WILDER SYSTEMS INC.

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.

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.

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 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 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.

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.