Patent Application
20240033910
The present invention relates to a training data generation device, a machine learning device, and a robot joint angle estimation device.
As a method for setting a tool tip point of a robot, there is known a method of causing the robot to operate, instructing the robot to cause the tool tip point to touch a jig or the like in a plurality of postures, and calculating the tool tip point from angles of the joint axes in the postures. See, for example, Patent Document 1.
In order to acquire angles of the joint axes of a robot, it is necessary to implement a log function in a robot program or acquire data using a dedicated I/F of the robot.
In the case of a robot that is not implemented with a log function or a dedicated I/F, however, it is not possible to acquire angles of the joint axes of the robot.
Therefore, it is desired to, even for a robot that is not implemented with a log function or a dedicated I/F, easily acquire angles of the joint axes of the robot.
According to one aspect, it is possible to, even for a robot that is not implemented with a log function or a dedicated I/F, easily acquire angles of the joint axes of the robot.
One embodiment of the present disclosure will be described below using diagrams.
First, an outline of the present embodiment will be described.
In the present embodiment, on a learning phase, a terminal device such as a smartphone operates as a training data generation device (an annotation automation device) that receives input of a two-dimensional image of a robot captured by a camera included in the terminal device, and the distance and tilt between the camera and the robot, and generates training data for generating a trained model to estimate angles of a plurality of joint axes included in the robot at the time when the two-dimensional image was captured, and a two-dimensional posture indicating positions of the centers of the plurality of joint axes.
The terminal device provides the generated training data for a machine learning device, and the machine learning device executes supervised learning based on the provided training data to generate a trained model. The machine learning device provides the generated trained model for the terminal device.
On an operational phase, the terminal device operates as a robot joint angle estimation device that inputs the two-dimensional image of the robot captured by the camera, and the distance and tilt between the camera and the robot to the trained model to estimate the angles of the plurality of joint axes of the robot at the time when the two-dimensional image was captured, and the two-dimensional posture indicating the positions of the centers of the plurality of joint axes.
Thereby, according to the present embodiment, it is possible to solve the subject of “easily acquiring, even for a robot that is not implemented with a log function or a dedicated I/F, angles of the joint axes of the robot”.
The above is the outline of the present embodiment.
Next, a configuration of the present embodiment will be described in detail using drawings.
The robot 10, the terminal device 20, and the machine learning device 30 may be mutually connected via a network not shown such as a wireless LAN (local area network), Wi-Fi (registered trademark), and a mobile phone network conforming to a standard such as 4G or 5G. In this case, the robot 10, the terminal device 20, and the machine learning device 30 include communication units not shown for mutually performing communication via such connection. Though it has been described that the robot 10 and the terminal device 20 perform data transmission/reception via the communication units not shown, data transmission/reception may be performed via a robot control device (not shown) that controls motions of the robot 10.
The terminal device 20 may include the machine learning device 30 as described later. The terminal device 20 and the machine learning device 30 may be included in the robot control device (not shown).
In the description below, the terminal device 20 that operates as the training data generation device acquires, as the training data, only such pieces of data that are acquired at a timing when all the pieces of data can be synchronized. For example, if a camera included in the terminal device 20 captures frame images at 30 frames/s, and the period with which angles of a plurality of joint axes included in the robot 10 can be acquired is 100 milliseconds, and other data can be immediately acquired, then the terminal device 20 outputs training data as a file with the period of 100 milliseconds.
The robot 10 is, for example, an industrial robot that is well known to one skilled in the art, and has a joint angle response server 101 incorporated therein. The robot 10 drives movable members (not shown) of the robot 10 by driving a servomotor not shown that is arranged for each of the plurality of joint axes not shown, which are included in the robot 10, based on a drive instruction from the robot control device (not shown).
Though the robot 10 will be described below as a 6-axis vertically articulated robot having six joint axes J1 to J6, the robot 10 may be a vertically articulated robot other than the six-axis one and may be a horizontally articulated robot, a parallel link robot, or the like.
The joint angle response server 101 is, for example, a computer or the like, and outputs joint angle data including angles of joint axes J1 to J6 of the robot 10 with the above-described predetermined period that enables synchronization, such as 100 milliseconds, based on a request from the terminal device 20 as the training data generation device described later. The joint angle response server 101 may output the joint angle data directly to the terminal device 20 as the training data generation device as described above, or may output the joint angle data to the terminal device 20 as the training data generation device via the robot control device (not shown).
The joint angle response server 101 may be an device independent of the robot 10.
The terminal device 20 is, for example, a smartphone, a tablet terminal, AR (augmented reality) glasses, MR (mixed reality) glasses, or the like.
As shown in
The camera 22 is, for example, a digital camera or the like, and photographs the robot 10 at a predetermined frame rate (for example, 30 frames/s) based on an operation by a worker, who is a user, and generates a frame image that is a two-dimensional image projected on a plane vertical to the optical axis of the camera 22. The camera 22 outputs the generated frame image to the control unit 21 described later with the above-described predetermined period that enables synchronization, such as 100 milliseconds. The frame image generated by the camera 22 may be a visible light image such as an RGB color image and a gray-scale image.
The communication unit 23 is a communication control device to perform data transmission/reception with a network such as a wireless LAN (local area network), Wi-Fi (registered trademark), and a mobile phone network conforming to a standard such as 4G or 5G. The communication unit 23 may directly communicate with the joint angle response server 101 or may communicate with the joint angle response server 101 via the robot control device (not shown) that controls motions of the robot 10.
The storage unit 24 is, for example, a ROM (read-only memory) or an HDD (hard disk drive) and stores a system program, a training data generation application program, and the like executed by the control unit 21 described later. Further, the storage unit 24 may store input data 241, label data 242, and three-dimensional recognition model data 243.
In the input data 241, input data acquired by the input data acquisition unit 216 described later is stored.
In the label data 242, label data acquired by the label acquisition unit 217 described later is stored.
In the three-dimensional recognition model data 243, feature values such as an edge quantity extracted from each of a plurality of frame images of the robot 10 are stored as a three-dimensional recognition model, the plurality of frame images having been captured by the camera 22 at various distances and with various angles (tilts) in advance by changing the posture and direction of the robot 10. Further, in the three-dimensional recognition model data 243, three-dimensional coordinate values of the origin of the robot coordinate system of the robot 10 (hereinafter also referred to as “the robot origin”) in a world coordinate system at the time when the frame image of each of the three-dimensional recognition models was captured, and information indicating a direction of each of the X, Y, and Z axes of the robot coordinate system in the world coordinate system may be stored in association with the three-dimensional recognition model.
When the terminal device 20 starts the training data generation application program, a world coordinate system is defined, and a position of the origin of the camera coordinate system of the terminal device 20 (the camera 22) is acquired as coordinate values in the world coordinate system. Then, when the terminal device 20 (the camera 22) moves after starting the training data generation application program, the origin in the camera coordinate system moves from the origin in the world coordinate system.
The control unit 21 includes a CPU (central processing unit), a ROM, a RAM, a CMOS (complementary metal-oxide-semiconductor) memory and the like, and these are configured being mutually communicable via a bus and are well-known to one skilled in the art.
The CPU is a processor that performs overall control of the terminal device 20. The CPU reads out the system program and the training data generation application program stored in the ROM via the bus, and controls the whole terminal device 20 according to the system program and the training data generation application program. Thereby, as shown in
The three-dimensional object recognition unit 211 acquires a frame image of the robot 10 captured by the camera 22. The three-dimensional object recognition unit 211 extracts feature values such as an edge quantity from the frame image of the robot 10 captured by the camera 22, for example, using a well-known robot three-dimensional coordinate recognition method (for example, https://linx.jp/product/mvtec/halcon/feature/3d_vision.html). The three-dimensional object recognition unit 211 performs matching between the extracted feature values and the feature values of the three-dimensional recognition models stored in the three-dimensional recognition model data 243. Based on a result of the matching, the three-dimensional object recognition unit 211 acquires, for example, three-dimensional coordinate values of the robot origin in the world coordinate system and information indicating the direction of each of the X, Y, and Z axes of the robot coordinate system in a three-dimensional recognition model with the highest matching degree.
Though the three-dimensional object recognition unit 211 acquires the three-dimensional coordinate values of the robot origin in the world coordinate system, and the information indicating the direction of each of the X, Y, and Z axes of the robot coordinate system, using the robot three-dimensional coordinate recognition method, the present invention is not limited thereto. For example, by attaching a marker, such as a checker board, to the robot 10, the three-dimensional object recognition unit 211 may acquire the three-dimensional coordinate values of the robot origin in the world coordinate system and the information indicating the direction of each of the X, Y, and Z axes of the robot coordinate system, from an image of the marker captured by the camera 22 based on a well-known marker recognition technology.
Or alternatively, by attaching an indoor positioning device, such as a UWB (Ultra Wide Band), to the robot 10, and the three-dimensional object recognition unit 211 may acquire the three-dimensional coordinate values of the robot origin in the world coordinate system and the information indicating directions of each of the X, Y, and Z axes of the robot coordinate system, from the indoor positioning device.
The self-position estimation unit 212 acquires three-dimensional coordinate values of the origin of the camera coordinate system of the camera 22 in the world coordinate system (hereinafter also referred to as “the three-dimensional coordinate values of the camera 22”, using a well-known self-position estimation method. The self-position estimation unit 212 may be adapted to, based on the acquired three-dimensional coordinate values of the camera 22 and the three-dimensional coordinates acquired by the three-dimensional object recognition unit 211, calculate the distance and tilt between the camera 22 and the robot 10.
The joint angle acquisition unit 213 transmits a request to the joint angle response server 101 with the above-described predetermined period that enables synchronization, such as 100 milliseconds, for example, via the communication unit 23 to acquire angles of the joint axes J1 to J6 of the robot 10 at the time when a frame image was captured.
The forward kinematics calculation unit 214 solves forward kinematics from the angles of the joint axes J1 to J6 acquired by the joint angle acquisition unit 213, for example, using a DH (Denavit-Hartenberg) parameter table defined in advance, to calculate three-dimensional coordinate values of positions of the centers of the joint axes J1 to J6 and calculate a three-dimensional posture of the robot 10 in the world coordinate system. The DH parameter table is created in advance, for example, based on the specifications of the robot 10 and is stored into the storage unit 24.
The projection unit 215 arranges the positions of the centers of the joint axes J1 to J6 of the robot 10 calculated by the forward kinematics calculation unit 214 in the three-dimensional space of the world coordinate system, for example, using a well-known method for projection to a two-dimensional plane, and generates two-dimensional coordinates (pixel coordinates) (xi, yi) of the positions of the centers of the joint axes J1 to J6 as a two-dimensional posture of the robot 10, by projecting, from the point of view of the camera 22 decided by the distance and tilt between the camera 22 and the robot 10 calculated by the self-position estimation unit 212, onto a projection plane decided by the distance and tilt between the camera 22 and the robot 10. Here, i is an integer from 1 to 6.
As shown in
In the frame image of
Therefore, the projection unit 215 connects adjacent joint axes of the robot 10 with a line segment, and defines a thickness for each line segment with a link width of the robot 10 set in advance. The projection unit 215 judges whether there is another joint axis on each line segment or not, based on a three-dimensional posture of the robot 10 calculated by the forward kinematics calculation unit 214 and an optical axis direction of the camera 22 decided by the distance and tilt between the camera 22 and the robot 10. In a case like
That is, the projection unit 215 may include, for the two-dimensional coordinates (pixel coordinates) (xi, yi) of the projected positions of the centers of the joint axes J1 to J6, the confidence degrees ci indicating whether the joint axes J1 to J6 are shown or not, respectively, in a frame image, into the two-dimensional posture of the robot 10.
As for training data for performing supervised learning in the machine learning device 30 described later, it is desirable that many pieces of training data are prepared.
As shown in
The input data acquisition unit 216 acquires a frame image of the robot 10 captured by the camera 22, and the distance and tilt between the camera 22 that has captured the frame image and the robot 10 as input data.
Specifically, the input data acquisition unit 216 acquires a frame image as input data, for example, from the camera 22. Further, the input data acquisition unit 216 acquires the distance and tilt between the camera 22 and the robot 10 at the time when the acquired frame image was captured, from the self-position estimation unit 212. The input data acquisition unit 216 acquires the frame image, and the distance and tilt between the camera 22 and the robot 10, which have been acquired, as input data, and stores the acquired input data into the input data 241 of the storage unit 24.
At the time of generating a joint angle estimation model 252 described later, which is configured as a trained model, the input data acquisition unit 216 may convert the two-dimensional coordinates (pixel coordinates) (xi, yi) of the positions of the centers of the joint axes J1 to J6 included in the two-dimensional posture generated by the projection unit 215 to values of XY coordinates that have been normalized to satisfy −1<X<1 by being divided by the width of the frame image and satisfy −1<Y<1 by being divided by the height of the frame image, with the joint axis J1, which is a base link of the robot 10, as the origin, as shown in
The label acquisition unit 217 acquires angles of the joint axes J1 to J6 of the robot 10 at the time when frame images were captured with the above-stated predetermined period that enables synchronization, such as 100 milliseconds, and two-dimensional postures indicating positions of the centers of the joint axes J1 to J6 of the robot 10 in the frame images, as label data (correct answer data).
Specifically, for example, the label acquisition unit 217 acquires the two-dimensional postures indicating the positions of the centers of the joint axes J1 to J6 of the robot 10, and the angles of the joint axes J1 to J6, from the projection unit 215 and the joint angle acquisition unit 213, as the label data (the correct answer data). The label acquisition unit 217 stores the acquired label data into the label data 242 of the storage unit 24.
The machine learning device 30 acquires, for example, the above-described frame images of the robot 10 captured by the camera 22, and distances and tilts between the camera 22 that has captured the frame images and the robot 10, which are stored in the input data 241, from the terminal device 20 as input data.
Further, the machine learning device 30 acquires angles of the joint axes J1 to J6 of the robot 10 at the time when the frame images were captured by the camera 22, and two-dimensional postures indicating positions of the centers of the joint axes J1 to J6, which are stored in the label data 242, from the terminal device 20 as labels (correct answers).
The machine learning device 30 performs supervised learning with training data of pairs configured with the acquired input data and labels to construct a trained model described later.
By doing so, the machine learning device 30 can provide the constructed trained model for the terminal device 20.
The machine learning device 30 will be specifically described.
The machine learning device 30 includes a learning unit 301 and a storage unit 302 as shown in
As described above, the learning unit 301 accepts the pairs of input data and label, from the terminal device 20 as training data. When the terminal device 20 is operating as a robot joint angle estimation device as described later, the learning unit 301 constructs, by performing supervised learning using the accepted training data, a trained model that receives input of a frame image of the robot 10 captured by the camera 22, and the distance and tilt between the camera 22 and the robot 10, and outputs angles of joint axes J1 to J6 of the robot 10 and a two-dimensional posture indicating positions of the centers of the joint axes J1 to J6.
In the present invention, the trained model is constructed to be configured with a two-dimensional skeleton estimation model 251 and the joint angle estimation model 252.
As shown in
The learning unit 301 provides the trained model including the constructed two-dimensional skeleton estimation model 251 and joint angle estimation model 252, for the terminal device 20.
Description will be made below on construction of each of the two-dimensional skeleton estimation model 251 and the joint angle estimation model 252.
For example, based on a deep learning model used for a well-known markerless animal tracking tool (for example, DeepLabCut) or the like, the learning unit 301 performs machine learning based on training data configured with input data of frame images of the robot 10 and labels of two-dimensional postures indicating positions of the centers of the joint axes J1 to J6 at the time when the frame images were captured, the training data having been accepted from the terminal device 20, and generates the two-dimensional skeleton estimation model 251 that receives input of a frame image of the robot 10 captured by the camera 22 of the terminal device 20, and outputs a two-dimensional posture of pixel coordinates indicating positions of the centers of the joint axes Jl to J6 of the robot 10 in the captured frame image.
Specifically, the two-dimensional skeleton estimation model 251 is constructed based on a CNN (convolutional neural network) which is a neural network.
The convolutional neural network has a structure provided with a convolutional layer, a pooling layer, a fully connected layer, and an output layer.
In the convolutional layer, a predetermined parameter filter is applied to an inputted frame image in order to perform feature extraction such as edge extraction. The predetermined parameter of the filter corresponds to the weight of the neural network, and is learned by repeating forward propagation and back propagation.
In the pooling layer, the image outputted from the convolutional layer is blurred in order to allow position misalignment of the robot 10. Thereby, even if the position of the robot 10 fluctuates, the robot 10 can be regarded as the identical object.
By combining these convolutional layer and pooling layer, feature values can be extracted from the frame image.
In the fully connected layer, pieces of image data of feature parts that have been taken out through the convolutional layer and the pooling layer are combined to be one node, and a feature map of values converted by an activation function, that is, a feature map of confidence degrees is outputted.
As shown in
In the output layer, the row, column, and confidence degree (maximum) of a cell at which the confidence degree is the maximum value, in each of the feature maps of the joint axes J1 to J6, which are the output from the fully connected layer, is outputted. In a case where the frame image is convoluted to become 1/N in the convolutional layer, the row and column of each cell is increased by N times in the output layer, and pixel coordinates indicating the position of the center of each of the joint axes J1 to J6 in the frame image are set (N is an integer equal to or larger than 1).
The learning unit 301 performs machine learning, for example, based on training data configured with input data including distances and tilts between the camera 22 and the robot 10, and two-dimensional postures indicating the above-stated normalized positions of the centers of the joint axes J1 to J6, and label data of angles of the joint axes J1 to J6 of the robot 10 at the time when frame images were captured, to generate the joint angle estimation model 252.
Though the learning unit 301 normalizes the two-dimensional posture of the joint axes J1 to J6 outputted from the two-dimensional skeleton estimation model 251, the two-dimensional skeleton estimation model 251 may be generated such that a normalized two-dimensional posture is outputted from the two-dimensional skeleton estimation model 251.
Further, “inclination Rx of X axis”, “inclination Ry of Y axis”, and “inclination Rz of Z axis” are a rotation angle around the X axis, a rotation angle around the Y axis, and a rotation angle around the Z axis, between the camera 22 and the robot 10 in the world coordinate system that are calculated based on three-dimensional coordinate values of the camera 22 in the world coordinate system and three-dimensional coordinate values of the robot origin of the robot 10 in the world coordinate system.
The learning unit 301 may be adapted to, if acquiring new training data after constructing a trained model configured with the two-dimensional skeleton estimation model 251 and the joint angle estimation model 252, update a trained model configured with the two-dimensional skeleton estimation model 251 and the joint angle estimation model 252, which has been once constructed, by further performing supervised learning for the trained model configured with the two-dimensional skeleton estimation model 251 and the joint angle estimation model 252.
By doing so, training data can be automatically obtained from regular photographing of the robot 10, and, therefore, the accuracy of estimating the two-dimensional posture and angles of the joint axes J1 to J6 of the robot 10 can be increased on the daily basis.
The supervised learning described above may be performed as online learning, batch learning, or mini-batch learning.
The online learning is a learning method in which, each time a frame image of the robot 10 is captured, and training data is created, supervised learning is immediately performed. The batch learning is a learning method in which, while capturing of a frame image of the robot 10 and creation of training data are repeated, a plurality of pieces of training data corresponding to the repetition are collected, and supervised learning is performed using all the collected pieces of training data. The mini-batch learning is an intermediate learning method between the online learning and the batch learning, in which supervised learning is performed each time some pieces of training data have been collected.
The storage unit 302 is a RAM (random access memory) or the like, and stores input data and label data acquired from the terminal device 20, the two-dimensional skeleton estimation model 251 and the joint angle estimation model 252 constructed by the learning unit 301, and the like.
Description has been made above on machine learning for generating the two-dimensional skeleton estimation model 251 and the joint angle estimation model 252 provided in the terminal device 20 when the terminal device 20 operates as the robot joint angle estimation device.
Next, the terminal device 20 that operates as the robot joint angle estimation device on the operational phase will be described.
As shown in
The camera 22 and the communication unit 23 are similar to the camera 22 and the communication unit 23 on the learning phase.
The storage unit 24a is, for example, a ROM (read-only memory), an HDD (hard disk drive), or the like and stores a system program, a robot joint angle estimation application program, and the like executed by the control unit 21a described later. Further, the storage unit 24a may store the two-dimensional skeleton estimation model 251 and the joint angle estimation model 252 as a trained model, which have been provided from the machine learning device 30 on the learning phase, and the three-dimensional recognition model data 243.
<Control Unit 21a>
The control unit 21a includes a CPU (central processing unit), a ROM, a RAM, a CMOS (complementary metal-oxide-semiconductor) memory and the like, and these are configured being mutually communicable via a bus and are well-known to one skilled in the art.
The CPU is a processor that performs overall control of the terminal device 20. The CPU reads out the system program and the robot joint angle estimation application program stored in the ROM via the bus, and controls the whole terminal device 20 as the robot joint angle estimation device according to the system program and the robot joint angle estimation application program. Thereby, as shown in
The three-dimensional object recognition unit 211 and the self-position estimation unit 212 are similar to the three-dimensional object recognition unit 211 and the self-position estimation unit 212 on the learning phase.
The input unit 220 inputs a frame image of the robot 10 captured by the camera 22, and a distance L, the tilt Rx of the X axis, the tilt Ry of the Y axis, and the tilt Rz of the Z axis between the camera 22 and the robot 10 calculated by the self-position estimation unit 212.
The estimation unit 221 inputs the frame image of the robot 10, and the distance L, the tilt Rx of the X axis, the tilt Ry of the Y axis, and the tilt Rz of the Z axis between the camera 22 and the robot 10, which have been inputted by the input unit 220, to the two-dimensional skeleton estimation model 251 and the joint angle estimation model 252 as a trained model. By doing so, the estimation unit 221 can estimate angles of the joint axes J1 to J6 of the robot 10 at the time when the inputted frame image was captured, and a two-dimensional posture indicating positions of the centers of the joint axes J1 to J6, from outputs of the two-dimensional skeleton estimation model 251 and the joint angle estimation model 252.
As described above, the estimation unit 221 normalizes pixel coordinates of positions of the centers of the joint axes J1 to J6 outputted from the two-dimensional skeleton estimation model 251 and inputs the pixel coordinates to the joint angle estimation model 252. Further, the estimation unit 221 may be adapted to set each confidence degree ci of a two-dimensional posture outputted from the two-dimensional skeleton estimation model 251 to “1” when the confidence degree ci is 0.5 or above and to “0” when the confidence degree ci is below 0.5.
The terminal device 20 may be adapted to display the angles of the joint axes J1 to J6 of the robot 10, and the two-dimensional posture indicating the positions of the centers of the joint axes J1 to J6, which have been estimated, on a display unit (not shown), such as a liquid crystal display, included in the terminal device 20.
Next, an operation related to an estimation process of the terminal device 20 according to the present embodiment will be described.
At Step S1, the camera 22 photographs the robot 10 based on a worker's instruction via an input device, such as a touch panel (not shown), included in the terminal device 20.
At Step S2, the three-dimensional object recognition unit 211 acquires three-dimensional coordinate values of the robot origin in the world coordinate system, and information indicating a direction of each of the X, Y, and Z axes of the robot coordinate system, based on a frame image of the robot 10 captured at Step S1 and the three-dimensional recognition model data 243.
At Step S3, the self-position estimation unit 212 acquires three-dimensional coordinate values of the camera 22 in the world coordinate system, based on the frame image of the robot 10 captured at Step S1.
At Step S4, the self-position estimation unit 212 calculates the distance L, the tilt Rx of the X axis, the tilt Ry of the Y axis, and the tilt Rz of the Z axis between the camera 22 and the robot 10, based on the three-dimensional coordinate values of the camera 22 acquired at Step S3 and the three-dimensional coordinate values of the robot origin of the robot 10 acquired at Step S2.
At Step S5, the input unit 220 inputs the frame image captured at Step S1, and the distance L, the tilt Rx of the X axis, the tilt Ry of the Y axis, and the tilt Rz of the Z axis between the camera 22 and the robot 10 calculated at Step S3.
At Step S6, by inputting the frame image, and the distance L, the tilt Rx of the X axis, the tilt Ry of the Y axis, and the tilt Rz of the Z axis between the camera 22 and the robot 10, which have been inputted at Step S5, to the two-dimensional skeleton estimation model 251 and the joint angle estimation model 252 as a trained model, the estimation unit 221 estimates angles of the joint axes J1 to J6 of the robot 10 at the time when the inputted frame image was captured, and a two-dimensional posture indicating positions of the centers of the joint axes J1 to J6.
According to the above, by inputting a frame image of the robot 10, and the distance and tilt between the camera 22 and the robot 10 to the two-dimensional skeleton estimation model 251 and the joint angle estimation model 252 as a trained model, the terminal device 20 according to the one embodiment can easily acquire, even for a robot 10 that is not implemented with a log function or a dedicated I/F, angles of the joint axes J1 to J6 of the robot 10.
One embodiment has been described above. The terminal device 20 and the machine learning device 30, however, are not limited to the above embodiment, and modifications, improvements and the like within a range that the object can be achieved are included.
Though the machine learning device 30 is exemplified as an device different from the robot control device (not shown) for the robot 10 and the terminal device 20 in the above embodiment, the robot control device (not shown) or the terminal device 20 may be provided with a part or all of the functions of the machine learning device 30.
Further, for example, in the above embodiment, the terminal device 20 operating as the robot joint angle estimation device estimates angles of the joint axes J1 to J6 of the robot 10 and a two-dimensional posture indicating positions of the centers of the joint axes J1 to J6, from a frame image of the robot 10, and the distance and tilt between the camera 22 and the robot 10, which have been inputted, using the two-dimensional skeleton estimation model 251 and the joint angle estimation model 252 as a trained model, which has been provided from the machine learning device 30. However, the present invention is not limited thereto. For example, as shown in
Each of robots 10A(1) to 10A(m) corresponds to the robot 10 of
Each function included in the terminal device 20 and the machine learning device 30 in the one embodiment can be realized by hardware, software, or a combination thereof. Here, being realized by software means being realized by a computer reading and executing a program.
Each component included in the terminal device 20 and the machine learning device 30 can be realized by hardware including an electronic circuit and the like, software, or a combination thereof. In the case of being realized by software, a program configuring the software is installed into a computer. The program may be recorded in a removable medium and distributed to a user or may be distributed by being downloaded to the user's computer via a network. In the case of being configured with hardware, a part or all of functions of each component included in the above devices can be configured with an integrated circuit (IC), for example, an ASIC (application specific integrated circuit), a gate array, an FPGA (field programmable gate array), a CPLD (complex programmable logic device), or the like.
The program can be supplied to the computer by being stored in any of various types of non-transitory computer-readable media. The non-transitory computer-readable media include various types of tangible storage media. Examples of the non-transitory computer-readable media include a magnetic recording medium (for example, a flexible disk, a magnetic tape, or a hard disk drive), a magneto-optical recording medium (for example, a magneto-optical disk), a CD-ROM (read-only memory), a CD-R, a CD-R/W, a semiconductor memory (for example, a mask ROM and a PROM (programmable ROM)), an EPROM (Erasable PROM), a flash ROM, and a RAM). The program may be supplied to the computer by any of various types of transitory computer-readable media. Examples of the transitory computer-readable media include an electrical signal, an optical signal and an electromagnetic wave. The transitory computer-readable media can supply the program to the computer via a wired communication path such as an electrical wire and an optical fibers, or a wireless communication path.
Steps describing the program recorded in a recording medium include not only processes that are performed chronologically in that order but also processes that are not necessarily performed chronologically but are executed in parallel or individually.
In other words, the training data generation device, the machine learning device, and the robot joint angle estimation device of the present disclosure can take many different embodiments having the following configurations.
According to this training data generation device, it is possible to, even for a robot that is not implemented with a log function or a dedicated I/F, generate training data that is optimal to generate a trained model for easily acquiring angles of the joint axes of the robot.
According to the machine learning device 30, it is possible to, even for a robot that is not implemented with a log function or a dedicated I/F, generate a trained model that is optimal to easily acquire angles of the joint axes of the robot.
By doing so, the machine learning device 30 can easily acquire training data.
According to this robot joint angle estimation device, it is possible to, even for a robot that is not implemented with a log function or a dedicated I/F, easily acquire the angles of the joint axes of the robot.
By doing so, the robot joint angle estimation device can, even for a robot that is not implemented with a log function or a dedicated I/F, easily acquire angles of the joint axes of the robot.
By doing so, the robot joint angle estimation device can apply a trained model even when a new robot and a new robot joint angle estimation device are arranged.
By doing so, the robot joint angle estimation device has effects similar to those of (1) to (6).
| Number | Date | Country | Kind |
|---|---|---|---|
| 2020-211712 | Dec 2020 | JP | national |
| Filing Document | Filing Date | Country | Kind |
|---|---|---|---|
| PCT/JP2021/046117 | 12/14/2021 | WO |
