Abstract: An airport object location system comprising a number of vehicle location units, a sensor system, and a model generator. The number of vehicle location units is connected to a number of vehicles. The number of vehicle location units generate vehicle location information for the number of vehicles in an area including an operations surface at an airport and vehicle timestamps for the vehicle location information. The sensor system is connected to a reference vehicle. The sensor system is configured to generate sensor data for the area, wherein reference timestamps and reference location information are associated with the sensor data. The model generator is configured to correlate the vehicle location information for the vehicles with the sensor data using the vehicle timestamps, the reference location information, and the reference timestamps to form a dataset.

Abstract: A method is provided for supporting an aircraft approaching a runway on an airfield. The method includes receiving a sequence of images of the airfield, captured by a camera onboard the aircraft approaching the runway. For at least one image of the sequence of images, the method includes applying the image(s) to a machine learning model trained to predict a pose of the aircraft relative to the runway. The machine learning model is configured to map the image(s) to the pose based on a training set of labeled images with respective ground truth poses of the aircraft relative to the runway. The pose is output as a current pose estimate of the aircraft relative to the runway for use in at least one of monitoring the current pose estimate, generating an alert based on the current pose estimate, or guidance or control of the aircraft on a final approach.

Abstract: Systems and methods for autonomous vehicle path planning are described herein. An example vehicle includes an image sensor to obtain an image of a scene of an area surrounding the vehicle. The vehicle also includes navigation system circuitry to: analyze the image and generate a semantically segmented image that identifies one or more types of features in the image; project the semantically segmented image to a two-dimensional (2D) map projection; convert the 2D map projection into a cost map; and determine a path for the vehicle based on the cost map.

Abstract: A method is provided for supporting an aircraft approaching a runway on an airfield. The method includes receiving a sequence of images of the airfield, captured by a camera onboard the aircraft approaching the runway. For at least one image of the sequence of images, the method includes applying the image(s) to a machine learning model trained to predict a pose of the aircraft relative to the runway. The machine learning model is configured to map the image(s) to the pose based on a training set of labeled images with respective ground truth poses of the aircraft relative to the runway. The pose is output as a current pose estimate of the aircraft relative to the runway for use in at least one of monitoring the current pose estimate, generating an alert based on the current pose estimate, or guidance or control of the aircraft on a final approach.

Abstract: Systems and methods of aligning removable sensors mounted on a vehicle based upon sensor output of such sensors are described. Sensor output is collected from a removable sensor (e.g., a digital camera), and a representation of such sensor output is generated (e.g., a digital image). The representation of the sensor output is compared against a spatial template (e.g., a digital mask overlaid on the representation) to determine whether external references in the representation align with corresponding reference indicators in the spatial template. When alignment is required, the removable sensor is aligned by one or both of the following until the external references in a representation of sensor data at an updated current location align with the corresponding reference indicators in the spatial template: (i) adjusting the position of the removable sensor on the vehicle or (ii) adjusting the representation of the sensor output to simulate such repositioning.

Abstract: In an example, a method is described. The method includes causing one or more sensors arranged on an aircraft to acquire, over a window of time, first data associated with a first object that is within an environment of the aircraft, where the one or more sensors include one or more of a light detection and ranging (LIDAR) sensor, a radar sensor, or a camera, causing an array of microphones arranged on the aircraft to acquire, over approximately the same window of time as the first data is acquired, first acoustic data associated with the first object, and training a machine learning model by using the first acoustic data as an input value to the machine learning model and by using an azimuth, a range, an elevation, and a type of the first object identified from the first data as ground truth output labels for the machine learning model.

Abstract: A method including executing a logical computing element (LCE) on a server. Worker LCEs are executed on the server. A first electronic file comprising geometry data in a first data structure is received at the controller LCE. An available worker LCE is identified, by the controller LCE, as an in-use worker LCE from among the worker LCEs. The geometry data is imported by the in-use worker LCE. A job instance is established by the in-use worker LCE. A rendering engine is launched by the in-use worker LCE. The rendering engine generates, for the job instance and using the geometry data, a dataset file in a second data structure different than the first data structure. The dataset file is returned by the in-use worker LCE to the controller LCE. The dataset file is returned by the controller LCE to a remote computer.

Abstract: Solutions are provided for auto-labeling sensor data for machine learning (ML). An example includes: determining a platform's own position; recording, from a sensor aboard the platform, sensor data comprising a sensor image; receiving position data for at least one intruder object (e.g., a nearby airborne object); based at least on the position data for the intruder object and the platform's position, determining a relative position and a relative velocity of the intruder object; based at least on the relative position and a relative velocity of the intruder object and a field of view of the sensor, determining an expected position of the intruder object in the sensor image; labeling the sensor image, wherein the labeling comprises annotating the sensor image with a region of interest and an object identification; and training an artificial intelligence (AI) model using the labeled sensor image.

Abstract: Certain aspects of the present disclosure provide techniques for autonomous image acquisition. This includes determining a plurality of two-dimensional image perspectives for a plurality of image capture devices, and comparing the plurality of two-dimensional image perspectives with a generated two-dimensional representation of a target object, where the two-dimensional representation is generated based on a three-dimensional model of the target object. This further includes automatically moving at least one of the plurality of image capture devices, based on the comparing, to increase a portion of the target object captured by the plurality of image capture devices.

Abstract: A method of supporting an aircraft approaching a runway is provided. Example implementations involve receiving a sequence of images, captured by a camera onboard the aircraft. Example implementations involve applying a received image to a machine learning model trained to detect the runway or a runway marking in the image, and to produce a mask that includes a segment of pixels of the image assigned to an object class for the runway or marking. Example implementations may also involve applying the mask to a corner detector to detect interest points on the mask and match the interest points to corresponding points on the runway or the marking that have known runway-framed local coordinates. Example implementations may also involve performing a perspective-n-point estimation to determine a current pose estimate of the aircraft relative to the runway or the marking and outputting the current pose estimate for use during final approach.

Abstract: In an example, a method is described. The method includes causing one or more sensors arranged on an aircraft to acquire, over a window of time, first data associated with a first object that is within an environment of the aircraft, where the one or more sensors include one or more of a light detection and ranging (LIDAR) sensor, a radar sensor, or a camera, causing an array of microphones arranged on the aircraft to acquire, over approximately the same window of time as the first data is acquired, first acoustic data associated with the first object, and training a machine learning model by using the first acoustic data as an input value to the machine learning model and by using an azimuth, a range, an elevation, and a type of the first object identified from the first data as ground truth output labels for the machine learning model.

Abstract: An automated wire insertion machine for inserting wires into grommet cavity locations of an electrical connector includes a controllable wire insertion robot and a processor to generate pre-generated plug maps based upon an original plug map of the grommet cavity locations and to control the wire insertion robot based upon one pre-generated plug map to insert the wires into the grommet cavity locations. The pre-generated plug maps are generated by defining a range of potential error of the grommet cavity locations that includes at least one of a potential rotational error and a potential translational error, defining an acceptable tolerance of the grommet cavity locations that includes at least one of an acceptable rotational tolerance and an acceptable translational tolerance, and calculating offset values of the grommet cavity locations based on the range of potential error and the acceptable tolerance, thereby generating the plurality of pre-generated plug maps.

Abstract: A method for aligning a removable sensor on a vehicle includes connecting the removable sensor to a sensor mounting device. The method further includes connecting a connector of an alignment apparatus to either (i) the removable sensor such that a spatial reference component of the alignment apparatus has a known position and orientation relative to a current position and orientation of the removable sensor or (ii) a fixed connection location on the vehicle such that the spatial reference component indicates a desired position and orientation of the removable sensor. In addition the method includes adjusting the current position and orientation of the removable sensor by reference to the alignment apparatus to cause the current position and orientation of the removable sensor to match the desired position and orientation of the removable sensor.

Abstract: In an example, a method is described. The method includes causing one or more sensors arranged on an aircraft to acquire, over a window of time, first data associated with a first object that is within an environment of the aircraft, where the one or more sensors include one or more of a light detection and ranging (LIDAR) sensor, a radar sensor, or a camera, causing an array of microphones arranged on the aircraft to acquire, over approximately the same window of time as the first data is acquired, first acoustic data associated with the first object, and training a machine learning model by using the first acoustic data as an input value to the machine learning model and by using an azimuth, a range, an elevation, and a type of the first object identified from the first data as ground truth output labels for the machine learning model.

Abstract: A wire guide and a laser wire-processing device that includes a wire guide are provided. The laser wire-processing device includes a housing and an aperture in a side of the housing, wherein the aperture defines a longitudinal axis that is substantially perpendicular to the aperture. The laser wire-processing device also includes a backstop arranged in the housing and aligned with the longitudinal axis, the backstop defining a wire-contact surface in a facing relationship with the aperture. The laser wire-processing device also includes a wire guide arranged in the housing to manipulate a wire inserted through the aperture into a desired position relative to the longitudinal axis between the aperture and the backstop. The laser wire-processing device also includes a laser operable to direct a laser beam toward an insulation layer of the wire. The wire guide could be a tube arranged in the device or a backstop guide.

Abstract: Solutions are provided for auto-labeling sensor data for machine learning (ML). An example includes: determining a platform's own position; recording, from a sensor aboard the platform, sensor data comprising a sensor image; receiving position data for at least one intruder object (e.g., a nearby airborne object); based at least on the position data for the intruder object and the platform's position, determining a relative position and a relative velocity of the intruder object; based at least on the relative position and a relative velocity of the intruder object and a field of view of the sensor, determining an expected position of the intruder object in the sensor image; labeling the sensor image, wherein the labeling comprises annotating the sensor image with a region of interest and an object identification; and training an artificial intelligence (AI) model using the labeled sensor image.

Abstract: A method including executing a logical computing element (LCE) on a server. Worker LCEs are executed on the server. A first electronic file comprising geometry data in a first data structure is received at the controller LCE. An available worker LCE is identified, by the controller LCE, as an in-use worker LCE from among the worker LCEs. The geometry data is imported by the in-use worker LCE. A job instance is established by the in-use worker LCE. A rendering engine is launched by the in-use worker LCE. The rendering engine generates, for the job instance and using the geometry data, a dataset file in a second data structure different than the first data structure. The dataset file is returned by the in-use worker LCE to the controller LCE. The dataset file is returned by the controller LCE to a remote computer.

Abstract: A method is provided for supporting an aircraft approaching a runway on an airfield. The method includes receiving a sequence of images of the airfield, captured by a camera onboard the aircraft approaching the runway. For at least one image of the sequence of images, the method includes applying the image(s) to a machine learning model trained to predict a pose of the aircraft relative to the runway. The machine learning model is configured to map the image(s) to the pose based on a training set of labeled images with respective ground truth poses of the aircraft relative to the runway. The pose is output as a current pose estimate of the aircraft relative to the runway for use in at least one of monitoring the current pose estimate, generating an alert based on the current pose estimate, or guidance or control of the aircraft on a final approach.

Abstract: A method of reducing entanglement of wires includes receiving, at a tray, one or more first wires of a first wire group from a wire feed system of a wire processing machine. In addition, the method includes receiving, at the tray, one or more second wires of a second wire group from the wire feed system after receiving the first wire group at the tray. The method further includes physically separating, using a separator device associated with the tray, at least a portion of the first wire group from the second wire group. The method also includes moving the second wire group relative to the first wire group, reducing movement of at least a portion of the first wire group relative to a tray surface during movement of the second wire group relative to the first wire group.

Abstract: A shield trim is performed by tearing bunched shield strands circumferentially along a circular edge. The apparatus includes a pair of aligned metal plates that have been drilled through multiple times such that holes of varying diameters pass through both plates. A cable gripper on the entry side of the device clamps the cable in place. A shield gripper on the rear side of the device closes over the exposed shielding of the cable, and the two plates are pushed together. The shield gripper travels with the rear plate, pushing the shield over the wires and causing the shield to bunch between the two plates. With the two plates pushed together, both grippers open and the cable is pulled free from the device. This pull forces a stress concentration which tears the shield strands across the sharp edge of the hole, producing a uniformly trimmed shield.