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.

Abstract: A robot generates a cell grid (e.g., occupancy grid) representation of a geographic area. The occupancy grid may include a plurality of evenly sized cells, and each cell may be assigned an occupancy status, which can be used to indicate the location of obstacles present in the geographic area. Footprints for the robot corresponding to a plurality of robot orientations and joint states may be generated and stored in a look-up table. The robot may generate a planned path for the robot to navigate within the geographic area by generating a plurality of candidate paths, each candidate path comprising a plurality of candidate robot poses. For each candidate robot pose, the robot may query the look-up table for a corresponding robot footprint to determine if a collision will occur.

Abstract: A autonomous off-road vehicle (AOV) accesses a pile plan map indicating a plurality of locations in a geographic area at which piles are to be installed. The AOV selects a first location and a second location from the plurality of locations using the pile plan map. The AOV autonomously navigates to the first location. The AOV autonomously loads a first pile onto a driving tool of the AOV. The AOV autonomously drives the first pile into the ground at the first location using the driving tool. The AOV autonomously navigates to the second location. the AOV autonomously loads a second pile onto the driving tool. And the AOV autonomously drives the second pile into the ground at the second location using the driving tool.

Abstract: An autonomous off-road vehicle (AOV) autonomously performs a pile driving operation by driving a pile into the ground at a location identified by a pile plan map. The AOV detects one or more attributes of the pile using one or more sensors during or after the pile driving operation. The AOV determines whether the one or more attributes of the pile exceed respective tolerance thresholds. The AOV performs a quality control action in response to determining that the one or more attributes of the pile exceed the respective tolerance thresholds. The one or more attributes include one or more of a location and an orientation of the pile, and the quality control action is performed in response to determining that the location or the orientation of the pile exceeds the respective tolerance threshold.

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.

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.

Abstract: An autonomous pile driving system comprises an arm of an autonomous offroad vehicle (AOV) configured to hold and drive a pile, a chute configured to stabilize the pile as the pile is being driven. The autonomous pile driving system further comprises a controller configured to identify a location in the geographic area where a pile is to be driven (e.g., installed) and position the chute at the location. The controller aligns the pile secured by the AOV with an opening of the chute and inserts the pile into the chute by actuating an arm (or other suitable component) of the AOV securing the pile.

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.

Abstract: An autonomous off-road vehicle (AOV) includes a vehicle body and a vehicle arm coupled to the vehicle body and including a set of prongs and a clamp extending outward from an end of the vehicle arm. A controller causes the vehicle arm to pick up a pile by: aligning the set of prongs with an end of the pile such that a first prong aligns with an opening on a first side of a web of the pile and a second prong aligns with a space on a second side of the web, inserting the set of prongs into the opening on the first side and the space on the second side, and compressing the clamp into an exterior surface of a flange of the pile such that the set of prongs are reciprocally compressed into an interior surface of the flange on either side of the web.

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.

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.

Abstract: A robot generates a cell grid (e.g., occupancy grid) representation of a geographic area. The occupancy grid may include a plurality of evenly sized cells, and each cell may be assigned an occupancy status, which can be used to indicate the location of obstacles present in the geographic area. Footprints for the robot corresponding to a plurality of robot orientations and joint states may be generated and stored in a look-up table. The robot may generate a planned path for the robot to navigate within the geographic area by generating a plurality of candidate paths, each candidate path comprising a plurality of candidate robot poses. For each candidate robot pose, the robot may query the look-up table for a corresponding robot footprint to determine if a collision will occur.

Abstract: An excavation vehicle capable of autonomously actuating an excavation tool or navigating an excavation vehicle to perform an excavation routine within an excavation site is described herein. Sensors mounted to the excavation vehicle and the excavation tool produce signals representative of a position and orientation of the corresponding joint relative on the excavation vehicle relative to the excavation site, a position and orientation of the excavation vehicle relative to the excavation site, and one or more features of the excavation site based on the position of the excavation vehicle within the excavation site. A set of solenoids are configured to couple to corresponding hydraulic valves of the excavation tool to actuate the valve. A controller produces actuating signals to control the joints of the excavation tool to autonomously perform the excavation routine based on the signals produced by the sensors.

Abstract: This description provides an autonomous or semi-autonomous excavation vehicle that is capable determining a route between a start point and an end point in a site and navigating over the route. The sensors collect any or more of spatial, imaging, measurement, and location data to detect an obstacle between two locations within the site. Based on the collected data and identified obstacles, the excavation vehicle generates unobstructed routes circumventing the obstacles, obstructed routes traveling through the obstacles, and instructions for removing certain modifiable obstacles. The excavation vehicle determines and selects the shortest route of the unobstructed and obstructed route and navigates over the selected path to move within the site.

Abstract: This description provides an autonomous or semi-autonomous excavation vehicle that is capable of navigating through a dig site and carrying an excavation routine using a system of sensors physically mounted to the excavation vehicle. The sensors collect one or more of spatial, imaging, measurement, and location data representing the status of the excavation vehicle and its surrounding environment. Based on the collected data, the excavation vehicle executes instructions to perform an excavation routine by excavating earth from a hole using an excavation tool positioned at a single location within the site. The excavation vehicle is also able to carry out numerous other tasks, such as checking the volume of excavated earth in an excavation tool, navigating the excavation vehicle over a distance while continuously excavating earth from a below surface depth, and preparing a digital terrain model of the site as part of a process for creating the excavation routine.

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.

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.

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.

Abstract: This description provides an autonomous or semi-autonomous excavation vehicle that is capable of determining a route between a start point and an end point in a site and navigating over the route. The sensors collect any or more of spatial, imaging, measurement, and location data to detect an obstacle between two locations within the site. Based on the collected data and identified obstacles, the excavation vehicle generates unobstructed routes circumventing the obstacles, obstructed routes traveling through the obstacles, and instructions for removing certain modifiable obstacles. The excavation vehicle determines and selects the shortest route of the unobstructed and obstructed route and navigates over the selected path to move within the site.

Abstract: This description provides an autonomous or semi-autonomous excavation vehicle that is capable of navigating through a dig site and carrying out an excavation routine using a system of sensors physically mounted to the excavation vehicle. The sensors collects any one or more of spatial, imaging, measurement, and location data representing the status of the excavation vehicle and its surrounding environment. Based on the collected data, the excavation vehicle executes instructions to carry out an excavation routine. The excavation vehicle is also able to carry out numerous other tasks, such as checking the volume of excavated earth in an excavation tool, and helping prepare a digital terrain model of the site as part of a process for creating the excavation routine.