# US 11,440,184 B2 · Position control device and position control method

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- Number: US 11,440,184 B2
- Document ID: US11440184B2
- Status: Granted
- Kind: B2
- Application number: 16/483,381
- Filed: 2017-02-09
- Published: 2020-01-30
- Granted: 2022-09-13
- Earliest claimed: 2017-02-09
- Assignees: Mitsubishi Electric Corporation
- Applicants: Mitsubishi Electric Corporation
- Inventors: Toshisada Mariyama; Mamoru Miura; Wataru Matsumoto
- Web page: https://patentgrep.com/patent/US11440184B2

## CPC

- B25J 9/161: {Hardware, e.g. neural networks, fuzzy logic, interfaces, processor}
- B25J 9/163: {learning, adaptive, model based, rule based expert control}
- B25J 9/1697: {Vision controlled systems}
- B25J 13/08: by means of sensing devices, e.g. viewing or touching devices
- G05B 13/027: {using neural networks only}
- G05D 3/20: using a digital comparing device
- G06N 3/006: based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
- G06N 3/045: Combinations of networks
- G06N 3/0464: Convolutional networks [CNN, ConvNet]
- G06N 3/088: Non-supervised learning, e.g. competitive learning
- G06N 3/09: Supervised learning
- G06N 3/092: Reinforcement learning
- G06N 20/10: using kernel methods, e.g. support vector machines [SVM]
- G06T 7/70: Determining position or orientation of objects or cameras (camera calibration G06T7/80)
- G05B 2219/39271: Ann artificial neural network, ffw-nn, feedforward neural network
- G06N 3/008: based on physical entities controlled by simulated intelligence so as to replicate intelligent life forms, e.g. based on robots replicating pets or humans in their appearance or behaviour
- G06N 3/044: Recurrent networks, e.g. Hopfield networks
- G06T 2207/20081: Training; Learning
- G06T 2207/20084: Artificial neural networks [ANN]
- B25J: MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES ({manipulators specially adapted for use in surgery A61B34/70; manipulators used in cleaning hollow articles B08B9/04}; manipulators associated with rolling mills B21B39/20; manipulators associated with forging machines B21J13/10; {manipulators associated with picking-up and placing mechanisms B23P19/007}; means for holding wheels or parts thereof B60B30/00; {vehicles with ground-engaging propulsion means, e.g. walking members B62D57/02, B62D57/032; devices for picking-up and depositing articles or materials between conveyors B65G47/90, B65G47/91; manipulators with gripping or holding means for transferring packages B65H67/065}; cranes B66C; {manipulators used in the protection or supervision of pipe-line installations F17D5/00; walking equipment adapted for nuclear steam-generators F22B37/006}; manipulators specially adapted for, or associated with, nuclear reactors G21C; {apparatus used for handling wafers during manufacture or treatment of semiconductor H10P72/50})
- G05B: CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS (systems for controlling or regulating non-electric variables G05D; systems for regulating electric or magnetic variables G05F; control devices or systems insofar as characterised by mechanical features only G05G)
- G05D: SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL

## Abstract

An imaging unit, a control parameter generation unit, a control unit, and a drive unit are provided. The imaging unit captures an image including two objects. The control parameter generation unit feeds information of the captured image including the two objects into an input layer of a neural network, and outputs a position control amount for controlling the positional relation of between the two objects as an output layer of the neural network. The control unit controls current or voltage to control the positional relation between the two objects by using the outputted position control amount. The drive unit changes a position of one of the two objects by using the current or the voltage. Here, the control parameter generation unit selects the neural network from a plurality of neural networks. Therefore, even if there are differences between objects or errors in the positional relationship between the two objects, alignment can be performed more accurately.

## Claims

### Claim 1

A position control device to install a first object to a second object by causing a motor to move the first object, the device comprising:
imaging circuitry of a monocular camera to capture an image including two objects that are the first object and the second object; and
processing circuitry programmed to utilize a plurality of neural networks, wherein each of the plurality of neural networks
has an input layer into which information of an image captured by the imaging circuitry is inputted,
has an output layer in which a control amount obtained on a basis of a learning rule is outputted, the control amount being for moving the first object in correspondence with the image inputted into the input layer, and
is trained in association with a range of control amounts to be outputted by the output layer, wherein the plurality of neural networks are trained respectively in association with a plurality of different ranges of control amounts,
wherein the processing circuitry is further programmed to
in response to the imaging circuitry capturing a first image, which includes the first object and the second object, at a first time,
determine a first range from among the different ranges of control amounts having a highest magnitude, and
select a first neural network as the neural network among the plurality of neural networks trained in association with the first range,
input information of the first image into the input layer of the first neural network,
output a first control amount, which is outputted in the output layer of the first neural network in response to the information of the first image being inputted into the input layer of the first neural network, as a control amount for causing the motor to move the first object included in the first image acquired from the imaging circuitry,
in response to the imaging circuitry capturing a second image, which includes the first object and the second object, at a second time, which is later than the first time,
determine, from among the plurality of different ranges of the control amounts, a second range within which a magnitude of the first control amount falls,
select a second neural network as the neural network among the plurality of neural networks trained in association with the second range,
input information of the second image into an input layer of the second neural network, and
output a second control amount, which is outputted in the output layer of the second neural network in response to the information of the second image being inputted into the input layer of the second neural network, as a control amount for causing the motor to move the first object included in the second image acquired from the imaging circuitry.

### Claim 2 (depends on 1)

The position control device according to claim 1, further comprising a controlling circuitry to control a current value or a voltage value for the motor to move the first object using each of the first control amount and the second control amount outputted from the processing circuitry.

### Claim 3 (depends on 1)

The position control device according to claim 1, wherein
at a trial, the processing circuitry
receives information of an image captured by the imaging circuitry,
outputs a first movement correction amount that maximizes a defined reward, that is obtained on a basis of a position of the first object in an image captured by the imaging circuitry at a first trial time, as a first movement control amount for causing the motor to move the first object,
outputs a second movement correction amount that is obtained using a recommendation value on a basis of a position of the first object in an image captured by the imaging circuitry at a second trial time, which is later than the first trial time, as a second movement control amount for causing the motor to move the first object, and
obtains, when the first object is moved according to each of the first and second movement control amounts outputted from the processing circuitry, a recommendation value on a basis of an evaluation value indicating a degree of success based on a positional relation between the first object and the second object.

### Claim 4 (depends on 2)

The position control device according to claim 2, wherein
at a trial, the processing circuitry
receives information of an image captured by the imaging circuitry,
outputs a first movement correction amount that maximizes a defined reward, that is obtained on a basis of a position of the first object in an image captured by the imaging circuitry at a first trial time, as a first movement control amount for causing the motor to move the first object,
outputs a second movement correction amount that is obtained using a recommendation value on a basis of a position of the first object in an image captured by the imaging circuitry at a second trial time, which is later than the first trial time, as a second movement control amount for causing the motor to move the first object, and
obtains, when the first object is moved according to each of the first and second movement control amounts outputted from the processing circuitry, a recommendation value on a basis of an evaluation value indicating a degree of success based on a positional relation between the first object and the second object.

### Claim 5

A position control method for installing a first object to a second object by causing a motor to move the first object, the method comprising:
determining a first neural network from among a plurality of neural networks, each of the plurality of neural networks including an input layer for inputting information of an image including the first object and the second object, each of the plurality of neural networks including an output layer for outputting a control amount from a corresponding range of control amounts, the first neural network being determined as the one of the plurality of neural networks whose corresponding range of control amount has a highest magnitude;
inputting information of a first image, which is captured by imaging circuitry of a monocular camera at a first time and includes the first object and the second object, into an input layer of the first neural network;
outputting a first control amount that is outputted in the output layer of the first neural network, the first control amount being a value
that is outputted as a result of the information of the first image being inputted into the input layer of the first neural network,
that is obtained on a basis of a learning rule, and
that is applied as a control amount for causing the motor to move one of the two objects included in the first image captured by the imaging circuitry;
inputting information of a second image including the first object and the second object captured by the imaging circuitry at a second time, which is later than the first time, into an input layer of a second neural network selected from among the plurality of neural networks on a basis of which of the plurality of neural networks has a corresponding range of control amounts within which a magnitude of the first control amount falls; and
outputting a second control amount that is outputted in the output layer of the second neural network, the second control amount being a value
that is outputted as a result of the information of the second image being inputted into the input layer of the second neural network,
that is obtained on a basis of a learning rule, and
that is applied as a control amount for causing the motor to move the first object included in the second image captured by the imaging circuitry.

### Claim 6

A position control method for installing a first object to a second object by causing a motor to move the first object, the method comprising:
performing a process of training a plurality of neural networks including
calculating a first movement correction amount that maximizes a defined reward on a basis of a position of the first object in information of an image that includes two objects that are the first object and the second object, and that is captured by imaging circuitry of a monocular camera at a first trial, as a first movement control amount for causing the motor to move the first object;
calculating a first recommendation value on a basis of an evaluation value indicating a degree of success based on a positional relation between the first object and the second object when the first object is moved according to the first movement control amount;
calculating, using the first recommendation value, a second movement correction amount on a basis of a position of the first object in an image captured by the imaging circuitry at a second trial, as a second movement control amount for causing the motor to move the first object;
calculating, when the imaging circuitry captures an image at a third trial, a second recommendation value on a basis of an evaluation value indicating a degree of success based on a positional relation between the first object and the second object when the first object is moved according to the second movement control amount;
calculating, using the second recommendation amount, a third movement correction amount from a position of the first object in an image captured by the imaging circuitry at the third trial, as a third movement control amount for causing the motor to move the first object; and
selecting a neural network from among the plurality of trained neural networks, each of the plurality of neural networks including an input layer for inputting information of an image including the first object and the second object, each of the plurality of neural networks including an output layer for outputting a control amount from a corresponding range of control amounts, the selection being made on a basis of the ranges of control amounts respectively corresponding to the plurality of neural networks;
inputting information of a particular image including the first object and the second object, which is captured by the imaging circuitry, into the input layer of the selected neural network; and
outputting a control amount that is outputted in the output layer of the selected neural network, the obtained control amount being a value
that is outputted as a result of the information of the particular image being inputted into the input layer of the selected neural network,
that is obtained on a basis of a learning rule subjected to said training, and
that is applied as a control amount for causing the motor to move the first object included in the particular image captured by the imaging circuitry.