Abstract: Techniques for auto-locating and autonomous operation data storage are disclosed. An example method can include storing a multi-dimensional representation of an aircraft in a data storage. The method can further include causing transmission of the multi-dimensional representation of the aircraft and first control instructions to a robot for performing a first autonomous operation on the aircraft, the robot configured to identify the aircraft based on the multi-dimensional representation, the aircraft located at a premises. The method can further include processing sensor data generated at the premises. The method can further include generating second control instructions for performing a second autonomous operation on the aircraft based on the sensor data. The method can further include causing transmission of the second control instructions to the robot for performing the second autonomous operation on the aircraft.

Abstract: The present disclosure relates to a multi-tiered computing environment for balancing compute resources in support of robot operations. In an example, a first server can receive from a robot located on a premises, a request and a first data, The first server can access a local configuration table to determine a program code associated with the first operation. The first server can generate second data based on the first data and the local configuration table. The first server can transmit the second data to the robot. The first server can receive, from the robot, third data indicating performance of the operation. The first server can transmit to a second server associated with the airplane model, fourth data, the fourth data generated based on the third data.

Abstract: Techniques for auto-locating and autonomous operation data storage are disclosed. An example method can include storing a representation of an aircraft in a data storage of the computing system. The representation and instructions for performing a first operation on the aircraft can be transmitted to a first robot. The first robot can be configured to identify the aircraft based on a first feature of the representation. The method can further include receiving sensor data. The method can further include transmitting the sensor data to an analysis service to identify a state of a part of the aircraft. The method can further include storing the analysis in the data storage. The method can further include generating second instructions for performing a second operation on the aircraft based on the analysis and the representation stored in the data storage.

Abstract: The present disclosure relates to a multi-tiered computing environment for balancing compute resources in support of robot operations. In an example, a robot is tasked with performing an operation associated with an airplane having an airplane model. To do so, the robot may need another operation that is computationally complex to be performed. An on-premises server can execute a process that corresponds to this computationally-complex operation based on sensor data of the robot and can output the resulting data to the robot. Next, the robot can use the resulting data to execute another process corresponding to its operation and can indicate performance of this operation to the on-premises network. The on-premises network can send the indication about the operation performance to a top-tier server that is also associated with the airplane model.

Abstract: Aspects of the disclosure are directed towards artificial intelligence-based modeling of target objects, such as aircraft parts. In an example, a system initially trains a machine learning (ML) model based on synthetic images generated based on multi-dimensional representation of target objects. The same system or a different system subsequently further trains the ML model based on actual images generated by cameras positioned by robots relative to target objects. The ML model can be used to process an image generated by a camera positioned by a robot relative to a target object based on a multi-dimensional representation of the target object. The output of the ML model can indicate, for a detected target, position data, a target type, and/or a visual inspection property. This output can then be used to update the multi-dimensional representation, which is then used to perform robotics operations on the target object.

Abstract: Aspects of the disclosure are directed towards path generation. A method includes a user interface (UI) displaying a first page on a first pane, wherein the first page provides a first control input for registering a working frame of a target object with a reference frame of a robot. The method further includes receiving, via the UI, a first user selection of the first control input for registering the working frame with the reference frame, based on detection of the first user selection. The UI can display a second page on the first pane, wherein the second page provides a second control input for generating a path for the robot to traverse over a surface of the target object. The method further includes receiving, via the UI, a second user selection of the second control input for generating the path, based on detection of the second user selection.

Abstract: Aspects of the disclosure are directed towards path generation. A method includes a computing system registering a first coordinate system of a target object with a second coordinate system of a robot. The computing system can generate a trajectory over the surface of the target object based on the registration. The computing system can generate a robot job file based at least in part on the generated trajectory. The computing system can transmit the robot job file to a robot controller.

Abstract: Aspects of the disclosure are directed towards artificial intelligence-based modeling of target objects, such as aircraft parts. In an example, a system initially trains a machine learning (ML) model based on synthetic images generated based on multi-dimensional representation of target objects. The same system or a different system subsequently further trains the ML model based on actual images generated by cameras positioned by robots relative to target objects. The ML model can be used to process an image generated by a camera positioned by a robot relative to a target object based on a multi-dimensional representation of the target object. The output of the ML model can indicate, for a detected target, position data, a target type, and/or a visual inspection property. This output can then be used to update the multi-dimensional representation, which is then used to perform robotics operations on the target object.

Abstract: Aspects of the disclosure are directed towards path generation. A method includes a user interface (UI) displaying a first page on a first pane, wherein the first page provides a first control input for registering a working frame of a target object with a reference frame of a robot. The method further includes receiving, via the UI, a first user selection of the first control input for registering the working frame with the reference frame, based on detection of the first user selection. The UI can display a second page on the first pane, wherein the second page provides a second control input for generating a path for the robot to traverse over a surface of the target object. The method further includes receiving, via the UI, a second user selection of the second control input for generating the path, based on detection of the second user selection.

Abstract: A movable gantry system is configured to interface with jigs of different types or sizes and/or with different positions of a same jig and/or to perform operations on different parts mounted in such jigs. To do so, the movable gantry system includes an end effector, a gantry, and a computing system. The end effector is mounted within the gantry and provides at least rotational movement to perform operations on a part. The gantry is movable and interfaces with a jig holding the part. Further, the gantry provides translational movement to the end effector. The computing system identifies the gantry and the part and controls the gantry and the end effector, thereby facilitating the operations on the part. The computing system stores, in a data store, information about an operation upon performed by the end effector after a datuming process based translational data and on rotational data.

Abstract: Techniques are described herein for generating a database. A method can include receiving a first value associated with a physical element of a target object and obtained during a performance of a first stage of a robot task, and a second value associated with the physical element and obtained during a second stage, wherein the first and second value describe a same characteristic of the physical element and are represented in a target object coordinate system. A third value associated with a tool of a robot and obtained during a third stage can be received, wherein the third value is represented in the robot coordinate system. A first data structure can be generated, wherein the first data structure comprises the first, second, and third value. The first data structure can be associated with a second data structure, wherein the second data structure comprises a fourth value identifying the target object.

Abstract: The present disclosure relates to a multi-tiered computing environment for balancing compute resources in support of robot operations. In an example, a robot is tasked with performing an operation associated with an airplane having an airplane model. To do so, the robot may need another operation that is computationally complex to be performed. An on-premises server can execute a process that corresponds to this computationally-complex operation based on sensor data of the robot and can output the resulting data to the robot. Next, the robot can use the resulting data to execute another process corresponding to its operation and can indicate performance of this operation to the on-premises network. The on-premises network can send the indication about the operation performance to a top-tier server that is also associated with the airplane model.

Abstract: Aspects of the disclosure are directed towards generating a trajectory for use by a robot to perform an operation on a target object. A method includes a robot instructed to traverse a surface of the target object from the starting position and obtain sensor input from a sensor system of the robot using a first trajectory specified by a CAD file for the target object. Sensor input that includes a first height to the target object, a width of the seam, and a depth of the seam may be received. A second trajectory may be generated for traversing the surface of the target object and that modifies the first trajectory based on the sensor input. The robot may be instructed to move to the starting position and traverse the surface of the target object using the second trajectory and apply a sealant to the seam.

Abstract: A pump mechanism for dispensing a sealant is described. The pump mechanism can include a housing comprising a cavity; a tubing track arranged in the cavity, the tubing track having a curved surface inward from the housing; a tubing arranged in proximity to the curved surface of the tubing track; a rotor assembly arranged inward from the curved surface and the tubing, the rotor assembly comprising a rotor and a first roller in contact with the tubing, the first roller compressing the tubing to reduce fluidic communication between a first end and a second end of the tubing; a motor in operable communication with the rotor assembly; and a computing device configured to control a rotational speed of the rotor based on a velocity of a tool center point (TCP) of the robot.

Abstract: Aspects of the disclosure are directed towards generating a trajectory for use by a robot to perform an operation on a target object. A method includes a robot instructed to traverse a surface of the target object from the starting position and obtain sensor input from a sensor system of the robot using a first trajectory specified by a CAD file for the target object. Sensor input that includes a first height to the target object, a width of the seam, and a depth of the seam may be received. A second trajectory may be generated for traversing the surface of the target object and that modifies the first trajectory based on the sensor input. The robot may be instructed to move to the starting position and traverse the surface of the target object using the second trajectory and apply a sealant to the seam.

Abstract: Techniques for auto-locating and positioning relative to an aircraft are disclosed. An example method can include a computing system receiving a multi-dimensional representation of a candidate target aircraft upon which the robot is to perform an operation. The computing system can extract a first feature from the multi-dimensional representation associated with the candidate target aircraft. The computing system can compare the first feature with a second feature associated with a target aircraft. The computing system can determine whether the candidate aircraft is the target aircraft based on the comparison. The computing system can determine a path from the location of the robot to the target aircraft based at least in part on the determining of whether the candidate aircraft is the target aircraft.

Abstract: Techniques for auto-locating and autonomous operation data storage are disclosed. An example method can include storing a representation of an aircraft in a data storage of the computing system. The representation and instructions for performing a first operation on the aircraft can be transmitted to a first robot. The first robot can be configured to identify the aircraft based on a first feature of the representation. The method can further include receiving sensor data. The method can further include transmitting the sensor data to an analysis service to identify a state of a part of the aircraft. The method can further include storing the analysis in the data storage. The method can further include generating second instructions for performing a second operation on the aircraft based on the analysis and the representation stored in the data storage.

Abstract: Aspects of the disclosure are directed towards artificial intelligence-based modeling of target objects, such as aircraft parts. In an example, a system initially trains a machine learning (ML) model based on synthetic images generated based on multi-dimensional representation of target objects. The same system or a different system subsequently further trains the ML model based on actual images generated by cameras positioned by robots relative to target objects. The ML model can be used to process an image generated by a camera positioned by a robot relative to a target object based on a multi-dimensional representation of the target object. The output of the ML model can indicate, for a detected target, position data, a target type, and/or a visual inspection property. This output can then be used to update the multi-dimensional representation, which is then used to perform robotics operations on the target object.

Abstract: Aspects of the disclosure are directed towards artificial intelligence-based modeling of target objects, such as aircraft parts. In an example, a system initially trains a machine learning (ML) model based on synthetic images generated based on multi-dimensional representation of target objects. The same system or a different system subsequently further trains the ML model based on actual images generated by cameras positioned by robots relative to target objects. The ML model can be used to process an image generated by a camera positioned by a robot relative to a target object based on a multi-dimensional representation of the target object. The output of the ML model can indicate, for a detected target, position data, a target type, and/or a visual inspection property. This output can then be used to update the multi-dimensional representation, which is then used to perform robotics operations on the target object.

Abstract: Techniques are described herein for generating a database. A method can include receiving a first value associated with a physical element of a target object and obtained during a performance of a first stage of a robot task, and a second value associated with the physical element and obtained during a second stage, wherein the first and second value describe a same characteristic of the physical element and are represented in a target object coordinate system. A third value associated with a tool of a robot and obtained during a third stage can be received, wherein the third value is represented in the robot coordinate system. A first data structure can be generated, wherein the first data structure comprises the first, second, and third value. The first data structure can be associated with a second data structure, wherein the second data structure comprises a fourth value identifying the target object.