OBSTACLE MAP FOR AUTONOMOUS PILE DRIVING SYSTEM

    公开(公告)号:US20240210947A1

    公开(公告)日:2024-06-27

    申请号:US18483652

    申请日:2023-10-10

    CPC classification number: G05D1/0274 G05D2201/0202

    Abstract: An autonomous off-road vehicle (AOV) accesses a pile plan map indicating a plurality of locations within a geographic area at which piles are to be installed. The AOV generates an obstacle map indicating locations of obstacles within the geographic area. The AOV autonomously navigates to a first location of the plurality of locations using the pile plan map. In response to driving a pile into the ground at the first location, the AOV modifies the obstacle map to include a representation of the pile at the first location. The AOV autonomously navigates to a second location of the plurality of locations using the pile plan map. In response to driving a pile into ground at the second location, the AOV modifies the obstacle map that includes the representation of the pile at the first location to further include a representation of the pile at the second location.

    ONLINE MACHINE LEARNING FOR CALIBRATION OF AUTONOMOUS EARTH MOVING VEHICLES

    公开(公告)号:US20220412057A1

    公开(公告)日:2022-12-29

    申请号:US17748999

    申请日:2022-05-19

    Abstract: In some implementations, the EMV uses a calibration to inform autonomous control over the EMV. To calibrate an EMV, the system first selects a calibration action comprising a control signal for actuating a control surface of the EMV. Then, using a calibration model comprising a machine learning model trained based on one or more previous calibration actions taken by the EMV, the system predicts a response of the control surface to the control signal of the calibration action. After the EMV executes the control signal to perform the calibration action, the EMV system monitors the actual response of the control signal and uses that to update the calibration model based on a comparison between the predicted and monitored states of the control surface.

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