CLOUD SERVER AND METHOD FOR CONVERTING SOFTWARE IMAGE OF ROBOT IN CLOUD SERVER

    公开(公告)号:US20240111548A1

    公开(公告)日:2024-04-04

    申请号:US18553963

    申请日:2021-04-08

    CPC classification number: G06F9/445 B25J9/0084 B25J9/1656 G06F8/65

    Abstract: The present disclosure relates to a cloud server, comprising a communication unit transmitting and receiving data to and from at least one robot and an external device; and a control unit which receives a first signal including operating system information of a first robot, hardware information of the first robot, and software information of the first robot from a first robot among the at least one robot; if no software image corresponding to the first signal is present, extracts the operating system information of the first robot, the hardware information of the first robot, and the software information of the first robot from the first signal; generates a software image based on the extracted operating system information of the first robot, the extracted hardware information of the first robot, and the extracted software information of the first robot; and transmits the generated software image to the first robot.

    HETEROGENEOUS ROBOT SYSTEM COMPRISING EDGE SERVER AND CLOUD SERVER, AND METHOD FOR CONTROLLING SAME

    公开(公告)号:US20240272649A1

    公开(公告)日:2024-08-15

    申请号:US18580791

    申请日:2022-07-06

    Abstract: The present embodiment relates to a cloud-based robot control method for controlling a plurality of robots which are positioned in a plurality of spaces divided arbitrarily, the method comprising the steps of: generating a control base model which can be applied to the plurality of robots in a cloud server; distributing the control base model to edge servers allocated to respective spaces; upgrading the control base model in accordance with the plurality of robots of a space, in the edge server; directly transmitting the upgraded control model from the edge server to another edge server; and controlling the plurality of robots by means of the upgraded control model in the edge server. Therefore, by sharing a deep-learning model among edge servers, supporting heterogeneous robots and heterogeneous services is possible. Further, a base deep-learning model from the cloud server is tuned into a customized deep-learning model to be suitable for respective robots in the edge server, and the deep-learning model is upgraded to an adaptive deep-learning model to be suitable for a service provided by respective robots, and thus an optimized service can be provided.

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