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公开(公告)号：US11761171B2
公开(公告)日：2023-09-19
申请号：US18183889
申请日：2023-03-14
Applicant: Built Robotics Inc.
CPC classification number: E02F9/2029 , E02F9/205 , E02F9/262 , E02F9/265 , G06N20/00
Abstract: When an EMV performs an action comprising moving a tool of the EMV through soil or other material, the EMV can measure a current speed of the tool through the material and a current kinematic pressure exerted on the tool by the material. Using the measured current speed and kinematic pressure, the EMV system can use a machine learned model to determine one or more soil parameters of the material. The EMV can then make decisions based on the soil parameters, such as by selecting a tool speed for the EMV based on the determined soil parameters.
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公开(公告)号：US11634888B2
公开(公告)日：2023-04-25
申请号：US17735905
申请日：2022-05-03
Applicant: Built Robotics Inc.
Abstract: When an EMV performs an action comprising moving a tool of the EMV through soil or other material, the EMV can measure a current speed of the tool through the material and a current kinematic pressure exerted on the tool by the material. Using the measured current speed and kinematic pressure, the EMV system can use a machine learned model to determine one or more soil parameters of the material. The EMV can then make decisions based on the soil parameters, such as by selecting a tool speed for the EMV based on the determined soil parameters.
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公开(公告)号：US11352769B1
公开(公告)日：2022-06-07
申请号：US17359432
申请日：2021-06-25
Applicant: Built Robotics Inc.
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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公开(公告)号：US11680384B2
公开(公告)日：2023-06-20
申请号：US17190828
申请日：2021-03-03
Applicant: Built Robotics Inc.
CPC classification number: E02F9/205 , E02F9/267 , G05D1/0016 , G05D1/0214 , G05D1/0238 , G05D1/0278 , G05D2201/0202
Abstract: An earth moving vehicle (EMV) autonomously performs an earth moving operation within a dig site. If the EMV determines that a state of the EMV or the dig site triggers a triggering condition associated with a pause in the autonomous behavior of the EMV, the EMV determines a risk associated with the state or triggering condition. If the risk is greater than a first threshold, the EMV continues the autonomous performance and notifies a remote operator that the triggering condition was triggered. If the risk is greater than the first threshold risk but less than a second threshold risk, the EMV is configured to operate in a default state before continuing and notifying the remote operator. If the risk is greater than the second threshold risk, the EMV notifies the remote operator of the state pauses the performance until feedback is received from the remote operator.
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公开(公告)号：US20220412051A1
公开(公告)日：2022-12-29
申请号：US17735905
申请日：2022-05-03
Applicant: Built Robotics Inc.
Abstract: When an EMV performs an action comprising moving a tool of the EMV through soil or other material, the EMV can measure a current speed of the tool through the material and a current kinematic pressure exerted on the tool by the material. Using the measured current speed and kinematic pressure, the EMV system can use a machine learned model to determine one or more soil parameters of the material. The EMV can then make decisions based on the soil parameters, such as by selecting a tool speed for the EMV based on the determined soil parameters.
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Patent Agency Ranking
公开(公告)号：US11346086B1
公开(公告)日：2022-05-31
申请号：US17359421
申请日：2021-06-25
Applicant: Built Robotics Inc.
Abstract: An autonomous earth moving system can select an action for an earth moving vehicle (EMV) to autonomously perform using a tool (such as an excavator bucket). The system then generates a set of candidate tool paths, each illustrating a potential path for the tool to trace as the earth moving vehicle performs the action. In some cases, the system uses an online learning model iteratively trained to determine which candidate tool path best satisfies one or more metrics measuring the success of the action. The earth moving vehicle the executes the earth moving action using the selected tool path and measures the results of the action. In some implementations, the autonomous earth moving system updates the machine learning model based on the result of the executed action.
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公开(公告)号：US20210047806A1
公开(公告)日：2021-02-18
申请号：US16991961
申请日：2020-08-12
Applicant: Built Robotics Inc.
Inventor： Noah Austen Ready-Campbell , Gaurav Jitendra Kikani , James Alan Emerick , Andrew Xiao Liang , Lucas Allen Bruder , Ian Joseph Warmouth , Joonhyun Kim
Abstract: An earth shaping vehicle (ESV) includes a chassis and a rear earth shaping tool. The chassis drives the ESV through the site from a first location in the site to a second location in the site. The front earth shaping tool moves earth from the first location to the second location. The rear earth shaping tool grades a ground surface between first location and the second location. Sensors coupled to the rear excavation tool produce one or more signals representative of a position and an orientation of the rear tool relative to the ground surface of the site. A controller produces actuating signals to actuate the rear excavation tool to grade the ground surface based on the one or more signals representative of the position and the orientation of the rear tool.
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公开(公告)号：US12098528B2
公开(公告)日：2024-09-24
申请号：US18315791
申请日：2023-05-11
Applicant: Built Robotics Inc.
CPC classification number: E02F9/265 , E02F9/262 , G05D1/0061 , G06N3/08
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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公开(公告)号：US12066825B2
公开(公告)日：2024-08-20
申请号：US18325418
申请日：2023-05-30
Applicant: Built Robotics Inc.
CPC classification number: G05D1/0088 , G05D1/0212 , G06N20/00
Abstract: An autonomous earth moving system can determine a desired state for a portion of the EMV including at least one control surface. Then the EMV selects a set of control signals for moving the portion of the EMV from the current state to the desired state using a machine learning model trained to generate control signals for moving the portion of the EMV to the desired state based on the current state. After the EMV executes the selected set of control signals, the system measures an updated state of the portion of the EMV. In some cases, this updated state of the EMV is used to iteratively update the machine learning model using an online learning process.
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公开(公告)号：US20230305560A1
公开(公告)日：2023-09-28
申请号：US18325418
申请日：2023-05-30
Applicant: Built Robotics Inc.
CPC classification number: G05D1/0088 , G05D1/0212 , G06N20/00 , G05D2201/0202
Abstract: An autonomous earth moving system can determine a desired state for a portion of the EMV including at least one control surface. Then the EMV selects a set of control signals for moving the portion of the EMV from the current state to the desired state using a machine learning model trained to generate control signals for moving the portion of the EMV to the desired state based on the current state. After the EMV executes the selected set of control signals, the system measures an updated state of the portion of the EMV. In some cases, this updated state of the EMV is used to iteratively update the machine learning model using an online learning process.
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