JP6947082B2 - Fall variation predictors, methods, programs, and robot systems - Google Patents

Description

The present invention relates to a drop variation prediction device, a drop variation prediction method, a drop variation prediction program, and a robot system.

There is a pick-and-place device as one of the devices for transporting workpieces such as parts and products in a factory production line. The pick-and-place device includes, for example, a suction pad or the like, which holds a work piece by sucking and holding the work piece. Release the suction and place the work.

As an example of a pick-and-place device that adsorbs and conveys an object to be conveyed, for example, a substrate transfer device that vacuum-adsorbs and conveys a substrate such as a wafer in a semiconductor manufacturing process has been proposed (see, for example, Patent Document 1). ). In the substrate transfer device described in Patent Document 1, the vacuum pressure of the substrate suction portion is detected in consideration of the adhesion of foreign matter such as dust to the vacuum suction surface, and the detected pressure data is calculated. Next, the change in the vacuum pressure of the suction part of the substrate to be processed is predicted.

Japanese Unexamined Patent Publication No. 2007-281573

For example, when a work containing a packaging material and contents packaged with the packaging material is to be transported, wrinkles or the like are generated on the suction surface, so that the work falls even when suction transportation is performed under the same conditions. Depending on whether it is done or not, the occurrence of the fall will vary. When the drop varies, it is necessary to set the operation command values such as the suction pressure and the acceleration of the robot arm in anticipation of the variation, so it is important for the user to know the variation in the drop.

However, in the technique described in Patent Document 1, the object to be transported is a substrate, and the variation in drop due to the wrinkling of the object to be transported is not taken into consideration. Further, when the object to be transported is a work including the packaging material and the contents packaged with the packaging material, wrinkles are randomly generated in the packaging material, so that the object is actually detected as in the technique described in Patent Document 1. It is difficult to predict the subsequent change in the suction pressure of the suction part based on the suction pressure of the suction part.

The present invention has been made in view of the above points, and it is possible to predict the variation in the fall of the work when the work including the packaging material and the contents packaged in the packaging material is adsorbed and conveyed. It is an object of the present invention to provide a predictor, a method, and a program, and a robot system that operates according to the predicted variation in the fall of a workpiece.

In order to achieve the above object, the drop variation predictor according to the present invention is a work including a packaging material and a content packaged with the packaging material, and is attracted and held by a suction member attached to the robot arm. A reception unit that receives information indicating the type of the material of the packaging material of the workpiece to be conveyed, a type of the material of the packaging material, and a packaging material for each type of the material of the packaging material are subjected to the suction member under predetermined conditions. Corresponds to the information indicating the type of the material of the packaging material received at the reception unit by referring to the correlation information indicating the correlation between the variation in the adsorption pressure between the packaging material and the adsorption member when adsorbed. Based on the deviation of the suction pressure derived from the lead-out unit that derives the variation of the suction pressure and the variation of the suction pressure derived by the lead-out unit, the variation of the probability of the work falling when the robot arm conveys the work is predicted. It is configured to include a prediction unit.

According to the drop variation prediction device according to the present invention, the receiving portion is a work including the packaging material and the contents packaged in the packaging material, and is attracted and held by the suction member attached to the robot arm and conveyed. Receives information indicating the type of material used for the packaging material of the work. Then, when the lead-out unit sucks the packaging material by the suction member under a predetermined condition for each type of the packaging material and the type of the packaging material, the suction pressure varies between the packaging material and the suction member. With reference to the correlation information indicating the correlation with, the variation of the adsorption pressure corresponding to the information indicating the type of the packaging material received by the reception unit is derived. Further, the prediction unit predicts the variation in the probability that the work will fall when the robot arm conveys the work, based on the variation in the suction pressure derived by the out-licensing unit.

In this way, by referring to the correlation information showing the correlation between the type of packaging material and the variation in suction pressure, the variation in suction pressure corresponding to the information indicating the type of material of the received packaging material can be derived. Therefore, it is possible to derive the variation in the suction pressure that correlates with the variation in the drop of the work, so that the variation in the drop of the work when the work including the packaging material and the contents packaged in the packaging material is sucked and conveyed can be derived. Can be predicted.

Further, the receiving unit further receives at least one information of the thickness for each type of the packaging material, the content information, and the suction member information as correction information, and the derivation unit receives the information as correction information. It is possible to correct the variation in the suction pressure obtained from the correlation information based on the wrinkle generation state of the packaging material corresponding to the correction information received by the reception unit. As a result, the variation in the suction pressure is derived by reflecting the wrinkle generation state of the packaging material in more detail, so that the variation in the fall of the work can be predicted more accurately.

Further, when the correction information received by the receiving unit includes the information on the thickness of the packaging material, the lead-out unit is such that the thicker the packaging material, the smaller the variation in the suction pressure. Can be corrected to. This is because the thicker the packaging material, the less likely it is that wrinkles will occur in the packaging material.

Further, when the correction information received by the receiving unit includes information indicating the mass of the content as the information of the content, the derivation unit increases the suction pressure as the mass of the content increases. It can be corrected so that the variation of is small. This is because the heavier the mass of the contents, the stronger the tension of the packaging material, and the less likely it is that wrinkles are generated in the packaging material.

Further, when the correction information received by the receiving unit includes information indicating the filling rate of the content with respect to the packaging material as the information of the content, the derivation unit increases the filling rate. It can be corrected so that the variation in the suction pressure becomes small. This is because the higher the filling rate, the stronger the tension of the packaging material, and the less likely it is that wrinkles will occur in the packaging material.

Further, when the correction information received by the receiving unit includes information indicating the number of the suction members used for sucking and holding one work as the information of the suction member, the lead-out unit is said to be said. It can be corrected so that the larger the number, the smaller the variation in the suction pressure. This is because as the number of the suction members increases, the tension of the packaging material becomes stronger and wrinkles of the packaging material are less likely to occur.

Further, when the correction information received by the receiving unit includes, as the information of the suction member, information indicating whether or not the suction member has a function of improving the suction pressure, the lead-out unit receives the information. When the suction member has the function, it can be corrected so that the variation in the suction pressure becomes small. When the suction member has such a function, it is possible to suppress the generation of wrinkles in the packaging material and the generation of a gap between the packaging material and the suction member.

Further, the drop variation prediction device according to the present invention provides information indicating the variation in the fall probability of the work predicted by the prediction unit, and the number of trials related to the operation of the robot arm according to the variation in the fall probability of the work. Includes an output unit that outputs at least one piece of information indicating the instruction of the robot arm and information for changing the setting of the operation command value for operating the robot arm according to the variation in the probability of the work falling. Can be configured with. As a result, the user can confirm and use the predicted variation in the fall of the work.

Further, the predetermined condition means that the thickness of the packaging material is a reference thickness and the contents are in a reference state. For example, the thickness of the packaging material is the minimum thickness that can withstand practical use and the contents. It can be the case when the object is empty.

Further, in the drop variation prediction method according to the present invention, the computer is a work including the packaging material and the contents packaged in the packaging material, and is attracted and held by the suction member attached to the robot arm and conveyed. The above-mentioned case where the information indicating the type of the material of the packaging material of the work is received, and the packaging material is adsorbed by the suction member under predetermined conditions for each type of the material of the packaging material and the type of the material of the packaging material. With reference to the correlation information showing the correlation with the variation in the suction pressure between the packaging material and the suction member, the variation in the suction pressure corresponding to the received information indicating the type of the material of the packaging material is derived and derived. This is a method of executing a process including predicting a variation in the probability of the work falling when the robot arm conveys the work, based on the variation in the suction pressure.

Further, the drop variation prediction program according to the present invention is a work including a packaging material and the contents packaged in the packaging material, and the computer is attracted and held by a suction member attached to the robot arm and conveyed. When the packaging material is adsorbed by the suction member under predetermined conditions for each of the reception unit that receives information indicating the type of the material of the packaging material of the work, the type of the material of the packaging material, and the type of the material of the packaging material. The adsorption pressure corresponding to the information indicating the type of the material of the packaging material received at the reception unit with reference to the correlation information indicating the correlation with the variation in the adsorption pressure between the packaging material and the adsorption member. deriving unit that derives a variation in, and, based on the variation of the suction pressure derived by the deriving unit, the prediction unit which the robot arm is to predict the variation of drop probability of the work at the time of conveying the workpiece It is a program to function as.

Further, the robot system according to the present invention operates when information for changing the setting of an operation command value for operating the robot arm is output from the output unit of the drop variation prediction device. It is configured to include a control unit that controls the robot arm to operate based on the operation command value changed based on the information for changing the setting of the command value.

According to the drop variation prediction device, method, and program according to the present invention, the type of packaging material received can be determined by referring to the correlation information indicating the correlation between the type of packaging material and the variation in suction pressure. By deriving the variation in the suction pressure corresponding to the indicated information, it is possible to predict the variation in the drop of the work when the work including the packaging material and the contents packaged in the packaging material is sucked and conveyed. Further, according to the robot system according to the present invention, the robot arm can be operated according to the predicted variation in the fall of the work.

It is a figure for demonstrating the suction of a work by a suction pad. It is a figure for demonstrating the wrinkle which occurs in the wrapping material of the work including the wrapping material and the contents packaged with the wrapping material. It is a block diagram which shows the hardware composition of the drop variation prediction device. It is a block diagram which shows the example of the functional structure of the drop variation prediction device. It is a figure which shows an example of the reception screen in 1st Embodiment. It is a figure which shows an example of the correlation information database in 1st Embodiment. It is a figure for demonstrating the prediction processing of the variation of a fall. It is a figure which shows an example of the prediction result screen. It is a flowchart which shows the operation flow of the drop variation prediction apparatus which concerns on 1st Embodiment. It is a figure which shows an example of the reception screen in 2nd Embodiment. It is a figure which shows an example of the correlation information database in 2nd Embodiment. It is a figure for demonstrating the derivation process of the variation of suction pressure in 2nd Embodiment. It is a figure for demonstrating the occurrence state of wrinkles according to the mass of contents. It is a figure for demonstrating the occurrence state of the wrinkle according to the filling rate of the contents with respect to the packaging material. It is a figure for demonstrating the occurrence state of wrinkles according to the number of suction pads for one work. It is a graph which shows an example of the relationship between the mass of contents and the variation of suction pressure at the time of deriving the variation of suction pressure. It is a graph which shows an example of the relationship between the filling rate of the contents with respect to the packaging material, and the variation of the suction pressure at the time of deriving the variation of the suction pressure. It is a graph which shows an example of the relationship between the number of suction pads for one work, and the variation of suction pressure at the time of deriving the variation of suction pressure. It is a figure for demonstrating the occurrence state of wrinkles according to the thickness of a packaging material. It is a graph which shows an example of the relationship between the thickness of the packaging material and the variation of the suction pressure at the time of deriving the variation of the suction pressure. It is a figure for demonstrating the occurrence situation of wrinkle according to the presence or absence of an option for wrinkle. It is a figure for demonstrating an example of the derivation process of the variation of suction pressure in 2nd Embodiment. It is a figure for demonstrating the safety factor according to the prediction result of the fall variation. It is a flowchart which shows the operation flow of the drop variation prediction apparatus which concerns on 2nd Embodiment. It is a figure for demonstrating another example of the output of the prediction result of the drop variation. It is a block diagram which shows the schematic structure of a robot system.

Hereinafter, an example of the embodiment of the present invention will be described with reference to the drawings. The same reference numerals are given to the same or equivalent components and parts in each drawing. In addition, the dimensional ratios in the drawings are exaggerated for convenience of explanation and may differ from the actual ratios.

(First Embodiment)

The drop variation prediction device of the present embodiment predicts the drop variation of the work when the work is conveyed by the robot provided with the robot arm. As shown in FIG. 1, the robot picks up the work W by an elastic suction pad P attached to the tip of a robot arm R having a vacuum pipe, conveys the work W, and places the work W at the destination, so-called. It is a pick-and-place device. Pickup of the workpiece W is sucked by the suction pressure p _A which is set in the robot performed by a negative pressure within the suction pad P, place of work W is carried out by releasing the suction.

As shown in FIG. 1, when wrinkles do not occur on the surface of the work W, the suction pressure p _{A set in the robot and the suction pressure p B on} the suction surface between the suction pad P and the work W are approximately p. There is a relationship of _A = p _B. Therefore, in this case, if the suction pressure p _A of the extent to which the workpiece W is not dropped are properly configured, the workpiece W does not fall during transport.

In the present embodiment, as shown in FIG. 2, the work W includes a packaging material O and a content C packaged with the packaging material O. When such a work W is sucked, the suction of the suction pad P causes wrinkles that communicate the inside and the outside of the suction pad P in the region including the suction portion of the packaging material O by the suction pad P. This situation is shown by the alternate long and short dash line in FIG. The right figure of FIG. 2 is a diagram schematically showing a case where the portion inside the alternate long and short dash line in the left figure of FIG. 2 is viewed from above. The generation of this wrinkle, will be a gap between the suction pad P and packaging material O, the adsorption pressure p of the suction surface between the adsorption pressure p _A which is set in the robot, the suction pad P and the workpiece W _B has a relationship of _{p A} > p _B. If the suction pressure p _{B on the} suction surface becomes too small, the work W will fall. Wrinkles occurring packaging material O, for each adsorption, wrinkle depth, number, position in a random state such as, for every change wrinkling states, in variations every time the value of the adsorption pressure p _B of the suction surface Become.

Therefore, the suction pressure p _A, and even if the operation command value such as acceleration of the robot arm R are the same, so that the variation in the falling of the workpiece W occurs. As described above, the drop variation prediction device according to the present embodiment predicts the drop variation of the work W when the work W including the packaging material O and the content C packaged with the packaging material O is adsorbed and conveyed. Is what you do.

FIG. 3 is a block diagram showing a hardware configuration of the drop variation prediction device 10 according to the present embodiment. As shown in FIG. 3, the drop variation prediction device 10 includes a CPU (Central Processing Unit) 11, a ROM (Read Only Memory) 12, a RAM (Random Access Memory) 13, a storage 14, an input unit 15, a monitor 16, and an optical disk drive. It has a device 17 and a communication interface 18. The configurations are connected to each other via the bus 19 so as to be communicable with each other.

In the present embodiment, the ROM 12 or the storage 14 stores a drop variation prediction program that executes the drop variation prediction process. The CPU 11 is a central arithmetic processing unit that executes various programs and controls each configuration. That is, the CPU 11 reads the program from the ROM 12 or the storage 14, and executes the program using the RAM 13 as a work area. The CPU 11 controls each of the above configurations and performs various arithmetic processes according to the program recorded in the ROM 12 or the storage 14.

The ROM 12 stores various programs and various data. The RAM 13 temporarily stores a program or data as a work area. The storage 14 is composed of an HDD (Hard Disk Drive) or an SSD (Solid State Drive), and stores various programs including an operating system and various data.

The input unit 15 includes a pointing device such as a keyboard 151 and a mouse 152, and is used to perform various inputs. The monitor 16 is, for example, a liquid crystal display and displays various information such as a prediction result. The monitor 16 may adopt a touch panel system and function as an input unit 15. The optical disc drive device 17 reads data stored in various recording media (CD-ROM, Blu-ray disc, etc.), writes data to the recording medium, and the like.

The communication interface 18 is an interface for communicating with other devices, and standards such as Ethernet (registered trademark), FDDI, and Wi-Fi (registered trademark) are used.

Next, the functional configuration of the drop variation prediction device 10 according to the first embodiment will be described.

FIG. 4 is a block diagram showing an example of the functional configuration of the drop variation prediction device 10. As shown in FIG. 4, the drop variation prediction device 10 has a reception unit 22, a suction pressure variation derivation unit 24, a drop variation prediction unit 26, and an output unit 28 as functional configurations. Each functional configuration is realized by the CPU 11 reading the drop variation prediction program stored in the ROM 12 or the storage 14 and expanding and executing the drop variation prediction program in the RAM 13. The suction pressure variation derivation unit 24 is an example of the derivation unit of the present invention, and the drop variation prediction unit 26 is an example of the prediction unit of the present invention.

The reception unit 22 receives information indicating the type of the material of the packaging material O of the work W including the packaging material O and the content C packaged with the packaging material O. The reception unit 22 displays the reception screen 40 as shown in FIG. 5, for example, on the monitor 16. In the example of FIG. 5, the reception screen 40 is a pull-down menu 41 for selecting and inputting the material type of the packaging material O from preset options, and an OK button selected when confirming the input information. 42, and a cancel button 43 selected when canceling the entered information. Instead of the pull-down menu 41, an input box for directly inputting text data indicating the material of the packaging material O may be used. When the user inputs information indicating the material of the packaging material O from the pull-down menu 41 of the reception screen 40 displayed on the monitor 16 and selects the OK button 42, the reception unit 22 receives the package input on the reception screen 40. Accepts information indicating the type of material of material O.

The suction pressure variation derivation unit 24 refers to the correlation information database (DB) 30 and derives the suction pressure variation corresponding to the information indicating the type of the material of the packaging material O received by the reception unit 22. The correlation information DB 30 is between the material type of the packaging material O and the packaging material O and the suction pad P when the packaging material O is sucked by the suction pad P under predetermined conditions for each type of the material of the packaging material O. It is a database that stores the correlation information showing the correlation with the variation of the adsorption pressure of.

FIG. 6 shows an example of the correlation information DB 30. In the example of FIG. 6, the correlation information DB 30 is given an identification information “ID” for each type of material of the packaging material O, and the “material of the packaging material” and the “adsorption pressure variation” are stored in association with each other. Has been done.

"Material of packaging material" is information indicating the type of material of packaging material O. In the example of FIG. 6, information indicating the type of material of the packaging material O, which is formed by laminating a plurality of types of materials, is shown. In FIG. 6, "OPP / PE" means a two-layer structure of OPP (biaxially stretched polypropylene) and PE (polyethylene), and "PET / CPP" means PET (polyethylene) and CPP (polyethylene). It indicates that it has a two-layer structure with (axial-stretched polypropylene), and "PET / Al / CPP" indicates that it has a three-layer structure of PET, Al (aluminum), and CPP, and "OPP / EVOH / PE". "" Indicates that it has a three-layer structure of OPP, EVOH (ethylene vinyl alcohol copolymer), and PE.

"Suction pressure variation" is obtained by repeatedly operating the actual machine under predetermined conditions, measuring the suction pressure between the packaging material O and the suction pad P, calculating the dispersion of the measured values, and correlating as correlation information. It is stored in the information DB 30. The predetermined conditions can be, for example, the following conditions as the conditions at which wrinkles are most likely to occur.

-The content C to be packaged in the packaging material O is emptied, and the tension of the packaging material O is the minimum.-The thickness of the packaging material O is the minimum (generally about 30 μm).

Generally, the harder the packaging material O, the less likely it is to wrinkle, and the variation in suction pressure is small. Although FIG. 6 shows an example in which the variation in suction pressure is represented by dispersion, an index representing other variation such as standard deviation may be used.

Specifically, the adsorption pressure variation derivation unit 24 searches for information indicating the type of the material of the packaging material O received by the reception unit 22 from the “material of the packaging material” in the correlation information DB 30, and the searched “material” is searched. By acquiring the "adsorption pressure variation" corresponding to the "packaging material", the adsorption pressure variation corresponding to the information indicating the type of the packaging material O material received by the reception unit 22 is derived.

The drop variation prediction unit 26 predicts the drop variation of the work W when the robot arm R conveys the work W based on the variation of the suction pressure derived by the suction pressure variation derivation unit 24. The variation in the drop of the work W correlates with the variation in the suction pressure between the packaging material O and the suction pad P, and the larger the variation in the suction pressure, the larger the variation in the drop of the work W. Therefore, as shown in FIG. 7, for example, the drop variation prediction unit 26 sets a plurality of degrees of drop variation, such as “large”, “medium”, and “small”, according to the magnitude of the variation in suction pressure. Predict in stages.

The output unit 28 outputs screen information indicating the prediction result by the drop variation prediction unit 26 to the monitor 16. As a result, the monitor 16 displays the prediction result screen 50 as shown in FIG. 8, for example. In the example of FIG. 8, the prediction result screen 50 includes a message 51 indicating the prediction result and a confirmation button 52 selected when the prediction result is confirmed.

Next, the operation of the drop variation prediction device 10 according to the first embodiment will be described.

FIG. 9 is a flowchart showing a flow of operations executed by the CPU 11 of the drop variation prediction device 10. The drop variation prediction process is performed by the CPU 11 reading the drop variation prediction program from the ROM 12 or the storage 14, deploying it in the RAM 13 and executing it.

As the reception unit 22, the CPU 11 displays the reception screen 40 as shown in FIG. 5, for example, on the monitor 16, and the packaging material O of the work W including the packaging material O and the contents C packaged with the packaging material O. Receives information indicating the type of the material of (step S12).

The CPU 11 searches the information indicating the type of the material of the packaging material O received by the reception unit 22 from the “material of the packaging material” of the correlation information DB 30 as the suction pressure variation derivation unit 24, and searches for the “packaging material”. By acquiring the "suction pressure variation" corresponding to the "material", the suction pressure variation corresponding to the information indicating the type of the material of the packaging material O received by the reception unit 22 is derived (step S14).

As the drop variation prediction unit 26, the CPU 11 sets the degree of drop variation to “large” according to the magnitude of the suction pressure variation derived by the suction pressure variation derivation unit 24, for example, as shown in FIG. Prediction is made in a plurality of stages such as "medium" and "small" (step S16).

As the output unit 28, the CPU 11 outputs screen information indicating the prediction result by the drop variation prediction unit 26 to the monitor 16 (step S18). As a result, the monitor 16 displays the prediction result screen 50 as shown in FIG. 8, for example. Then, the CPU 11 ends the drop variation prediction process.

As described above, according to the drop variation prediction device 10 of the first embodiment, the packaging material O is subjected to the suction pad P under predetermined conditions for each type of the material of the packaging material O and the type of the material of the packaging material O. Information indicating the type of material of the received packaging material O with reference to the correlation information DB30 in which the correlation information indicating the correlation with the variation in the adsorption pressure between the packaging material O and the adsorption pad P when adsorbed is stored. Derivation of the variation in suction pressure corresponding to. Since there is a correlation that the larger the variation in the suction pressure, the larger the variation in the drop of the work W, the drop of the work W when the robot arm R conveys the work W according to the magnitude of the variation in the suction pressure. Variation can be predicted. As a result, the user can know whether or not the work W falls in a variation without actually carrying the work W many times and confirming it, and considers the predicted variation in the work W drop. Therefore, it can be determined whether or not it is necessary to adjust the operation command value of the robot.

(Second Embodiment)

In the second embodiment, the variation in the suction pressure derived based on the type of the material of the packaging material O is used as the correction information using the information other than the type of the material of the packaging material O that affects the wrinkle generation state. A case of deriving the corrected variation of the suction pressure will be described.

Since the hardware configuration of the drop variation prediction device according to the second embodiment is the same as that of the drop variation prediction device 10 according to the first embodiment, the description thereof will be omitted.

As shown in FIG. 4, the drop variation prediction device 210 according to the second embodiment has a reception unit 222, a suction pressure variation derivation unit 224, a drop variation prediction unit 26, and an output unit 228 as functional configurations. .. Each functional configuration is realized by the CPU 11 reading the drop variation prediction program stored in the ROM 12 or the storage 14 and expanding and executing the drop variation prediction program in the RAM 13. The suction pressure variation derivation unit 224 is an example of the derivation unit of the present invention, and the drop variation prediction unit 26 is an example of the prediction unit of the present invention.

In addition to the information indicating the type of the material of the packaging material O, the reception unit 222 provides the information on the thickness of the packaging material O, the information on the content C, and the information on the suction pad P according to the type of the material of the packaging material O. It is accepted as correction information for correcting the variation in suction pressure. The information of the content C is, for example, information indicating the mass of the content C and information indicating the filling rate of the content C with respect to the packaging material O. The information on the suction pad P is, for example, information indicating the number of suction pads P used for sucking and holding one work W, and a function that the suction pad P improves the suction pressure on the suction surface with the packaging material O. It is information indicating whether or not it has.

The reception unit 222 displays the reception screen 240 as shown in FIG. 10, for example, on the monitor 16. In the example of FIG. 10, the reception screen 240 has an input area 44 including a pull-down menu for selecting and inputting the material type of the packaging material O, an input box for inputting the thickness of the packaging material O, and the contents. An input area 45 including an input box for inputting each of the mass and the filling rate of the content C which is the information of C, an input box for inputting the number of suction pads P which is the information of the suction pad P, and an input box for inputting the number of suction pads P which is the information of the suction pad P. It includes an input area 46 including a check box for selecting a function of the suction pad P, an OK button 42, and a cancel button 43. An input box may be used instead of the pull-down menu included in the input area 44. Further, the pull-down menu may be used instead of each of the text boxes included in the input areas 44, 45, and 46. Further, the pull-down menu may be used instead of the check box included in the input area 46.

When the user inputs information indicating the type of the material of the packaging material O and correction information in the input areas 44, 45, 46 of the reception screen 240 displayed on the monitor 16 and selects the OK button 42, the reception unit 222 Accepts the information indicating the type of the material of the packaging material O and the correction information input on the reception screen 240.

The suction pressure variation derivation unit 224 generates wrinkles in the packaging material O corresponding to the correction information received by the reception unit 222 for the variation in the suction pressure acquired from the correlation information DB 230 according to the type of the material of the packaging material O. Make corrections based on the situation.

FIG. 11 shows an example of the correlation information DB 230. In the example of FIG. 11, the correlation information DB 230 is given an "ID" which is identification information for each type of material of the packaging material O, and includes "material of packaging material", "maximum adsorption pressure variation", and "packaging". "The maximum mass of the contents that the material does not break" is stored in association with it. The “maximum suction pressure variation” is the same as the “suction pressure variation” of the correlation information DB 30 in the first embodiment, and is a variation in the suction pressure according to the type of the material of the packaging material O under a predetermined condition. "The maximum mass of the contents that the packaging material does not break" is that when the thickness of the packaging material O is the minimum (generally 30 μm), the packaging material O is affected by the gravity applied to the contents C to be packaged. It represents the limit value of the mass of the content C that does not tear or stretch unbearably as a commercial product. "The maximum mass of the contents that the packaging material does not break" is calculated experimentally.

Specifically, as shown in FIG. 12, the suction pressure variation deriving unit 224 sets the variation of the suction pressure acquired from the correlation information DB 230 according to the type of the material of the packaging material O as the maximum suction pressure variation p_max. The maximum suction pressure variation p_max is corrected by the correction information to derive the final suction pressure variation p. As the correction based on the correction information, the factors that affect the wrinkle generation state of the packaging material O are (a) the tension of the packaging material O, (b) the thickness of the packaging material O, and (c) the adsorption of the suction pad P. The presence or absence of the function to improve the pressure (hereinafter, also referred to as "wrinkle option") is applied. (A) The tighter the packaging material O, the less likely it is to wrinkle, (b) the thicker the packaging material, the less likely it is to wrinkle. Hard to occur. The fact that wrinkles are less likely to occur means that the suction pressure is less likely to vary. The maximum suction pressure variation p_max is corrected accordingly. Specifically, when wrinkles are unlikely to occur, the maximum suction pressure variation p_max is corrected to be smaller than when wrinkles are likely to occur.

Hereinafter, the wrinkle occurrence status according to each of the correction information and the correction example according to the factors (a) to (c) above will be described.

First, the mass of the content C is one of the correction information that affects the tension of the packaging material O in (a). As shown in FIG. 13, the smaller the mass of the content C, the weaker the tension of the packaging material O, and the packaging material O is likely to be sucked into the suction holes of the suction pad P at the time of suction, and wrinkles are likely to occur. On the other hand, the larger the mass of the content C, the stronger the tension of the packaging material O, and the more difficult it is for the packaging material O to be sucked into the suction holes of the suction pad P at the time of suction, so that wrinkles are less likely to occur. In FIG. 13, the number and height of wrinkles (wavy lines in the suction pad P) indicate the state of wrinkles, and the smaller the number of wrinkles and the lower the height of the wrinkles, the more. Indicates that wrinkles are unlikely to occur. The same applies to the following figure.

Another correction information that affects the tension of the packaging material O in (a) is the filling rate of the content C with respect to the packaging material O. As shown in FIG. 14, the lower the filling rate of the content C, the weaker the tension of the packaging material O, and the more likely it is that wrinkles are generated. On the other hand, the higher the filling rate of the content C, the stronger the tension of the packaging material O, and the less likely it is that wrinkles will occur.

As another correction information that affects the tension of the packaging material O in (a) above, there is the number of suction pads P used for sucking and holding one work W. FIG. 15 schematically shows an example of the arrangement of the suction pads P with respect to one work W in each case where the number of suction pads P is 1 to 4. As the number of suction pads P increases, the packaging material O is pulled between the suction pads P, the area of the region where the tension is weak (the shaded portion in the packaging material O in FIG. 15) becomes smaller, and the region where the tension is strong becomes smaller. The area (white portion in the packaging material O in FIG. 15) becomes large, and wrinkles are less likely to occur.

Based on the above points, the suction pressure variation deriving unit 224 corrects so that the larger the mass of the content C, the smaller the suction pressure variation p. Further, the suction pressure variation deriving unit 224 corrects so that the higher the filling rate of the content C with respect to the packaging material O, the smaller the suction pressure variation p. Further, the suction pressure variation deriving unit 224 corrects so that the larger the number of suction pads P used for sucking and holding one work W, the smaller the suction pressure variation p. Hereinafter, an example of deriving the variation p of the suction pressure will be described more specifically.

For example, as shown in the following equation (1), the suction pressure variation derivation unit 224 uses the function f_tension to set the input suction pressure variation p _IN based on (a) the tension of the packaging material O. The difference in suction pressure is corrected to _{p OUT.}

p _OUT = f_tension (p _IN , m, fill, pad_num)
= Max (f_tension_m (p _IN , m, th),
f_tension_fill (p _IN , fill),
f_tension_pad_num (p _IN , pad_num)
... (1)

m is the mass of the content C of the work W, fill is the filling rate, pad_num is the number of suction pads P, and th is the thickness of the packaging material O. In the above equation (1), the tension of the packaging material O is maximized from the tension of the packaging material O due to any of the mass of the content C, the filling rate, or the number of suction pads P, that is, the packaging material O. It represents the calculation of the variation in suction pressure when the occurrence of wrinkles is assumed to be the minimum.

In Eq. (1), f_tension_m is a function for obtaining the adsorption pressure variation from the tension due to the mass of the content C. For example, f_tension_m can be expressed by the following equation (2).

m_max is a constant representing the maximum mass of the content C in which the packaging material is not broken, and is information acquired from the correlation information DB 230. Further, th_min is a constant representing the minimum value of the thickness of the packaging material O, and is, for example, 30 μm. In equation (2), for example, as shown in FIG. 16, as the mass m of the content C increases, the tension of the packaging material O becomes stronger, so that wrinkles are less likely to occur and the variation in suction pressure becomes smaller. Is an expression that expresses. In addition, in the term of th / th_min of the equation (2), in the present embodiment, the value (m_max) of the "maximum mass of the contents without breaking the packaging material" of the correlation information DB 230 is the thickness of the packaging material O. Is the measured value when is the minimum (th_min), so this section reflects the actual thickness of the packaging material O.

Further, in the equation (1), f_tension_fill is a function for obtaining the adsorption pressure variation from the tension depending on the filling rate of the content C. For example, f_tension_fil can be expressed by the following equation (3).

f_tension_fill (p _IN , fill)
= -P _IN * fill + p _IN ... (3)

In the formula (3), for example, as shown in FIG. 17, the higher the filling rate of the content C, the stronger the tension of the packaging material O, so that wrinkles are less likely to occur and the variation in suction pressure becomes smaller. Is an expression that expresses.

Further, in the equation (1), f_tension_pad_num is a function for obtaining the suction pressure variation from the tension depending on the number of suction pads P. f_tension_pad_num can be expressed by, for example, the following equation (4).

f_tension_pad_num (p _IN , pad_num)
= P _IN / pad_num ・ ・ ・ (4)

In the formula (4), for example, as shown in FIG. 18, as the number of suction pads P increases, the tension of the packaging material O becomes stronger, so that wrinkles are less likely to occur and the variation in suction pressure becomes smaller. It is the expressed formula.

Next, the case where the thickness of the packaging material O (b) is used as the correction information will be described. As shown in FIG. 19, the thinner the packaging material O, the more easily the packaging material O is deformed and wrinkled during adsorption. On the other hand, the thicker the packaging material O, the less likely it is that the packaging material O will be deformed and wrinkled during adsorption. Therefore, the suction pressure variation deriving unit 224 corrects so that the thicker the packaging material O, the smaller the suction pressure variation p.

For example, as shown in the following equation (5), the suction pressure variation derivation unit 224 uses the function f_thickness to set the input suction pressure variation p _IN based on (b) the thickness of the packaging material O. The difference in suction pressure is corrected to _{p OUT.}

p _OUT = f_thickness (p _IN , th)
= P _IN / (th / th_min) ・ ・ ・ (5)

Equation (5) is an equation showing, for example, as shown in FIG. 20, that the thicker the packaging material O, the more difficult it is for the packaging material O to wrinkle, and the smaller the variation in suction pressure.

Next, a case where the presence or absence of the wrinkle option of the suction pad P in (c) above is used as correction information will be described. As an option for wrinkles, for example, as shown in the upper right figure of FIG. 21, a guard for preventing the packaging material O from being drawn into the suction hole of the suction pad P, and as shown in the lower right figure of FIG. 21 , The skirt portion of the suction pad P is softened, and the outer shape of the skirt is made to follow the wrinkles of the packaging material O to prevent the generation of a gap between the suction pad P and the packaging material O. As shown in FIG. 21, when there is a wrinkle option, wrinkles of the packaging material O or a gap between the suction pad P and the packaging material O are less likely to occur as compared with the case where there is no wrinkle option. It is possible to suppress variations in suction pressure. Therefore, the suction pressure variation deriving unit 224 corrects the suction pressure variation so as to be small when the suction pad P has the wrinkle option.

For example, as shown in the following equation (6), the suction pressure variation derivation unit 224 uses the function f_pad_option to set the input suction pressure variation p _IN to (c) the presence or absence of the wrinkle option of the suction pad P. Based on this, the difference in suction pressure is corrected to _{p OUT.}

Equation (6) shows that the more the suction pad P has the option for wrinkling, the less likely it is that the packaging material O will wrinkle, and the smaller the variation in suction pressure will be. In the equation (6), k_i is a constant assigned for each wrinkle option op_i (i = 1, 2, ...) To correct the suction pressure variation p so as to be small. When the wrinkle option op_i = True (when the suction pad P has the wrinkle option op_i), when 0 <k_i <1, op_i = False (the suction pad P does not have the wrinkle option op_i). Case), k_i = 1. Also, num_of_options is a constant representing the total number of wrinkle options.

The corrections based on the factors (a) to (c) above applied to the maximum suction pressure variation p_max may be applied in any order. Hereinafter, as shown in FIG. 22, a case where (a), (b), and (c) are applied in this order will be described. In this case, the suction pressure variation p finally derived can be expressed by the following equation (7).

p = f_pad_option (p2, op_i)
p2 = f_thickness (p1, th)
p1 = f_tension (p_max, m, fill, pad_num, th)
... (7)

The output unit 228 outputs the safety factor as information for changing the setting of the operation command value of the robot according to the degree of the drop variation of the work W predicted by the drop variation prediction unit 26. In the present embodiment, for example, a value of 0 to 1 that is multiplied by a set value such as the maximum speed and the maximum acceleration of the robot arm R and an operation command value such as an suction pressure value set in the robot is set as a safety factor. As shown in FIG. 23, for example, the output unit 228 sets the safety factor closer to 0 as the degree of drop variation of the work W predicted by the drop variation prediction unit 26 is larger, and 1 as the degree of drop variation is smaller. The value should be close to. The output safety factor is multiplied by the operation command value set in the robot. Therefore, the robot operates based on the operation command value multiplied by the safety factor output in consideration of the drop variation.

Next, the operation of the drop variation prediction device 210 according to the second embodiment will be described.

FIG. 24 is a flowchart showing the flow of operations executed by the CPU 11 of the drop variation prediction device 210. The drop variation prediction process is performed by the CPU 11 reading the drop variation prediction program from the ROM 12 or the storage 14, deploying it in the RAM 13 and executing it.

When the reception unit 222 displays the reception screen 240 as shown in FIG. 10 on the monitor 16 and adsorbs the work W including the packaging material O and the contents C packaged with the packaging material O. Accept the condition (step S22). The conditions include information indicating the type of the material of the packaging material O, information on the thickness of the packaging material O, and contents as correction information for correcting the variation in the suction pressure according to the type of the material of the packaging material O. The information of C and the information of the suction pad P are included.

The CPU 11 refers to the correlation information DB 230 as the suction pressure variation derivation unit 224, and acquires the maximum suction pressure variation p_max corresponding to the information on the material type of the packaging material O received by the reception unit 222 (step S24). ..

The CPU 11 serves as the suction pressure variation deriving unit 224, for example, according to the mass m of the content C received by the reception unit 222, the filling rate fill, and the number of suction pads P pad_num, for example, equations (1) to (4). The maximum suction pressure variation p_max is corrected to the suction pressure variation p1 according to the equation and the equation (7) (step S26).

As the suction pressure variation deriving unit 224, the CPU 11 sucks the suction pressure variation p1 according to the thickness th of the packaging material O received by the reception unit 222, for example, according to the equations (5) and (7). The variation is corrected to p2 (step S28).

The CPU 11 acts as the suction pressure variation deriving unit 224 according to the information on whether or not the suction pad P received by the reception unit 222 has the wrinkle option op_i, for example, according to the equations (6) and (7). , The suction pressure variation p2 is corrected to the suction pressure variation p (step S30). As a result, the final suction pressure variation p is derived.

As the drop variation prediction unit 26, the CPU 11 sets the degree of drop variation to “large” according to the magnitude of the suction pressure variation p derived by the suction pressure variation derivation unit 224, for example, as shown in FIG. , "Medium", "Small", and so on (step S32).

As the output unit 228, the CPU 11 outputs a safety factor according to the degree of drop variation of the work W predicted by the drop variation prediction unit 26, for example, as shown in FIG. 23. The output safety factor is multiplied by the operation command value set in the robot. Therefore, the robot operates based on the operation command value multiplied by the safety factor output in consideration of the drop variation. Then, the CPU 11 ends the drop variation prediction process.

As described above, according to the drop variation prediction device 210 of the second embodiment, in addition to the type of the material of the packaging material O, the thickness of the packaging material O, which is a factor affecting the occurrence of wrinkles of the packaging material O, is determined. The information to be shown, the information of the content C, and the information of the suction pad P are accepted as correction information. Then, the drop variation prediction device 210 corrects the maximum suction pressure variation according to the type of the material of the packaging material O based on the correction information, and derives the final suction pressure variation. In this way, since the adsorption pressure variation is derived by reflecting the wrinkle generation state of the packaging material O in more detail, it is possible to predict the drop variation of the work W more accurately.

Further, by multiplying the operation command value set in the robot by the safety factor according to the predicted degree of variation in the fall, the operation command value can be changed according to the variation in the fall of the work W.

The drop variation prediction device is not limited to each of the above embodiments, and various modifications can be made.

For example, in the first embodiment, when information indicating the predicted degree of variation in the work W drop is presented, in the second embodiment, the operation command value is provided according to the predicted degree of variation in the work W drop. The case of changing is described, but the present invention is not limited to this. For example, information indicating the number of trials regarding the operation of the robot may be presented according to the predicted degree of variation in the fall of the work W. Specifically, as shown in FIG. 25, when the degree of variation in the fall is large, a message instructing to increase the number of test operations for adjusting the operation command value before the actual operation of the robot. May be displayed on the monitor 16.

Further, in each of the above embodiments, the case where the fall variation is predicted in three stages of "large", "medium", and "small" has been described, but the present invention is not limited to this. For example, it may be predicted in two stages, or it may be predicted in four or more stages. Further, the variation of the suction pressure derived by the suction pressure variation derivation unit may be output as it is as the prediction result of the drop variation. This is because there is a correlation between the variation in the suction pressure and the variation in the drop of the work W, and the variation in the suction pressure itself is an index showing the variation in the drop of the work W.

Further, in the second embodiment, the case where all of the information indicating the thickness of the packaging material O, the information of the content C, and the information of the suction pad P are used as the correction information has been described, but the present invention is not limited to this. At least one of these pieces of information may be used as correction information.

Further, as shown in FIG. 26, the drop variation prediction device according to each of the above embodiments can be applied to the robot system 60. The robot system 60 shown in FIG. 26 includes a drop variation prediction device 10 (210), a presentation unit 61, a control unit 62, and a robot unit 63. The robot unit 63 includes a robot arm R to which a suction pad P is attached to the tip thereof, and a drive mechanism for operating the robot arm R. The drive mechanism operates the robot arm R based on the control signal from the control unit 62.

The presentation unit 61 can be configured by a display device such as a display, a speaker that outputs sound, or the like. The presentation unit 61 outputs information indicating the variation in the fall of the work W and information indicating the number of trials regarding the operation of the robot arm R according to the variation in the fall of the work W, which is output from the drop variation prediction device 10 (210). , And a message based on information for changing the setting of the operation command value for operating the robot arm according to the variation in the fall of the work is presented.

The control unit 62 is based on the information for changing the setting of the operation command value when the information for changing the setting of the operation command value is output from the drop variation prediction device 10 (210). The operation command value preset in 62 is changed, and a control signal is sent to the drive mechanism so that the robot arm R operates based on the changed operation command value.

Further, various processors other than the CPU may execute the drop variation prediction process executed by the CPU reading the software (program) in each of the above embodiments. In this case, the processor includes a PLD (Programmable Logic Device) whose circuit configuration can be changed after manufacturing an FPGA (Field-Programmable Gate Array), an ASIC (Application Specific Integrated Circuit), and the like. An example is a dedicated electric circuit or the like, which is a processor having a circuit configuration designed exclusively for the purpose. Further, the drop variation prediction process may be executed by one of these various processors, or a combination of two or more processors of the same type or different types (for example, a plurality of FPGAs, and a CPU and an FPGA). It may be executed by combination etc.). Further, the hardware structure of these various processors is, more specifically, an electric circuit in which circuit elements such as semiconductor elements are combined.

Further, in each of the above embodiments, the mode in which the drop variation prediction program is stored (installed) in the storage 14 or ROM 12 in advance has been described, but the present invention is not limited to this. The program may be provided in a form recorded on a recording medium such as a CD-ROM (Compact Disk Read Only Memory), a DVD-ROM (Digital Versaille Disk Online Memory), and a USB (Universal Serial Bus) memory. Further, the program may be downloaded from an external device via a network.

10, 210 Drop variation prediction device 22, 222 Reception section 24, 224 Suction pressure variation lead-out section 26 Drop variation prediction section 28, 228 Output section 30, 230 Correlation information database 60 Robot system 61 Presentation section 62 Control section W Work O Packaging material C Contents P Suction pad R Robot arm

Claims (12)

A work including a packaging material and contents packaged with the packaging material, and accepts information indicating the type of the material of the packaging material of the work that is attracted, held, and transported by a suction member attached to the robot arm. With the reception department
The type of the material of the packaging material and the variation in the adsorption pressure between the packaging material and the adsorption member when the packaging material is adsorbed by the adsorption member under predetermined conditions for each type of the material of the packaging material. With reference to the correlation information indicating the correlation, a derivation unit for deriving the variation of the adsorption pressure corresponding to the information indicating the type of the material of the packaging material received at the reception unit, and the derivation unit.
Based on the variation in the suction pressure derived by the lead-out unit, the prediction unit predicts the variation in the probability that the work will fall when the robot arm conveys the work.
Fall variation predictor including. The reception unit further receives at least one information of the thickness of the packaging material, the contents, and the suction member as correction information.
The drop according to claim 1, wherein the out-licensing unit corrects the variation in the suction pressure obtained from the correlation information based on the wrinkle generation state of the packaging material corresponding to the correction information received by the reception unit. Variation prediction device. When the correction information received by the receiving unit includes information on the thickness of the packaging material, the derivation unit corrects so that the thicker the packaging material, the smaller the variation in the suction pressure. The drop variation prediction device according to claim 2. When the correction information received by the receiving unit includes information indicating the mass of the content as the information of the content, the derivation unit has a variation in the suction pressure as the mass of the content increases. The drop variation prediction device according to claim 2 or 3, wherein the drop variation prediction device is corrected so that When the correction information received by the receiving unit includes information indicating the filling rate of the content with respect to the packaging material as the information of the content, the derivation unit has the higher the filling rate, the more the adsorption. The drop variation predictor according to any one of claims 2 to 4, wherein the pressure variation is corrected so as to be small. When the correction information received by the receiving unit includes information indicating the number of the suction members used for sucking and holding one work as the information of the suction member, the number of the lead-out parts is the same. The drop variation prediction device according to any one of claims 2 to 5, wherein the larger the number, the smaller the variation in the suction pressure. When the correction information received by the receiving unit includes, as the information of the suction member, information indicating whether or not the suction member has a function of improving the suction pressure, the lead-out unit performs the suction. The drop variation prediction device according to any one of claims 2 to 6, wherein when the member has the function, the variation in the suction pressure is corrected so as to be small. Information indicating the variation in the probability of the work falling, the information indicating the number of trials for the operation of the robot arm according to the variation in the probability of the work falling, and the information indicating the variation in the number of trials for the operation of the robot arm predicted by the prediction unit , and the information in which the work falls. Any one of claims 1 to 7, which includes an output unit that outputs at least one piece of information for changing the setting of the operation command value for operating the robot arm according to the variation in probability. The described drop variation predictor. The drop variation prediction device according to any one of claims 1 to 8, wherein the predetermined condition is that the thickness of the packaging material is a reference thickness and the contents are in a reference state. The computer
Accepts information indicating the type of material of the packaging material of the work including the packaging material and the contents packaged with the packaging material, which is sucked and held by the suction member attached to the robot arm and transported. ,
The type of the material of the packaging material and the variation in the adsorption pressure between the packaging material and the adsorption member when the packaging material is adsorbed by the adsorption member under predetermined conditions for each type of the material of the packaging material. With reference to the correlation information indicating the correlation, the variation in the adsorption pressure corresponding to the received information indicating the type of the packaging material is derived.
A drop variation prediction method for executing a process including predicting a variation in the probability of the work falling when the robot arm conveys the work based on the derived variation in the suction pressure. Computer,
A work including a packaging material and contents packaged with the packaging material, and accepts information indicating the type of the material of the packaging material of the work that is attracted, held, and transported by a suction member attached to the robot arm. Reception department,
The type of the material of the packaging material and the variation in the adsorption pressure between the packaging material and the adsorption member when the packaging material is adsorbed by the adsorption member under predetermined conditions for each type of the material of the packaging material. With reference to the correlation information indicating the correlation, the derivation unit for deriving the variation of the adsorption pressure corresponding to the information indicating the type of the material of the packaging material received at the reception unit, and the derivation unit.
A drop variation prediction program for functioning as a predictor for predicting a variation in the probability of the work falling when the robot arm conveys the work, based on the variation in the suction pressure derived by the derivation unit. When information for changing the setting of the operation command value for operating the robot arm is output from the output unit of the drop variation prediction device according to claim 8, the setting of the operation command value is changed. A robot system including a control unit that controls the robot arm to operate based on an operation command value changed based on the information for the robot arm.

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