BASKET ASSEMBLY OPERATION FOR AUTONOMOUS PILE DRIVING SYSTEM

    公开(公告)号:US20250122686A1

    公开(公告)日:2025-04-17

    申请号:US19000220

    申请日:2024-12-23

    Abstract: A pile plan map indicating a plurality of locations in a geographic area at which piles are to be installed is accessed. A first set of locations is identified from the plurality of locations and a first set of piles to be driven into the ground at the first set of locations using the pile plan map is identified. An order for driving the first set of piles into the ground is identified and a pile type for each of the first set of piles is identified. Basket assembly instructions are generated for assembling the first set of piles into a basket based on the identified order and the identified pile types. The first set of piles are assembled autonomously or manually into the basket based on the generated basket assembly instructions.

    Pile manipulation for autonomous pile driver vehicle

    公开(公告)号:US12215477B1

    公开(公告)日:2025-02-04

    申请号:US18615258

    申请日:2024-03-25

    Abstract: An autonomous pile driving system identifies using a pile plan map a target location to install a pile. The system autonomously navigates towards the target location. The system autonomously detects using an object sensor system an orientation and location of the pile located within a threshold distance of the system. The system autonomously positions a tool of the autonomous pile driving system based on the detected orientation and location of the pile. The system autonomously picks up the pile using the positioned tool of the autonomous pile driving system. The system autonomously positions the pile based on the target location. The system autonomously drives the pile into ground at the target location.

    Online machine learning for calibration of autonomous earth moving vehicles

    公开(公告)号:US11352769B1

    公开(公告)日:2022-06-07

    申请号:US17359432

    申请日:2021-06-25

    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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