Traversal Method and System, Robot, and Readable Storage Medium

    公开(公告)号:US20240085919A1

    公开(公告)日:2024-03-14

    申请号:US17767998

    申请日:2020-08-13

    IPC分类号: G05D1/02 G05B19/4155

    摘要: A traversal method and system, a robot, and a readable storage medium are disclosed, wherein the method may include: acquiring a grid map and establishing the rectangular coordinate system covering the grid map; and if traversal is performed for the first time, driving the robot to arrive at the starting point, and driving the robot to, according to a randomly selected preset rule, traverse the working region in which the starting point is located and work synchronously; when it is confirmed that the current preset rule applied to the first traversal cannot continue to be executed, acquiring the area of each independent working region in the remaining working region, if the area of any independent working region is not less than a preset area threshold, selecting any coordinate point as a working start point in the working region the area of which is not less than the preset area threshold, driving the robot to arrive at the working start point, and, starting from the working start point, randomly selecting the preset rule to perform traversal and work synchronously until the areas of all the independent working regions are less than the preset area threshold (S3). The present disclosure beneficially improves the traversal ability and work efficiency of the robot.

    TARGET-BASED SCHEMA IDENTIFICATION AND SEMANTIC MAPPING FOR ROBOTIC PROCESS AUTOMATION

    公开(公告)号:US20230419161A1

    公开(公告)日:2023-12-28

    申请号:US17747448

    申请日:2022-05-18

    申请人: UiPath, Inc.

    发明人: Daniel DINES

    IPC分类号: G06N20/00 G05B19/4155

    摘要: Target-based schema identification and semantic mapping for robotic process automation (RPA) are disclosed. When looking at a source, such as a document, a web form, a user interface of a software application, a data file, etc., it is often difficult for software to determine which fields are labels and which are values associated with those labels. Since values have not yet been entered for various labels (e.g., first name, company, customer number, etc.), these labels are easier to detect than when the target also includes various values associated with the labels. A selection of an empty target may be received and target-based schema identification may be performed on the empty target, determining labels and a type of the target. Semantic matching may then be performed between a source and the target. These features may be performed at design time or runtime.