Abstract: A method for controlling a robotic system includes determining a location and/or a pose of a power system component based on data received from one or more sensors, and determining a mapping of a location of a robotic system within a model of an external environment of the robotic system based on the data. The model of the external environment provides locations of objects external to the robotic system. A sequence of movements of components of the robotic system is determined to perform maintenance on the power system component based on the locations of the objects external to the robotic system and/or the location or pose of the power system component. One or more control signals are communicated to remotely control movement of the components of the robotic system based on the sequence or movements of the components to perform maintenance on the power system component.

Abstract: A robotic system includes a controller configured to obtain image data from one or more optical sensors and to determine one or more of a location and/or pose of a vehicle component based on the image data. The controller also is configured to determine a model of an external environment of the robotic system based on the image data and to determine tasks to be performed by components of the robotic system to perform maintenance on the vehicle component. The controller also is configured to assign the tasks to the components of the robotic system and to communicate control signals to the components of the robotic system to autonomously control the robotic system to perform the maintenance on the vehicle component.

Abstract: A system evaluates modifications to components of an autonomous vehicle (AV) stack. The system receives driving recommendations traffic scenarios based on user annotations of video frames showing each traffic scenario. For each traffic scenario, the system predicts driving recommendations based on the AV stack. The system determines a measure of quality of driving recommendation by comparing predicted driving recommendations based on the AV stack with the driving recommendations received for the traffic scenario. The measure of quality of driving recommendation is used for evaluating components of the AV stack. The system determines a driving recommendation for an AV corresponding to ranges of SOMAI (state of mind) score and sends signals to controls of the autonomous vehicle to navigate the autonomous vehicle according to the driving recommendation. The system identifies additional training data for training machine learning model based on the measure of driving quality.

Abstract: A system evaluates modifications to components of an autonomous vehicle (AV) stack. The system receives driving recommendations traffic scenarios based on user annotations of video frames showing each traffic scenario. For each traffic scenario, the system predicts driving recommendations based on the AV stack. The system determines a measure of quality of driving recommendation by comparing predicted driving recommendations based on the AV stack with the driving recommendations received for the traffic scenario. The measure of quality of driving recommendation is used for evaluating components of the AV stack. The system determines a driving recommendation for an AV corresponding to ranges of SOMAI (state of mind) score and sends signals to controls of the autonomous vehicle to navigate the autonomous vehicle according to the driving recommendation. The system identifies additional training data for training machine learning model based on the measure of driving quality.

Abstract: A system evaluates modifications to components of an autonomous vehicle (AV) stack. The system receives driving recommendations traffic scenarios based on user annotations of video frames showing each traffic scenario. For each traffic scenario, the system predicts driving recommendations based on the AV stack. The system determines a measure of quality of driving recommendation by comparing predicted driving recommendations based on the AV stack with the driving recommendations received for the traffic scenario. The measure of quality of driving recommendation is used for evaluating components of the AV stack. The system determines a driving recommendation for an AV corresponding to ranges of SOMAI (state of mind) score and sends signals to controls of the autonomous vehicle to navigate the autonomous vehicle according to the driving recommendation. The system identifies additional training data for training machine learning model based on the measure of driving quality.

Abstract: Among other things, techniques are described for steering angle calibration. An autonomous vehicle receives a steering angle measurement and a yaw rate measurement, and estimates a steering angle offset using the steering angle measurement, the yaw rate measurement, and a wheel base of the autonomous vehicle. An estimated yaw rate is determined based on a yaw rate model, the steering angle measurement and the estimated steering angle offset. The yaw rate measurement and the estimated yaw rate are compared and an action is initiated on the autonomous vehicle in response to the comparing.

Abstract: A robotic system includes a controller configured to obtain image data from one or more optical sensors and to determine one or more of a location and/or pose of a vehicle component based on the image data. The controller also is configured to determine a model of an external environment of the robotic system based on the image data and to determine tasks to be performed by components of the robotic system to perform maintenance on the vehicle component. The controller also is configured to assign the tasks to the components of the robotic system and to communicate control signals to the components of the robotic system to autonomously control the robotic system to perform the maintenance on the vehicle component.

Abstract: A controller for operating a rod pumping unit at a pump speed. The controller includes a processor configured to operate a pump piston of the rod pumping unit at a first speed. The processor is further configured to determine a pump fillage level for a pump stroke based on a position signal and a load signal. The processor is further configured to reduce the pump speed to a second speed based on the pump fillage level for the pump stroke.

Abstract: Among other things, we describe techniques for redundancy in autonomous vehicles. For example, an autonomous vehicle can include two or more redundant autonomous vehicle operations subsystems.

Abstract: A robotic system includes a controller configured to obtain image data from one or more optical sensors and to determine one or more of a location and/or pose of a vehicle component based on the image data. The controller also is configured to determine a model of an external environment of the robotic system based on the image data and to determine tasks to be performed by components of the robotic system to perform maintenance on the vehicle component. The controller also is configured to assign the tasks to the components of the robotic system and to communicate control signals to the components of the robotic system to autonomously control the robotic system to perform the maintenance on the vehicle component.

Abstract: A control system for operating a beam pumping unit includes a strain gauge and a beam pumping unit controller. The strain gauge is coupled to a Samson post of the beam pumping unit, and is configured to measure a Samson post strain. The beam pumping unit controller is coupled to the strain gauge and is configured to operate the beam pumping unit to induce a variable load on a rod of the beam pumping unit. The beam pumping unit controller is further configured to receive the Samson post strain from the strain gauge and compute the variable load based on the Samson post strain.

Abstract: A controller for operating a rod pumping unit at a pump speed. The controller includes a processor configured to operate a pump piston of the rod pumping unit at a first speed. The processor is further configured to determine a pump fillage level for a pump stroke based on a position signal and a load signal. The processor is further configured to reduce the pump speed to a second speed based on the pump fillage level for the pump stroke.

Abstract: A controller for operating a rod pumping unit includes a processor configured to operate the rod pumping unit at a pumping profile speed. The processor is further configured to compute a first downhole dynamometer card from surface measurements at the rod pumping unit. The processor is further configured to compute a second downhole dynamometer card from the surface measurements. The processor is further configured to validate at least one of the first downhole dynamometer card and the second downhole dynamometer card based on a rod pumping unit condition.

Abstract: Among other things, techniques are described for steering angle calibration. An autonomous vehicle receives a steering angle measurement and a yaw rate measurement, and estimates a steering angle offset using the steering angle measurement, the yaw rate measurement, and a wheel base of the autonomous vehicle. An estimated yaw rate is determined based on a yaw rate model, the steering angle measurement and the estimated steering angle offset. The yaw rate measurement and the estimated yaw rate are compared and an action is initiated on the autonomous vehicle in response to the comparing.

Abstract: Systems and methods are provided for an automation system. The systems and methods calculate a motion trajectory of a manipulator and an end-effector. The end-effector is configured to grasp a target object. The motion trajectory defines successive positions of the manipulator and the end-effector along a plurality of via-points toward the target object. The systems and methods further acquire force/torque (F/T) data from an F/T sensor associated with the end-effector, and adjusts the motion trajectory based on the F/T data.

Abstract: A robotic system is provided that includes a base, an articulable arm, a visual acquisition unit, and a controller. The articulable arm may extend from a base and is movable toward a target. The visual acquisition unit can be mounted to the arm or the base and to acquire image data. The controller is operably coupled to the arm and the visual acquisition unit, and can derive from the image data environmental information corresponding to at least one of the arm or the target. The controller further can generate at least one planning scheme using the environmental information to translate the arm toward the target, select at least one planning scheme for implementation, and control movement of the arm toward the target using the at least one selected planning scheme.

Abstract: A robot system is provided that includes a base, an articulable arm, a visual acquisition unit, and at least one processor. The articulable arm extends from the base and is configured to be moved toward a target. The visual acquisition unit is mounted to the arm or the base, and acquires environmental information. The at least one processor is operably coupled to the arm and the visual acquisition unit, the at least one processor configured to: generate an environmental model using the environmental information; select, from a plurality of planning schemes, using the environmental model, at least one planning scheme to translate the arm toward the target; plan movement of the arm toward the target using the selected at least one planning scheme; and control movement of the arm toward the target using the at least one selected planning scheme.

Abstract: Systems and methods are provided for an automation system. The systems and methods calculate a motion trajectory of a manipulator and an end-effector. The end-effector is configured to grasp a target object. The motion trajectory defines successive positions of the manipulator and the end-effector along a plurality of via-points toward the target object. The systems and methods further acquire force/torque (F/T) data from an F/T sensor associated with the end-effector, and adjusts the motion trajectory based on the F/T data.

Abstract: A robotic system includes a controller configured to obtain image data from one or more optical sensors and to determine one or more of a location and/or pose of a vehicle component based on the image data. The controller also is configured to determine a model of an external environment of the robotic system based on the image data and to determine tasks to be performed by components of the robotic system to perform maintenance on the vehicle component. The controller also is configured to assign the tasks to the components of the robotic system and to communicate control signals to the components of the robotic system to autonomously control the robotic system to perform the maintenance on the vehicle component.

Abstract: Systems and methods are provided for an automation system. The systems and methods calculate a motion trajectory of a manipulator and an end-effector. The end-effector is configured to grasp a target object. The motion trajectory defines successive positions of the manipulator and the end-effector along a plurality of via-points toward the target object. The systems and methods further acquire force/torque (F/T) data from an F/T sensor associated with the end-effector, and adjusts the motion trajectory based on the F/T data.