Abstract: A machine learning device that learns an operation of a robot for picking up, by a hand unit, any of a plurality of workpieces placed in a random fashion, including a bulk-loaded state, includes a state variable observation unit that observes a state variable representing a state of the robot, including data output from a three-dimensional measuring device that obtains a three-dimensional map for each workpiece, an operation result obtaining unit that obtains a result of a picking operation of the robot for picking up the workpiece by the hand unit, and a learning unit that learns a manipulated variable including command data for commanding the robot to perform the picking operation of the workpiece, in association with the state variable of the robot and the result of the picking operation, upon receiving output from the state variable observation unit and output from the operation result obtaining unit.

Abstract: A machine learning device that learns an operation of a robot for picking up, by a hand unit, any of a plurality of objects placed in a random fashion, including a bulk-loaded state, includes a state variable observation unit that observes a state variable representing a state of the robot, including data output from a three-dimensional measuring device that obtains a three-dimensional map for each object, an operation result obtaining unit that obtains a result of a picking operation of the robot for picking up the object by the hand unit, and a learning unit that learns a manipulated variable including command data for commanding the robot to perform the picking operation of the object, in association with the state variable of the robot and the result of the picking operation, upon receiving output from the state variable observation unit and output from the operation result obtaining unit.

Abstract: A machine learning device that learns an operation of a robot for picking up, by a hand unit, any of a plurality of objects placed in a random fashion, including a bulk-loaded state, includes a state variable observation unit that observes a state variable representing a state of the robot, including data output from a three-dimensional measuring device that obtains a three-dimensional map for each object, an operation result obtaining unit that obtains a result of a picking operation of the robot for picking up the object by the hand unit, and a learning unit that learns a manipulated variable including command data for commanding the robot to perform the picking operation of the object, in association with the state variable of the robot and the result of the picking operation, upon receiving output from the state variable observation unit and output from the operation result obtaining unit.

Abstract: A machine learning device that learns an operation of a robot for picking up, by a hand unit, any of a plurality of workpieces placed in a random fashion, including a bulk-loaded state, includes a state variable observation unit that observes a state variable representing a state of the robot, including data output from a three-dimensional measuring device that obtains a three-dimensional map for each workpiece, an operation result obtaining unit that obtains a result of a picking operation of the robot for picking up the workpiece by the hand unit, and a learning unit that learns a manipulated variable including command data for commanding the robot to perform the picking operation of the workpiece, in association with the state variable of the robot and the result of the picking operation, upon receiving output from the state variable observation unit and output from the operation result obtaining unit.

Abstract: A machine learning device that learns an operation of a robot for picking up, by a hand unit, any of a plurality of workpieces placed in a random fashion, including a bulk-loaded state, includes a state variable observation unit that observes a state variable representing a state of the robot, including data output from a three-dimensional measuring device that obtains a three-dimensional map for each workpiece, an operation result obtaining unit that obtains a result of a picking operation of the robot for picking up the workpiece by the hand unit, and a learning unit that learns a manipulated variable including command data for commanding the robot to perform the picking operation of the workpiece, in association with the state variable of the robot and the result of the picking operation, upon receiving output from the state variable observation unit and output from the operation result obtaining unit.

Abstract: A machine learning device that learns an operation of a robot for picking up, by a hand unit, any of a plurality of workpieces placed in a random fashion, including a bulk-loaded state, includes a state variable observation unit that observes a state variable representing a state of the robot, including data output from a three-dimensional measuring device that obtains a three-dimensional map for each workpiece, an operation result obtaining unit that obtains a result of a picking operation of the robot for picking up the workpiece by the hand unit, and a learning unit that learns a manipulated variable including command data for commanding the robot to perform the picking operation of the workpiece, in association with the state variable of the robot and the result of the picking operation, upon receiving output from the state variable observation unit and output from the operation result obtaining unit.

Abstract: An article take-out apparatus comprising: a robot having a hand capable of holding an article; a 3D measuring device measuring surface positions of a plurality of articles stored in bulk in a 3D space to acquire a 3D point set composed of a plurality of 3D points; a local maximum point selecting unit selecting from the 3D point set a 3D point, where a coordinate value with respect to a predetermined coordinate axis is maximum; a processing unit determining a hand position and posture including a target position and target posture of the hand enabling an article near the local maximum point to be taken out based on the selected local maximum point; and a robot control unit controlling the robot so as to move the hand to the determined hand position and posture and take out the article from the hand position and posture.

Abstract: An article take-out apparatus comprising: a robot having a hand capable of holding an article; a 3D measuring device measuring surface positions of a plurality of articles stored in bulk in a 3D space to acquire a 3D point set composed of a plurality of 3D points; a local maximum point selecting unit selecting from the 3D point set a 3D point, where a coordinate value with respect to a predetermined coordinate axis is maximum; a processing unit determining a hand position and posture including a target position and target posture of the hand enabling an article near the local maximum point to be taken out based on the selected local maximum point; and a robot control unit controlling the robot so as to move the hand to the determined hand position and posture and take out the article from the hand position and posture.

Abstract: A method and an apparatus for predicting interference, at practical accuracy and calculation time, between a target section of a robot and a peripheral object installed around the robot, when the target section, such as a tool or a sensor attached to a robot hand, is moved along a movement path thereof due to the motion of the robot. A convex hull, defined by areas occupied by the tool at adjacent time points, is calculated. It is judged whether a common area exists between the convex hull and a polyhedron area. When the common area exists, it is judged that the tool interferes with the container box on the movement path, and the procedure is terminated. When the common area does not exist, it is judged whether j<n is true. If j?n is true, it is judged that the interference does not occur.

Abstract: A workpiece removing device including a camera for imaging a workpiece loading area including a plurality of workpieces loaded in bulk; a workpiece detection section for detecting a workpiece, based on a camera image taken with the camera; a workpiece selection section for selecting a workpiece adapted to be removed, based on a detection result by the workpiece detection section; a robot for removing the workpiece selected by the workpiece selection section; a loading state determination section for determining whether a loading state of the workpieces in the workpiece loading area has changed due to a operation of the robot; and an area setting section for setting a workpiece detection area where the workpiece detection section detects a workpiece. If the loading state determination section determines that the loading state of the workpieces has changed, the area setting section sets the workpiece detection area in a peripheral area of a changing position of the loading state, i.e.

Abstract: A method and an apparatus for predicting interference, at practical accuracy and calculation time, between a target section of a robot and a peripheral object installed around the robot, when the target section, such as a tool or a sensor attached to a robot hand, is moved along a movement path thereof due to the motion of the robot. A convex hull, defined by areas occupied by the tool at adjacent time points, is calculated. It is judged whether a common area exists between the convex hull and a polyhedron area. When the common area exists, it is judged that the tool interferes with the container box on the movement path, and the procedure is terminated. When the common area does not exist, it is judged whether j<n is true. If j?n is true, it is judged that the interference does not occur.

Abstract: A workpiece removing device including a camera for imaging a workpiece loading area including a plurality of workpieces loaded in bulk; a workpiece detection section for detecting a workpiece, based on a camera image taken with the camera; a workpiece selection section for selecting a workpiece adapted to be removed, based on a detection result by the workpiece detection section; a robot for removing the workpiece selected by the workpiece selection section; a loading state determination section for determining whether a loading state of the workpieces in the workpiece loading area has changed due to a operation of the robot; and an area setting section for setting a workpiece detection area where the workpiece detection section detects a workpiece. If the loading state determination section determines that the loading state of the workpieces has changed, the area setting section sets the workpiece detection area in a peripheral area of a changing position of the loading state, i.e.

Abstract: An apparatus for picking up objects including a robot for picking up an object, at least one part of the object having a curved shape, having a storing means for storing a gray gradient distribution model of the object, a recognizing means for recognizing a gray image of the object, a gradient extracting means for extracting a gray gradient distribution from the gray image recognized by the recognizing means, an object detecting means for detecting a position or position posture of the object in the gray image in accordance with the gray gradient distribution extracted by the gradient extracting means and the gray gradient distribution model stored by the storing means, a detection information converting means for converting information of the position or position posture detected by the object detecting means into information of position or position posture in a coordinate system regarding the robot; and a robot moving means for moving the robot to the position or position posture converted by the detection

Abstract: An apparatus for picking up objects including a robot for picking up an object, at least one part of the object having a curved shape, having a storing means for storing a gray gradient distribution model of the object, a recognizing means for recognizing a gray image of the object, a gradient extracting means for extracting a gray gradient distribution from the gray image recognized by the recognizing means, an object detecting means for detecting a position or position posture of the object in the gray image in accordance with the gray gradient distribution extracted by the gradient extracting means and the gray gradient distribution model stored by the storing means, a detection information converting means for converting information of the position or position posture detected by the object detecting means into information of position or position posture in a coordinate system regarding the robot; and a robot moving means for moving the robot to the position or position posture converted by the detection

Abstract: A first masking image of an input image is created by assuming that pixels in an area including the area of a target in all sample images are valid pixels while pixels in the remaining area are invalid pixels. The first masking image is used in a first comparison between the input image and each sample image to select an image that provides a best match as a provisionally selected sample image. In a second comparison, a second masking image corresponding to the provisionally selected sample image is used to select a final sample image. The second masking image is created from an individual sample image by assuming that pixels in an area including the area of the target are valid pixels while pixels in the remaining area invalid pixels.

Abstract: (1) Orthogonal transformation is applied to sample images based on the images of a normalized orthogonal system, and component groups are determined. A plurality of comparison target images are selected from an input image, and a partial normalized orthogonal system is created from the normalized orthogonal system by decreasing the dimensions. (2) Based on the partial normalized orthogonal system, orthogonal transformation is applied to the comparison target images, so as to determine the partial component group for each comparison target image. A comparison target image having high consistency with one of the sample images is extracted by comparison and collation between these partial component groups and the partial component groups which include each element of each partial normalized orthogonal system among component groups of each sample image.

Abstract: The entire region of variation in the viewing range of the attitude of a workpiece is divided roughly, and images of the workpiece are captured from each direction. The images are stored together with imaging direction data as a first teaching model. Images captured by varying the imaging direction at a narrow pitch within a predetermined range of attitude variation in the workpiece are then stored together with imaging direction data as a second teaching model. Images of the workpiece are captured and compared with the teaching models. The position and attitude of the workpiece are determined by the imaging direction and imaging position of the selected teaching model.

Abstract: (1) Orthogonal transformation is applied to sample images based on the images of a normalized orthogonal system, and component groups are determined. A plurality of comparison target images are selected from an input image, and a partial normalized orthogonal system is created from the normalized orthogonal system by decreasing the dimensions. (2) Based on the partial normalized orthogonal system, orthogonal transformation is applied to the comparison target images, so as to determine the partial component group for each comparison target image. A comparison target image having high consistency with one of the sample images is extracted by comparison and collation between these partial component groups and the partial component groups which include each element of each partial normalized orthogonal system among component groups of each sample image.

Abstract: The entire region of variation in the viewing range of the attitude of a workpiece is divided roughly, and images of the workpiece are captured from each direction. The images are stored together with imaging direction data as a first teaching model. Images captured by varying the imaging direction at a narrow pitch within a predetermined range of attitude variation in the workpiece are then stored together with imaging direction data as a second teaching model. An image of the workpiece is then captured and compared with the first teaching model, whereupon the first teaching model most closely resembling the captured image is selected. A camera is then moved in accordance with the selected teaching model to a target moving position to obtain an image to be used in a comparison with the second teaching model, whereupon an image of the workpiece is captured. The captured image is compared with the second teaching model, and the second teaching model most closely resembling the captured image is selected.

Abstract: A first masking image of an input image is created by assuming that pixels in an area including the area of a target in all sample images are valid pixels while pixels in the remaining area are invalid pixels. The first masking image is used in a first comparison between the input image and each sample image to select an image that provides a best match as a provisionally selected sample image. In a second comparison, a second masking image corresponding to the provisionally selected sample image is used to select a final sample image. The second masking image is created from an individual sample image by assuming that pixels in an area including the area of the target are valid pixels while pixels in the remaining area invalid pixels.