Hitachi, Ltd.

Hitachi Solutions, Ltd.

Hitachi High-Tech Corporation

Hitachi Transport System, Ltd.

Nobutaka Kimura

Fumiko Beniyama

Ryo Sakai

Toshio Moriya

Taiki Yano

Nobuhiro Chihara

Kishiko Maruyama

Keisuke Fujimoto

Sadaki Nakano

Takahiro Miki

Takashi Watanabe

Ryo Koyama

Takaomi Nishigaito

Yoshihiro Nishida

Kohsei Matsumoto

Sanato Nagata

Kosei Matsumoto

Kei Utsugi

Taiki Fuji

Takao Ueki

Yoshihiro Masuyama

Yasuki Shimazu

An overhead wire recognition device and method are capable of overhead wire recognition based on plural images taken by an imaging device such as a digital camera. The overhead wire recognition device includes an input unit to which multi-viewpoint images taken by a camera from different viewpoints and camera positions/attitudes are inputted, an overhead wire candidate extraction unit which selects, from the multi-viewpoint input images inputted to the input unit, an overhead wire region including an overhead wire and extracts, from the overhead wire region, a line segment as a candidate of the overhead wire, and an overhead wire model estimation unit which projects the candidate line segment in an imaginary 3D space, determines, based on the candidate line segment, the camera positions/attitudes, and the multi-viewpoint images, a plane where the overhead wire is present in the 3D space, and estimates a 3D model of the overhead wire.

Overhead wire recognition device and overhead wire recognition method

A computer system comprising a transportation system and a control system, wherein: the transportation system comprises a plurality of transportation modules having transportation means for transporting articles; the transportation system transmits, for each of the plurality of transportation modules configuring a transportation line, connection information pertaining to connections between transportation modules; and the control system controls the transportation system on the basis of the multiple pieces of connection information.

Computer system, computer, and method for controlling conveyance system

Provided is a robot stop position setting device that sets a stop position of a robot, including a map storage unit for storing map information, a stop position storage unit for storing a robot stop position, a reference position output unit that outputs a reference position for calculating the robot stop position, and a stop position calculation unit that calculates the robot stop position based on the reference position.

Robot stop position setting device and mobile robot system

A model creation apparatus being configured to: hold at least one image of the registration target object in one or more postures and a reference model indicating a shape of a reference object; acquire information indicating a feature of the registration target object in a first posture; and correct, when a shape in the first posture that is indicated by the reference model is determined to be dissimilar based on a predetermined first condition, the reference model based on the information indicating the feature to thereby create the model indicating the shape of the registration target object.

Model creation device and model creation method

A plurality of workpieces include a plurality of first workpieces of a first workpiece group to be loaded onto a first loaded member according to a first loading sequence and a plurality of second workpieces of a second workpiece group to be loaded onto a second loaded member according to a second loading sequence. On a conveying device, workpieces are arranged at ransom with respect to respective arrangement sequences of the first workpiece group and the second workpiece group and the classifications of the first workpiece group and the second workpiece group. On a conveying device, workpieces of the first workpiece group are sequentially arranged based on the first loading sequence and workpieces of the second workpiece group are sequentially arranged based on the second loading sequence. At the same time, the workpieces of the first workpiece group and the second workpiece group are arranged together.

Palletizing system and palletizing method

To determine diverting availability of a trained recognition model of another object to a target object. A system includes one or more processors and one or more storage devices. The one or more storage devices store an image time series including a target object, and a trained object recognition model for one or more objects which are different from the target object. The one or more processors acquire a movement trajectory of the target object from a recognition result of the target object in the image time series by the object recognition model, and determine diverting availability of the object recognition model to the target object based on the movement trajectory of the target object.

System for determining diverting availability of object recognition model

A technique facilitates selecting and designating an arbitrary one of a plurality of aerial lines. The aerial line extraction system, includes: an area-of-interest cropping unit that crops a region where an aerial line is assumed to exist as an area of interest by setting a support of the aerial line as a reference from a three-dimensional point cloud data; an element segmenting unit that segments the area of interest into a plurality of subdivided areas, obtains a histogram by counting three-dimensional point clouds existing in each of the subdivided areas, and obtains a segmentation plane of the area of interest on the basis of the histogram; and an element display unit that segments the area of interest into a plurality of segmented areas by the segmentation plane and displays the three-dimensional point clouds included in each of the segmented areas in a distinguishable manner.

Aerial line extraction system and method

An autonomous system including a plurality of autonomous bodies, each of the autonomous bodies includes a situation grasping unit that grasps situation, an operation determining unit that determines an operation based on the grasped situation, and an operation executing unit that executes the determined operation, the plurality of autonomous bodies included in the autonomous body system includes one or more first autonomous bodies and two or more second autonomous bodies, situation grasped by the situation grasping unit of the first autonomous body includes situation of the second autonomous body, the situation grasped by the situation grasping unit of the second autonomous body includes a result of an operation executed by the operation executing unit of the first autonomous body, and the operation determining unit of the second autonomous body determines an operation based on the result of the operation executed by the operation executing unit of the first autonomous body.

Autonomous body system and control method thereof

Provided is an aerial line extraction system including: an area-of-interest cropping unit that crops a region where a point cloud data of aerial lines is assumed to exist as an area of interest by setting coordinates of utility poles as a reference from a three-dimensional point cloud data of a three-dimensional shape that includes the aerial lines and trees installed in the air via the utility poles; an aerial line candidate extraction unit that extracts a candidate point cloud data of the aerial lines from the three-dimensional point cloud data in the area of interest; and an aerial line model estimation unit that estimates a model of the aerial lines on the basis of the extracted candidate point cloud data of the aerial lines.

Aerial line extraction system and aerial line extraction method

A machine learning system includes a first determination model that determines whether an input image is a second domain image, and a second determination model that determines whether an extracted image obtained by extracting an area where an object is presented from the input image is from the second domain image. Either a pseudo second domain image or the second domain image is selected and input into the first determination model, and either a first extracted image in the pseudo second domain image or a second extracted image in the second domain image is selected and input into an image extracting unit. Learning of the first determination model is performed based on a first determination result, learning of the second determination model is performed based on a second determination result, and learning of a pseudo image generative model is performed based on the first and second determination results.

Machine learning system, domain conversion device, and machine learning method

A robot system has a manipulator that conveys a conveying object to a conveyance destination having a bottom surface and a wall surface connected to the bottom surface. The system includes: a first process in which the manipulator puts a first conveying object on the bottom surface of the conveyance destination; a second process in which the manipulator conveys a second conveying object to a position that does not come into contact with the first conveying object on the bottom surface of the conveyance destination; and a third process in which the manipulator moves the second conveying object in the direction of the first conveying object from the position that does not come into contact with the first conveying object, and moves the first conveying object by further moving the second conveying object after coming into contact with the first conveying object.

Robot system and control method thereof

A technology capable of dealing with a variation in appropriate recognition parameters due to a change in work environment at a work site at a time of recognizing an article in an image. An article recognition system includes: an input section to which a captured image is input; an output section that presents information to a user; a storage section that stores master data defining features of an object to be recognized; a recognition section that detects the object to be recognized in the image; and a recognition evaluation section that evaluates a recognition content of the recognition section and that presents an alert to the output section on the basis of an evaluation result.

Article recognition system and article recognition method

An object recognition device includes: a data holding unit that stores a reference image of an object of a recognition candidate, each feature point in the reference image, and a feature quantity at each feature point; an image acquisition unit acquires a scene image that is an image of a recognition processing target; a definition calculation unit detects definition indicating the degree of sharpness in each region of the scene image; and a feature acquisition unit and a matching calculation unit that detect a feature point in the scene image to perform a process of matching with the feature point. The matching calculation unit executes, by different methods, an extraction method of feature points in a first region of the scene image where the definition is a first range and in a second region of the scene image where the definition is a second range lower than the first range.

Object recognition device and object recognition method

An object information registration apparatus that registers information of a first object that is a reference object of object recognition holds a first object image that is an image of the first object and recognition method information related to the first object, selects one or more partial regions included in the first object image, sets a recognition method corresponding to each of the one or more partial regions, acquires feature information of each of the one or more partial regions from the first object image based on the set recognition method, and stores the one or more partial regions, the set recognition method, and the acquired feature information in the recognition method information in association with each other.

Object information registration apparatus and object information registration method

An information processing apparatus obtains a plurality of combinations of a position of a work target candidate and a transport machine optimum control parameter which is a control parameter of the transport machine that maximizes performance of the work on a work target when the work target candidate is set as the work target, based on a captured image obtained by capturing an area including a plurality of the work target candidates transported by the transport machine, determines the work target from among the work target candidates based on the combinations, controls the transport machine based on the transport machine optimum control parameter of the determined work target, generates a control plan of the robot based on a position of the determined work target and the transport machine optimum control parameter of the work target and controls the robot according to the generated control plan.

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Tokyo, Japan

Nobutaka Kimura