a processor, or a DSP (Digital Signal Processor).

[0019] FIG. 5 is a hardware configuration diagram of a computer in the case where the gripping force estimation device 3 is realized by software, firmware, or the like. When the grip force estimation device 3 is realized by software, firmware, or the like, a program for causing a computer to execute the respective processing procedures in the sensing data acquisition unit 31 and the grip force estimation unit 32 is stored in the memory 51. Then, the processor 52 of the computer executes the program stored in the memory 51.

[0020] 4 shows an example in which each of the components of the grip force estimation device 3 is realized by dedicated hardware, and FIG 5 shows an example in which the grip force estimation device 3 is realized by software, firmware, etc. However, this is merely an example, and some of the components in the grip force estimation device 3 may be realized by dedicated hardware, and the remaining components may be realized by software, firmware, etc.

[0021] Next, the operation of the gripping force estimation device 3 shown in FIG. 3 will be described. FIG. 6 is a flowchart showing a grip force estimation method which is a processing procedure of the grip force estimation device 3. When the pulling device pulls up the two attachment parts 12 of the gripping tool 1, the two object gripping parts 13 approach each other. As a result, the two object gripping parts 13 become able to grip the object 2 as shown in FIG.

[0022] The hollow body 21 is deformed by pulling the mounting portion 12 up with a tensioning tool. The internal pressure of the hollow body 21 increases as the hollow body 21 deforms. That is, the internal pressure of the hollow body 21 increases as the tensioning tool pulls up the mounting parts 12. The closer the tensioning tool brings the two mounting parts 12 closer to each other, the higher the internal pressure of the hollow body 21 becomes. When the internal pressure of the hollow body 21 increases, the air that was present inside the hollow body 21 flows into the inside of the tube 22, and the internal pressure of the tube 22 increases.

[0023] The pressure sensor 23 monitors the internal pressure of the tube 22 as the internal pressure of the hollow body 21 . The pressure sensor 23 outputs sensing data indicating the internal pressure of the hollow body 21 to the gripping force estimation device 3.

[0024] The sensing data acquisition unit 31 of the gripping force estimation device 3 acquires sensing data from the pressure sensor 23 (step ST1 in FIG. 6). The sensing data acquisition unit 31 outputs the sensing data of the pressure sensor 23 to the gripping force estimation unit 32 .

[0025] The gripping force estimation unit 32 acquires the sensing data of the pressure sensor 23 from the sensing data acquisition unit 31 . The gripping force estimation unit 32 estimates the gripping force of the gripping tool 1 by obtaining information indicating the gripping force of the gripping tool 1 corresponding to the internal pressure of the hollow body 21 indicated by the sensing data from a table stored in the internal memory (step ST2 in Figure 6). The gripping force estimating unit 32 outputs information indicating the gripping force of the gripping tool 1 to, for example, a control device (not shown) of the robot.

[0026] In the above-described first embodiment, the gripping force estimation device 3 is configured to estimate the gripping force of the gripping tool 1 having a kirigami structure, which includes an attachment part 12 to which a pulling tool is attached and two object gripping parts 13 that are parts for gripping an object 2, and in which the attachment part 12 is pulled up by the pulling tool, and the two object gripping parts 13 are deformed so as to approach each other. The gripping force estimation device 3 includes a sensing data acquisition part 31 that acquires sensing data of a sensor that observes the deformation of the gripping tool 1, and a gripping force estimation part 32 that estimates the gripping force of the gripping tool 1 with respect to the object 2 based on the sensing data acquired by the sensing data acquisition part 31. Therefore, the gripping force estimation device 3 can estimate the gripping force of the gripping tool 1 having a kirigami structure that is not necessarily driven by a motor.

[0027] It is assumed that the grip force estimation device 3 shown in Fig. 3 is a device separate from the control device that controls the pulling device. However, this is merely an example, and the grip force estimation device 3 shown in Fig. 3 may be implemented in the control device.

[0028] 2, the hollow body 21 is attached to an object gripping portion 13, which is a portion that grips an object 2, among a plurality of portions of the gripper 1. The portion to which the hollow body 21 is attached may be a portion that changes in accordance with the deformation of the gripper 1, and may be attached to, for example, the cutout portion 11 or the attachment portion 12.

[0029] In the gripper 1 shown in FIG. 2, the hollow body 21 is attached to the object gripping part 13, and a pressure sensor 23 for observing the internal pressure of the hollow body 21 is provided as a sensor for observing the deformation of the gripper 1. However, the sensor for observing the deformation of the gripper 1 is not limited to the pressure sensor 23 for observing the internal pressure of the hollow body 21, and may be, for example, a strain sensor for observing the deformation of the object gripping part 13. When the object gripping part 13 is deformed, the strain sensor detects the strain associated with the deformation as an electrical signal. When a strain sensor is used as a sensor for observing the deformation of the gripper 1, a table indicating the correspondence between the electrical signal corresponding to the strain associated with the deformation and the gripping force of the gripper 1 with respect to the object 2 is stored in the internal memory of the gripping force estimation part 32. The gripping force estimating unit 32 estimates the gripping force of the gripping tool 1 by acquiring, from a table stored in an internal memory, information indicating the gripping force of the gripping tool 1 corresponding to the electrical signal indicated by the sensing data.

[0030] Embodiment 2 In embodiment 2, a grip force estimation device 3 is described in which a grip force estimation unit 33 provides sensing data acquired by a sensing data acquisition unit 31 to a first learning model 24 and acquires information indicating the grip force corresponding to the internal pressure indicated by the sensing data from the first learning model 24.

[0031] Fig. 7 is a configuration diagram showing a gripping force estimation device 3 according to embodiment 2. In Fig. 7, the same reference numerals as in Fig. 3 indicate the same or corresponding parts, and detailed explanations will be omitted. Fig. 8 is a hardware configuration diagram showing the hardware of a gripping force estimating device 3 according to embodiment 2. In Fig. 8, the same reference numerals as in Fig. 4 denote the same or corresponding parts, and detailed description thereof will be omitted. The first learning model 24 is realized by, for example, a neural network. During learning, when the first learning model 24 is given pressure data indicating the internal pressure of the hollow body 21 and teacher data indicating the gripping force of the gripping tool 1 against the object 2, it learns the gripping force of the gripping tool 1 that corresponds to the internal pressure indicated by the pressure data. During inference, when sensing data is given, the first learning model 24 outputs information indicating a gripping force corresponding to the internal pressure indicated by the sensing data. 7, the first learning model 24 is provided outside the grip force estimation device 3. However, this is merely an example, and the first learning model 24 may be provided inside the grip force estimation device 3.

[0032] The grip force estimating unit 33 is realized by, for example, a grip force estimating circuit 43 shown in FIG. The gripping force estimation unit 33 acquires the sensing data of the pressure sensor 23 from the sensing data acquisition unit 31 . The gripping force estimating unit 33 estimates the gripping force of the gripping tool 1 on the object 2 based on the sensing data of the pressure sensor 23. Specifically, the grip force estimation unit 33 estimates the grip force of the gripping tool 1 by providing the sensing data to the first learning model 24 and obtaining information from the first learning model 24 indicating the grip force corresponding to the internal pressure indicated by the sensing data. The gripping force estimating unit 33 outputs information indicating the gripping force of the gripping tool 1 to, for example, a control device (not shown) of the robot.

[0033] 7, it is assumed that the sensing data acquisition unit 31 and the grip force estimation unit 33, which are components of the grip force estimation device 3, are each realized by dedicated hardware as shown in Fig. 8. That is, it is assumed that the grip force estimation device 3 is realized by a sensing data acquisition circuit 41 and a grip force estimation circuit 43. Each of the sensing data acquisition circuit 41 and the gripping force estimation circuit 43 may be, for example, a single circuit, a composite circuit, a programmed processor, a parallel programmed processor, an ASIC, an FPGA, or a combination thereof.

[0034] The components of the grip force estimation device 3 are not limited to those realized by dedicated hardware, and the grip force estimation device 3 may be realized by software, firmware, or a combination of software and firmware. When the grip force estimation device 3 is realized by software, firmware, or the like, a program for causing a computer to execute the respective processing procedures in the sensing data acquisition unit 31 and the grip force estimation unit 33 is stored in a memory 51 shown in Fig. 5. Then, a processor 52 shown in Fig. 5 executes the program stored in the memory 51.

[0035] 8 shows an example in which each of the components of the grip force estimation device 3 is realized by dedicated hardware, and FIG 5 shows an example in which the grip force estimation device 3 is realized by software, firmware, etc. However, this is merely an example, and some of the components in the grip force estimation device 3 may be realized by dedicated hardware, and the remaining components may be realized by software, firmware, etc.

[0036] Next, the operation of the grip force estimation device 3 shown in Fig. 7 will be described. Other than the grip force estimation unit 33, the grip force estimation device 3 is the same as that shown in Fig. 3. Therefore, only the operation of the grip force estimation unit 33 will be described here. The gripping force estimation unit 33 acquires the sensing data of the pressure sensor 23 from the sensing data acquisition unit 31 . The gripping force estimation unit 33 provides the sensing data to the first learning model 24 . Then, the grip force estimation unit 33 estimates the grip force of the grip tool 1 by acquiring information indicating the grip force corresponding to the internal pressure indicated by the sensing data from the first learning model 24. The gripping force estimating unit 33 outputs information indicating the gripping force of the gripping tool 1 to, for example, a control device (not shown) of the robot.

[0037] In the above-described second embodiment, the pressure data indicating the internal pressure of the hollow body 21 and the teacher data indicating the gripping force of the gripping tool 1 on the object 2 are given to the first learning model 24, which has already learned the gripping force corresponding to the internal pressure of the hollow body 21. The gripping force estimation device 3 is configured such that the gripping force estimation unit 33 gives the sensing data acquired by the sensing data acquisition unit 31 to the first learning model 24, and acquires information indicating the gripping force corresponding to the sensing data from the first learning model 24. Therefore, the gripping force estimation device 3 can estimate the gripping force of the gripping tool 1 having a kirigami structure that is not necessarily driven by a motor.

[0038] Embodiment 3 In the third embodiment, a gripping force estimation device 3 is described that includes a gripping force estimation unit 35 that estimates the gripping force of the gripping tool 1 against the object 2 based on a captured image that is the imaging result of a visual sensor 25 that captures an image of the gripping tool 1.

[0039] Fig. 9 is a configuration diagram showing a gripping force estimation device 3 according to embodiment 3. In Fig. 9, the same reference numerals as in Fig. 3 and Fig. 7 indicate the same or corresponding parts, and detailed description thereof will be omitted. Fig. 10 is a hardware configuration diagram showing the hardware of a gripping force estimating device 3 according to embodiment 3. In Fig. 10, the same reference numerals as in Fig. 4 and Fig. 8 indicate the same or corresponding parts, and therefore detailed description thereof will be omitted. In FIG. 9, a visual sensor 25 is a sensor for observing the deformation of the gripping tool 1. The visual sensor 25 is a sensor for observing the deformation of the gripping tool 1. Specifically, the visual sensor 25 is a sensor that captures an image of the gripping tool 1 that is deformed by gripping the object 2. The visual sensor 25 outputs sensing data indicating the captured image of the gripping tool 1 to the gripping force estimation device 3. The captured image of the gripping tool 1 may be one or more still images, or may be a video.

[0040] The second learning model 26 is realized, for example, by a neural network. During learning, when the second learning model 26 is given a photographed image of the gripping tool 1 and teacher data indicating the gripping force of the gripping tool 1 against the object 2, it learns the gripping force of the gripping tool 1 that corresponds to the photographed image of the gripping tool 1. The captured images used during learning may be one or more still images, or may be a video. During inference, when sensing data is given, the second learning model 26 outputs information indicating the gripping force corresponding to the captured image indicated by the sensing data. 9, the second learning model 26 is provided outside the grip force estimation device 3. However, this is merely an example, and the second learning model 26 may be provided inside the grip force estimation device 3.

[0041] The gripping force estimation device 3 shown in FIG. 9 includes a sensing data acquisition unit 34 and a gripping force estimation unit 35. The sensing data acquiring unit 34 is realized by, for example, a sensing data acquiring circuit 44 shown in FIG. The sensing data acquisition unit 34 acquires the sensing data of the visual sensor 25 . The sensing data acquisition unit 34 outputs the sensing data of the visual sensor 25 to the gripping force estimation unit 35 .

[0042] The grip force estimating unit 35 is realized by, for example, a grip force estimating circuit 45 shown in FIG. The gripping force estimation unit 35 acquires the sensing data of the visual sensor 25 from the sensing data acquisition unit 34 . The gripping force estimating unit 35 estimates the gripping force of the gripping tool 1 on the object 2 based on the sensing data of the visual sensor 25. Specifically, the grip force estimation unit 35 estimates the grip force of the gripping tool 1 by providing the sensing data to the second learning model 26 and obtaining information indicating the grip force corresponding to the captured image indicated by the sensing data from the second learning model 26. The gripping force estimating unit 35 outputs information indicating the gripping force of the gripping tool 1 to, for example, a control device (not shown) of the robot.

[0043] 9, the grip force estimation unit 35 provides sensing data to the second learning model 26 and acquires information indicating the grip force corresponding to the captured image indicated by the sensing data from the second learning model 26. However, this is merely an example, and the grip force estimation unit 35 may estimate the grip force corresponding to the captured image indicated by the sensing data based on a rule base.

[0044] 9, it is assumed that the sensing data acquisition unit 34 and the grip force estimation unit 35, which are components of the grip force estimation device 3, are each realized by dedicated hardware as shown in Fig. 10. That is, it is assumed that the grip force estimation device 3 is realized by a sensing data acquisition circuit 44 and a grip force estimation circuit 45. Each of the sensing data acquisition circuit 44 and the gripping force estimation circuit 45 may be, for example, a single circuit, a composite circuit, a programmed processor, a parallel programmed processor, an ASIC, an FPGA, or a combination thereof.

[0045] The components of the grip force estimation device 3 are not limited to those realized by dedicated hardware, and the grip force estimation device 3 may be realized by software, firmware, or a combination of software and firmware. When the grip force estimation device 3 is realized by software, firmware, or the like, a program for causing a computer to execute the respective processing procedures in the sensing data acquisition unit 34 and the grip force estimation unit 35 is stored in a memory 51 shown in Fig. 5. Then, a processor 52 shown in Fig. 5 executes the program stored in the memory 51.

[0046] 10 shows an example in which each of the components of the grip force estimation device 3 is realized by dedicated hardware, and FIG 5 shows an example in which the grip force estimation device 3 is realized by software, firmware, etc. However, this is merely an example, and some of the components in the grip force estimation device 3 may be realized by dedicated hardware, and the remaining components may be realized by software, firmware, etc.

[0047] Next, the operation of the gripping force estimating device 3 shown in FIG. 9 will be described. When the pulling device pulls up the two attachment parts 12 of the gripping tool 1, the two object gripping parts 13 come closer to each other. As a result, the two object gripping parts 13 become able to grip the object 2, as shown in FIG. As the pulling tool brings the two attachment parts 12 closer to each other, as shown in FIG. 11, the opening degree of the notch 11 becomes smaller, and the gripping force of the gripping tool 1 becomes stronger. Fig. 11 is an explanatory diagram showing different degrees of opening of the cutout portion 11. Fig. 11 shows an example in which the object 2 is a mandarin orange. FIG. 11A is an explanatory diagram showing the state before the gripping tool 1 grips the object 2, with the notch 11 being wide open. FIG. 11B is an explanatory diagram showing a state in which the object gripping portion 13 lightly touches the object 2, and the notch 11 is narrower than before gripping the object 2. FIG. 11C is an explanatory diagram showing a state in which the gripper 1 grips the object 2, and the cutout portion 11 is narrower than in a state in which the object gripping portion 13 is in light contact with the object 2.

[0048] The visual sensor 25 captures an image of the gripping tool 1 that is deformed as the tension tool pulls up the attachment portion 12. The visual sensor 25 outputs sensing data indicating the captured image of the gripping tool 1 to the gripping force estimation device 3. The sensing data acquisition unit 34 of the gripping force estimation device 3 acquires sensing data from the visual sensor 25 . The sensing data acquisition unit 34 outputs the sensing data of the visual sensor 25 to the gripping force estimation unit 35 .

[0049] The gripping force estimation unit 35 acquires the sensing data of the visual sensor 25 from the sensing data acquisition unit 34 . The gripping force estimation unit 35 provides the sensing data to the second learning model 26 . Then, the grip force estimation unit 35 estimates the grip force of the grip tool 1 by acquiring information indicating the grip force corresponding to the captured image indicated by the sensing data from the second learning model 26. The gripping force estimating unit 35 outputs information indicating the gripping force of the gripping tool 1 to, for example, a control device (not shown) of the robot.

[0050] In the above-described third embodiment, the sensor is the visual sensor 25 that captures an image of the gripping tool 1. The gripping force estimation device 3 is configured such that the sensing data acquisition unit 34 acquires sensing data indicating a captured image that is an imaging result of the visual sensor 25, and the gripping force estimation unit 35 estimates the gripping force of the gripping tool 1 with respect to the object 2 based on the captured image indicated by the sensing data. Therefore, the gripping force estimation device 3 can estimate the gripping force of the gripping tool 1 having a kirigami structure that is not necessarily driven by a motor.

[0051] Embodiment 4 In the fourth embodiment, markers 4 are attached to each of a plurality of locations on the gripping tool 1. A gripping force estimation device 3 including a gripping force estimation unit 36 ​​that provides a captured image, which is an imaging result of the visual sensor 25, to a third learning model 27 and acquires, from the third learning model 27, information indicating gripping forces corresponding to the positions of the plurality of markers 4 shown in the captured image will be described.

[0052] Fig. 12 is a configuration diagram showing a gripping force estimation device 3 according to embodiment 4. In Fig. 12, the same reference numerals as those in Fig. 3, Fig. 7 and Fig. 9 indicate the same or corresponding parts, and detailed description thereof will be omitted. Fig. 13 is a hardware configuration diagram showing the hardware of a gripping force estimating device 3 according to embodiment 4. In Fig. 13, the same reference numerals as those in Fig. 4, Fig. 8, and Fig. 10 indicate the same or corresponding parts, and therefore detailed description thereof will be omitted. In the fourth embodiment, as shown in FIG. 14 described later, markers 4 are attached to a plurality of locations on the gripping tool 1.

[0053] The third learning model 27 is realized, for example, by a neural network. During learning, when the third learning model 27 is given position data indicating the positions of multiple markers 4 shown in the captured image and teacher data indicating the gripping force of the gripping tool 1 against the object 2, it learns the gripping force of the gripping tool 1 corresponding to the positions of the multiple markers 4. When sensing data is given to the third learning model 27 during inference, the third learning model 27 outputs information indicating the gripping forces corresponding to the positions of the multiple markers 4 shown in the captured image indicated by the sensing data. 12, the third learning model 27 is provided outside the grip force estimation device 3. However, this is merely an example, and the third learning model 27 may be provided inside the grip force estimation device 3.

[0054] The gripping force estimation device 3 shown in FIG. 12 includes a sensing data acquisition unit 34 and a gripping force estimation unit 36. The grip force estimating unit 36 ​​is realized by, for example, a grip force estimating circuit 46 shown in FIG. The gripping force estimation unit 36 ​​acquires the sensing data of the visual sensor 25 from the sensing data acquisition unit 34 . The gripping force estimating unit 36 ​​estimates the gripping force of the gripping tool 1 on the object 2 based on the sensing data of the visual sensor 25. Specifically, the grip force estimation unit 36 ​​estimates the grip force of the gripping tool 1 by providing the captured image indicated by the sensing data to a third learning model 27 and obtaining information from the third learning model 27 indicating the grip force corresponding to the positions of multiple markers 4 shown in the captured image. The gripping force estimating unit 36 ​​outputs information indicating the gripping force of the gripping tool 1 to, for example, a control device (not shown) of the robot.

[0055] 12, the grip force estimation unit 36 ​​provides the captured image indicated by the sensing data to the third learning model 27, and acquires information indicating the grip force corresponding to the positions of the multiple markers 4 shown in the captured image from the third learning model 27. However, this is merely an example, and the grip force estimation unit 36 ​​may estimate the grip force corresponding to the positions of the multiple markers 4 shown in the captured image based on a rule base.

[0056] In Fig. 12, it is assumed that the sensing data acquisition unit 34 and the grip force estimation unit 36, which are components of the grip force estimation device 3, are each realized by dedicated hardware as shown in Fig. 13. That is, it is assumed that the grip force estimation device 3 is realized by a sensing data acquisition circuit 44 and a grip force estimation circuit 46. Each of the sensing data acquisition circuit 44 and the grip force estimation circuit 46 may be, for example, a single circuit, a composite circuit, a programmed processor, a parallel programmed processor, an ASIC, an FPGA, or a combination thereof.

[0057] The components of the grip force estimation device 3 are not limited to those realized by dedicated hardware, and the grip force estimation device 3 may be realized by software, firmware, or a combination of software and firmware. When the grip force estimation device 3 is realized by software, firmware, or the like, a program for causing a computer to execute the respective processing procedures in the sensing data acquisition unit 34 and the grip force estimation unit 36 ​​is stored in a memory 51 shown in Fig. 5. Then, a processor 52 shown in Fig. 5 executes the program stored in the memory 51.

[0058] 13 shows an example in which each of the components of the grip force estimation device 3 is realized by dedicated hardware, and FIG 5 shows an example in which the grip force estimation device 3 is realized by software, firmware, etc. However, this is merely an example, and some of the components in the grip force estimation device 3 may be realized by dedicated hardware, and the remaining components may be realized by software, firmware, etc.

[0059] Next, the operation of the gripping force estimating device 3 shown in FIG. 12 will be described. When the pulling device pulls up the two attachment parts 12 of the gripping tool 1, the two object gripping parts 13 come closer to each other. As a result, the two object gripping parts 13 become able to grip the object 2, as shown in FIG. When the distance between the two mounting parts 12 changes, the positions of the markers 4 attached to the respective parts of the gripping tool 1 change, as shown in Fig. 14. In addition, when the distance between the two mounting parts 12 changes, the gripping force of the gripping tool 1 changes. FIG. 14 is an explanatory diagram showing the different positions of the markers 4 attached to the respective parts of the gripping tool 1. In FIG. Figure 14A is an explanatory diagram showing the position of marker 4 when the gripping tool 1 is in a state before gripping object 2, Figure 14B is an explanatory diagram showing the position of marker 4 when the object gripping portion 13 is lightly touching object 2, and Figure 14C is an explanatory diagram showing the position of marker 4 when the gripping tool 1 is gripping object 2. The position of the marker 4 varies depending on the state.

[0060] The visual sensor 25 captures an image of the gripping tool 1 that is deformed by the tension device pulling up the attachment portion 12. The visual sensor 25 outputs sensing data indicating the captured image of the gripping tool 1 to the gripping force estimation device 3. The sensing data acquisition unit 34 of the gripping force estimation device 3 acquires sensing data from the visual sensor 25 . The sensing data acquisition unit 34 outputs the sensing data of the visual sensor 25 to the gripping force estimation unit 36 ​​.

[0061] The gripping force estimation unit 36 ​​acquires the sensing data of the visual sensor 25 from the sensing data acquisition unit 34 . The gripping force estimation unit 36 ​​provides the captured image indicated by the sensing data to the third learning model 27. Then, the grip force estimation unit 36 ​​estimates the grip force of the gripping tool 1 by acquiring information indicating the grip force corresponding to the positions of the multiple markers 4 shown in the captured image from the third learning model 27. The gripping force estimating unit 36 ​​outputs information indicating the gripping force of the gripping tool 1 to, for example, a control device (not shown) of the robot.

[0062] In the above-described fourth embodiment, the third learning model 27 is provided with position data indicating the positions of the multiple markers 4 shown in the captured image and teacher data indicating the gripping force of the gripping tool 1 with respect to the object 2, and has already learned the gripping force corresponding to the positions of the multiple markers 4. The gripping force estimation device 3 is configured such that the gripping force estimation unit 36 ​​provides the captured image, which is the imaging result of the visual sensor 25, to the third learning model 27, and acquires information indicating the gripping force corresponding to the positions of the multiple markers 4 shown in the captured image from the third learning model 27. Therefore, the gripping force estimation device 3 can estimate the gripping force of the gripping tool 1 having a kirigami structure that is not necessarily driven by a motor.

[0063] Embodiment 5. In embodiment 5, a gripping force estimation device 3 is described in which a gripping force estimation unit 38 estimates the gripping force of a gripping tool 1 against an object 2 based on the internal pressure observed by a pressure sensor 23 and the captured image obtained by a visual sensor 25.

[0064] Fig. 15 is a configuration diagram showing a gripping force estimation device 3 according to embodiment 5. In Fig. 15, the same reference numerals as those in Fig. 3, Fig. 7, Fig. 9, and Fig. 12 indicate the same or corresponding parts, and detailed description thereof will be omitted. Fig. 16 is a hardware configuration diagram showing the hardware of a gripping force estimating device 3 according to embodiment 5. In Fig. 16, the same reference numerals as those in Fig. 4, Fig. 8, Fig. 10, and Fig. 13 indicate the same or corresponding parts, and therefore detailed description thereof will be omitted.

[0065] The fourth learning model 28 is realized, for example, by a neural network. During learning, when the fourth learning model 28 is given pressure data indicating the internal pressure of the hollow body 21, a photographed image of the gripping tool 1, and teacher data indicating the gripping force of the gripping tool 1 against the object 2, it learns the gripping force of the gripping tool 1 that corresponds to the internal pressure indicated by the pressure data and the photographed image of the gripping tool 1. During inference, when the fourth learning model 28 is given sensing data from the pressure sensor 23 and sensing data from the visual sensor 25, it outputs information indicating the gripping force corresponding to the internal pressure indicated by the sensing data and the captured image indicated by the sensing data. 15, the fourth learning model 28 is provided outside the grip force estimation device 3. However, this is merely an example, and the fourth learning model 28 may be provided inside the grip force estimation device 3.

[0066] The gripping force estimation device 3 shown in FIG. 15 includes a sensing data acquisition unit 37 and a gripping force estimation unit . The sensing data acquiring unit 37 is realized by, for example, a sensing data acquiring circuit 47 shown in FIG. The sensing data acquisition unit 37 acquires the sensing data of the pressure sensor 23 and the sensing data of the visual sensor 25 . The sensing data acquisition unit 31 outputs the sensing data of the pressure sensor 23 and the sensing data of the visual sensor 25 to the grip force estimation unit 38.

[0067] The grip force estimating unit 38 is realized by, for example, a grip force estimating circuit 48 shown in FIG. The grip force estimation unit 38 acquires the sensing data of the pressure sensor 23 and the sensing data of the visual sensor 25 from the sensing data acquisition unit 31. The gripping force estimating unit 38 estimates the gripping force of the gripping tool 1 on the object 2 based on the sensing data of the pressure sensor 23 and the sensing data of the visual sensor 25 . Specifically, the gripping force estimation unit 38 estimates the gripping force of the gripping tool 1 by providing the sensing data of the pressure sensor 23 and the sensing data of the visual sensor 25 to the fourth learning model 28 and obtaining information from the fourth learning model 28 indicating the gripping force of the gripping tool 1 corresponding to the internal pressure of the hollow body 21 and the captured image of the gripping tool 1. The gripping force estimating unit 38 outputs information indicating the gripping force of the gripping tool 1 to, for example, a control device (not shown) of the robot.

[0068] 15, the grip force estimation unit 38 provides the sensing data of the pressure sensor 23 and the sensing data of the visual sensor 25 to the fourth learning model 28, and acquires information indicating the grip force of the grip tool 1 corresponding to the internal pressure of the hollow body 21 and the captured image of the grip tool 1 from the fourth learning model 28. However, this is merely an example, and the grip force estimation unit 38 may estimate the grip force corresponding to the internal pressure of the hollow body 21 and the captured image of the grip tool 1 based on a rule base.

[0069] In Fig. 15, it is assumed that the sensing data acquisition unit 37 and the grip force estimation unit 38, which are components of the grip force estimation device 3, are each realized by dedicated hardware as shown in Fig. 16. That is, it is assumed that the grip force estimation device 3 is realized by a sensing data acquisition circuit 47 and a grip force estimation circuit 48. Each of the sensing data acquisition circuit 47 and the gripping force estimation circuit 48 may be, for example, a single circuit, a composite circuit, a programmed processor, a parallel programmed processor, an ASIC, an FPGA, or a combination thereof.

[0070] The components of the grip force estimation device 3 are not limited to those realized by dedicated hardware, and the grip force estimation device 3 may be realized by software, firmware, or a combination of software and firmware. When the grip force estimation device 3 is realized by software, firmware, or the like, a program for causing a computer to execute the respective processing procedures in the sensing data acquisition unit 37 and the grip force estimation unit 38 is stored in a memory 51 shown in Fig. 5. Then, a processor 52 shown in Fig. 5 executes the program stored in the memory 51.

[0071] 16 shows an example in which each of the components of the grip force estimation device 3 is realized by dedicated hardware, and FIG 5 shows an example in which the grip force estimation device 3 is realized by software, firmware, etc. However, this is merely an example, and some of the components in the grip force estimation device 3 may be realized by dedicated hardware, and the remaining components may be realized by software, firmware, etc.

[0072] Next, the operation of the gripping force estimating device 3 shown in FIG. 15 will be described. The pressure sensor 23 monitors the internal pressure of the tube 22 as the internal pressure of the hollow body 21 . The pressure sensor 23 outputs sensing data indicating the internal pressure of the hollow body 21 to the gripping force estimation device 3. The visual sensor 25 captures an image of the gripping tool 1 that is deformed by the tension device pulling up the attachment portion 12. The visual sensor 25 outputs sensing data indicating the captured image of the gripping tool 1 to the gripping force estimation device 3.

[0073] The sensing data acquisition unit 37 of the gripping force estimation device 3 acquires the sensing data of the pressure sensor 23 and the sensing data of the visual sensor 25 . The sensing data acquisition unit 37 outputs the sensing data of the pressure sensor 23 and the sensing data of the visual sensor 25 to the grip force estimation unit 38.

[0074] The grip force estimation unit 38 acquires the sensing data of the pressure sensor 23 and the sensing data of the visual sensor 25 from the sensing data acquisition unit 37 . The grip force estimation unit 38 provides the sensing data of the pressure sensor 23 and the sensing data of the visual sensor 25 to the fourth learning model 28. Then, the gripping force estimation unit 38 estimates the gripping force of the gripping tool 1 by acquiring information from the fourth learning model 28 indicating the gripping force of the gripping tool 1 corresponding to the internal pressure of the hollow body 21 indicated by the sensing data of the pressure sensor 23 and the captured image indicated by the sensing data of the visual sensor 25. The gripping force estimating unit 38 outputs information indicating the gripping force of the gripping tool 1 to, for example, a control device (not shown) of the robot.

[0075] In the above-described fifth embodiment, the gripping force estimation device 3 shown in FIG. 15 is configured so that the sensing data acquisition unit 37 acquires sensing data indicating the internal pressure observed by the pressure sensor 23 and sensing data indicating the captured image which is the imaging result of the visual sensor 25, and the gripping force estimation unit 38 estimates the gripping force of the gripping tool 1 on the object 2 based on the internal pressure observed by the pressure sensor 23 and the captured image. Therefore, the gripping force estimation device 3 shown in FIG. 15 can estimate the gripping force of the gripping tool 1 having a kirigami structure which is not necessarily driven by a motor. In addition, the gripping force estimation device 3 shown in FIG. 15 can improve the estimation accuracy of the gripping force more than the gripping force estimation device 3 shown in FIG. 7 or the gripping force estimation device 3 shown in FIG. 9.

[0076] In addition, the present disclosure allows free combination of the respective embodiments, modification of any of the components of each embodiment, or omission of any of the components of each embodiment. [Industrial Applicability]

[0077] The present disclosure is suitable for a grip force estimation device and a grip force estimation method. [Explanation of symbols]

[0078] 1 gripping tool, 2 object, 3 gripping force estimation device, 4 marker, 11 cutout portion, 12 mounting portion, 13 object gripping portion, 21 hollow body, 22 tube, 23 pressure sensor, 24 first learning model, 25 visual sensor, 26 second learning model, 27 third learning model, 28 fourth learning model, 31 sensing data acquisition portion, 32, 33 gripping force estimation portion, 34 sensing data acquisition portion, 35, 36 gripping force estimation portion, 37 sensing data acquisition portion, 38 gripping force estimation portion, 41 sensing data acquisition circuit, 42, 43 gripping force estimation circuit, 44 sensing data acquisition circuit, 45, 46 gripping force estimation circuit, 47 sensing data acquisition circuit, 48 gripping force estimation circuit, 51 memory, 52 processor.

Claims

1. A gripping force estimation device that estimates the gripping force of a gripping tool having a kirigami structure, the device comprising: an attachment portion to which a tensile tool is attached; and two object gripping portions that are portions that grip an object; and the attachment portion is pulled up by the tensile tool, causing the two object gripping portions to deform so as to approach each other, a sensing data acquisition unit that acquires sensing data including information on the internal pressure detected by a sensor that detects the internal pressure of the hollow body attached to the object gripping unit, the internal pressure changing due to deformation of the gripping tool; a gripping force estimating unit that estimates the gripping force of the gripping tool on the object based on information about the internal pressure included in the sensing data acquired by the sensing data acquiring unit. It is characterized by:

2. The gripping force estimation unit obtains information indicating the gripping force of the gripping tool corresponding to the information on the internal pressure included in the sensing data from a table indicating a correspondence relationship between the internal pressure of the hollow body and the gripping force of the gripping tool on the object. The grip force estimation device according to claim 1 .

3. The gripping force estimation unit is provided with pressure data indicating the internal pressure of the hollow body and data indicating the gripping force of the gripping tool on the object, and provides the sensing data to a first learning model that has learned the gripping force corresponding to the internal pressure of the hollow body, and obtains from the first learning model information on the gripping force that corresponds to the information on the internal pressure included in the sensing data. The grip force estimation device according to claim 1 .

4. The sensor includes a pressure sensor and a visual sensor that captures an image of the gripping tool, the sensing data acquisition unit acquires the sensing data including information on the internal pressure detected by the pressure sensor and a captured image captured by the visual sensor; The gripping force estimation unit estimates the gripping force of the gripping tool on the object based on the internal pressure information and the captured image. The grip force estimation device according to claim 1 .

5. A gripping force estimation device for estimating the gripping force of a gripping tool having a kirigami structure, which comprises an attachment part to which a pulling tool is attached and two object gripping parts which are parts for gripping an object, and in which the attachment part is pulled up by the pulling tool, causing the two object gripping parts to deform so as to approach each other, a sensing data acquisition unit that acquires sensing data including an image captured by a visual sensor that captures an image of the gripping tool; a gripping force estimating unit that estimates the gripping force of the gripping tool on the object based on the captured image included in the sensing data acquired by the sensing data acquiring unit. It is characterized by:

6. The grip force estimation unit is provided with the captured image taken by the visual sensor and data indicating the grip force of the gripping tool on the object, and provides the sensing data to a second learning model that has learned the grip force corresponding to the captured image, and obtains information on the grip force corresponding to the captured image contained in the sensing data from the second learning model. The grip force estimation device according to claim 4 .

7. The gripping force estimation unit is provided with position data included in the photographed image indicating the positions of multiple markers attached to the gripping tool and data indicating the gripping force of the gripping tool relative to the object, and provides the sensing data to a third learning model that has learned the gripping force corresponding to the positions of the multiple markers, and obtains information on the gripping force corresponding to the positions of the multiple markers included in the photographed image from the third learning model. The grip force estimation device according to claim 4 .

8. A method using a gripping force estimation device for estimating the gripping force of a gripping tool having a kirigami structure, which comprises an attachment part to which a pulling tool is attached and two object gripping parts which are parts for gripping an object, and in which the attachment part is pulled up by the pulling tool, causing the two object gripping parts to deform so as to approach each other, a step of acquiring, by a sensing data acquiring unit, sensing data including information on the internal pressure detected by a sensor that detects the internal pressure of the hollow object attached to the object gripping unit, the internal pressure changing due to deformation of the gripping tool; and estimating the gripping force of the gripping tool on the object by a gripping force estimating unit based on information about the internal pressure included in the sensing data acquired by the sensing data acquiring unit. It is characterized by:

9. A program executed on a computer of a gripping force estimation device that estimates the gripping force of a gripping tool having a kirigami structure, the gripping tool having an attachment part to which a pulling tool is attached and two object gripping parts that are parts for gripping an object, and that deforms so that the two object gripping parts approach each other when the attachment part is pulled up by the pulling tool, The computer, a process of acquiring sensing data including information on the internal pressure detected by a sensor that detects the internal pressure of the hollow object attached to the object gripping unit, the internal pressure changing due to deformation of the gripping tool; and estimating the gripping force of the gripping tool on the object based on the information on the internal pressure included in the sensing data. It is characterized by:

10. A method for a gripping force estimation device that estimates the gripping force of a gripping tool having a kirigami structure, which includes an attachment part to which a pulling tool is attached and two object gripping parts that are parts for gripping an object, and in which the attachment part is pulled up by the pulling tool, causing the two object gripping parts to deform so as to approach each other, comprising: acquiring, by a sensing data acquisition unit, sensing data including an image captured by a visual sensor that captures an image of the gripping tool; and estimating the gripping force of the gripping tool on the object by a gripping force estimating unit based on the captured image included in the sensing data acquired by the sensing data acquiring unit. It is characterized by:

11. A program to be executed by a computer of a gripping force estimation device that estimates the gripping force of a gripping tool having a kirigami structure, the device comprising an attachment part to which a pulling tool is attached and two object gripping parts that are parts for gripping an object, and in which the attachment part is pulled up by the pulling tool, causing the two object gripping parts to deform so as to approach each other, A process of acquiring sensing data including an image captured by a visual sensor that captures an image of the gripping tool; and estimating the gripping force of the gripping tool on the object based on the captured image included in the sensing data. It is characterized by:

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