8 results about "Object specification" patented technology

Technology is provided for managing access regimes. A method includes generating a first set of tasks to retrieve target properties from a data element as a plurality of fragmented objects, and assigning the tasks to a queue. Nodes perform tasks in the queue. The method includes generating a second set of tasks to process the plurality of fragmented objects into a normalized data structure, and assigning them to the queue. At least some nodes are configured to normalize a respective fragmented object of the second set of tasks into the normalized data structure, and update the queue. In some examples, the normalization is based on a data model that uses a machine learning engine. The method includes generating a third task to generate a final normalized data structure for the data element, and generating the final normalized data structure by aggregating the normalized fragmented objects processed by the nodes.

A method for estimating the location and position of an object may include capturing an image of the object by a camera mounted on a robot, recognizing the marker from the image of the object by a controller, determining a principal surface by the controller using the recognized marker, determining a location of the marker on the principal surface by the controller, calculating a final mean location and a final normal vector of the principal surface by the controller using the determined location of the marker and the object specifications, and determining, by the controller, a final central position and a final normal vector of the principal surface using the determined position of the marker and the object specifications.and calculating a central position and the location of the object by the controller using the final central position and the final normal vector of the main surface and the specifications of the object. A system for estimating the location and position of an object can be set up to execute such a procedure.

A system and method are provided for managing access regimes. The illustrative method includes generating a first set of tasks to retrieve target properties from a data element as a plurality of fragmented objects, and assigning the tasks to a queue. Nodes perform tasks in the queue. The method includes generating a second set of tasks to process the plurality of fragmented objects into a normalized data structure, and assigning them to the queue. At least some nodes are configured to normalize a respective fragmented object of the second set of tasks into the normalized data structure, and update the queue. The method includes generating a third task to generate a final normalized data structure for the data element, and generating the final normalized data structure by aggregating the normalized fragmented objects processed by the nodes.

This disclosure provides a method, apparatus, device, and medium for standardized drawing of objects at accident scenes. The method includes: determining a target top view based on high-density point cloud data of the target accident scene; quickly and automatically acquiring the top view of the accident scene to provide a basis for subsequent drawing of target objects; identifying the shape and size of target objects in the target top view; determining an initial object symbol corresponding to the target object based on the shape of the target object; achieving unified management of object drawing by symbolizing the target object; adjusting the size of the initial object symbol according to the size of the target object to obtain the target symbol of the target object in the target top view; and adjusting the initial object symbol based on the size to match the size of the target object, thereby achieving accurate depiction of the target object based on objective data, ensuring the accuracy of the shape and size of the drawing elements, and realizing the standardization of drawing.

Devices, systems, and methods for machine learning (ML) automatic target recognition (ATR) decision explanation are provided. A method can include receiving an object specification matrix from an object model database that indicates, for each of a plurality of physical portions of an object, whether each of a plurality of features are present or absent in a physical portion of the physical portions of the object and a proportional physical displacement between the features in the object, receiving feature data indicating for an image of a portion of the object, a likelihood whether each of features are present in the image, determining based on the object specification matrix and the feature data, a probability and corresponding uncertainty that the image corresponds to the object, and providing the probability and corresponding uncertainty of the object to help classify the object.

The application provides a multi-specification target object grasping method based on a large model, which comprises three steps of constructing a grasping gesture dataset for multi-specification objects, performing object specification semantic guidance based on a large model (that is, fusing a prompt word engineering technology and a language large model to obtain text information of object shape, components, size, material and grasping gesture description for object specification characterization learning), and constructing a grasping generation model with object specification perception. The application relies on advanced large model technology and the unique generation capability of a diffusion model, and is committed to proposing a grasping mechanism guided by object specification information, and constructing a generation model with generalization and object specification perception, and focuses on solving the key work of grasping gesture generation of different specification objects.

The present invention aims to reduce the workload required to measure a workpiece. A workpiece image analysis device is provided with: a display section that displays an image of a workpiece that is a processing target of an industrial machine; an analysis object specification section that receives a specification of an analysis object for the image of the workpiece; a feature extraction section that extracts a structural feature of the analysis object specified by the analysis object specification section in a coordinate system of a real world; an analysis item setting section that receives a specification of an analysis item for the image of the workpiece; and a physical quantity calculation section that extracts a feature required for calculation of a physical quantity involved in the analysis item specified by the analysis item setting section from the structural feature of the analysis object, and calculates the physical quantity using the extracted feature.

This invention provides a method and system for cloud-controlled robots, relating to intelligent control technology. The robot terminal acquires environmental data and object state information based on a sensor module, and uploads this data to a cloud server via a communication module. The cloud server determines the object's specifications based on the object state information, calculates the gripping points based on these specifications, determines constraint segments based on the environmental data, calculates trajectory offset values ​​based on the object specifications and segment parameters, and generates control commands based on the gripping points and trajectory offset values, sending these commands to the robot terminal. The cloud server acquires real-time status data of the robot terminal while executing the control commands, and generates action adjustment information based on this real-time status data to update the robot terminal's commands. This enables more efficient and flexible object handling control of the robot.