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.

Aspects of the disclosure are directed towards path generation. A method includes a device registering working frame of a target object with a reference frame of a robot. The device can generate a path over a representation of a surface of the target object. The device can generate a trajectory over the surface of the target object based on the registration, the path, and a normal. The device can classify a target type for the real-world target using a machine learning model based on scanned data of the surface of the target object. The device can generate a robot job file, wherein the robot job file comprises the trajectory and an autonomous operation instruction. The device can transmit the robot job file to a robot controller.

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.

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.

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.

Aspects of the disclosure are directed towards path generation. A method includes a device registering working frame of a target object with a reference frame of a robot. The device can generate a path over a representation of a surface of the target object. The device can generate a trajectory over the surface of the target object based on the registration, the path, and a normal. The device can classify a target type for the real-world target using a machine learning model based on scanned data of the surface of the target object. The device can generate a robot job file, wherein the robot job file comprises the trajectory and an autonomous operation instruction. The device can transmit the robot job file to a robot controller.

An automation system (1) determines control data (S), outputs same to controlled elements (5) of the facility (ANL) and thereby controls the facility (ANL). Sensor devices (2) acquire measurement data (M) of the facility (ANL) and at least partly feed same to the automation system (1) and a man-machine interface (3). Said man-machine interface (3) receives planning data (P) from a production planning system (11) and/or control data (S) and/or internal data (I) from the automation system (1). The interface outputs the data (M, S, I) to a person (4). It furthermore receives control commands (S) from the person (4) and forwards them to the automation system (1). The automation system (1) processes the measurement data (M) and the control commands (S) when determining the control data (S). An artificial intelligence unit (9) receives at least part of the measurement data (M), control data (S) and/or internal data (I) and the data output to the person (4). It also receives the control commands (S). The artificial intelligence unit (9) processes the data (M, S, I) and control demands (S) received and determines evaluation results (A) therefrom and makes the latter available to the person (4) and/or to the production planning system (11) and/or sets them for the automation system (1) in the form of control commands (5) directly or via the man-machine interface (3). The data (M, S, I) received by the artificial intelligence unit (9) are at least to some extent dimensional data. Said dimensional data (M, S, I) comprise at least one image captured by a sensor device (2) or an image output via the man-machine interface (3), part of such an image, a time sequence of such images or a time sequence of a part of such images or an acoustic oscillation or an acoustic oscillation spectrum.

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.

A crimp machine is provided and includes an anvil having a lower die having a lower forming surface. The anvil is configured to support a crimp barrel of a terminal that receives a wire. The crimp machine includes a press having an upper die having an upper forming surface. The press is movable relative to the anvil during a crimping process to connect the crimp barrel to the wire. A crimp zone is defined between the upper forming surface and the lower forming surface. The crimp machine includes a vision system positioned to view the crimp zone. The vision system includes an imaging device configured to image the crimp barrel of the terminal and the wire. The vision system operates the imaging device to capture an image prior to the crimping process for performing a validation pre-check prior to the crimping process.

The disclosure provides a method of an appliance, including receiving prediction information indicating a predicted failure of the appliance, obtaining a schedule for which use of a repair service for repairing the predicted failure based on the prediction information is available, transmitting a signal for requesting maintenance information used to delay the predicted failure and maintain a normal operation of the appliance if the obtained schedule is after a predicted failure time point indicated by the prediction information, receiving the maintenance information, and operating based on the maintenance information.