The Boeing Company

Joshua David Kalin

Tyler Charles Staudinger

Eric Raymond Muir

Kevin Scott Callahan

Nick Shabdeh Evans

Philip Lawrence Freeman

Nick Shadbeh Evans

Phillip John Crothers

Alexander Rubin

Stephen G Dame

Gregg Robert Bogucki

Samuel J Tucker

Zachary D Jorgensen

Michelle Crivella

Isaac Chang

Daniel S ReMine

Robert Stephen Strong

Carla E Reynolds

Patrick Joel Michaels

Carla Elizabeth Reynolds

A system to aid in design for manufacturing an object includes a processor and a memory configured to store instructions. The processor is configured to receive first data representing a design of the object to be manufactured and second data representing a machine-learning model. The processor is configured to execute the instructions to generate third data using the first data and the second data. The third data indicates at least one of a modification to the design of the object or process conditions for production of the object. The processor is configured to send the design of the object, the process conditions, or both, to a manufacturing tool to enable production of the object. The machine-learning model is representative of production data and based at least partially on one or more of: object features, process parameters, environmental factors, and quality data.

Object design using machine-learning model

A computer-implemented method for generating a training set of images and labels for a native environment includes receiving physical coordinate sets, retrieving environmental model data corresponding to a georeferenced model of the environment, and creating a plurality of two-dimensional (2-D) rendered images each corresponding to a view from one of the physical coordinate sets. The 2-D rendered images include one or more of the environmental features. The method also includes generating linking data associating each of the 2-D rendered images with (i) labels for the one or more included environmental features and (ii) a corresponding native image. Additionally, the method includes storing the training set including the 2-D rendered images, labels, corresponding native images, and linking data.

Systems and methods for automatically generating training image sets for an environment

An apparatus for building an artificial-intelligence system is provided. The apparatus accesses images of a real-world scene and generates an image of a simulated object corresponding to a real-world object using a first generative adversarial network (GAN). The apparatus inserts the image of the simulated object into the images of the real-world scene to produce images of the real-world scene including the simulated object. The apparatus applies the images of the real-world scene including the simulated object to a second GAN to remove visual artifacts thereby producing a training set of images of the real-world scene including the simulated object. The apparatus trains an artificial-intelligence algorithm using the training set of images to build the artificial-intelligence system to detect the real-world object in further images of the real-world scene and outputs the artificial-intelligence system for deployment on an autonomous vehicle.

Building an artificial-intelligence system for an autonomous vehicle

Systems, methods, and computer-readable media storing instructions for determining cross-track error of an aircraft on a taxiway are disclosed herein. The disclosed techniques capture electronic images of a portion of the taxiway using cameras or other electronic imaging devices mounted on the aircraft, pre-process the electronic images to generate regularized image data, apply a trained multichannel neural network model to the regularized image data to generate a preliminary estimate of cross-track error relative to the centerline of the taxiway, and post-process the preliminary estimate to generate an estimate of cross-track error of the aircraft. Further embodiments adjust a GPS-based location estimate of the aircraft using the estimate of cross-track error or adjust the heading of the aircraft based upon the estimate of cross-track error.

Aircraft positioning on a taxiway

A method for implementing machining tasks for an object. The method identifies location coordinates for a plurality of holes. A task file contains the machining tasks. The robotic devices use the task files to perform the machining tasks. A minimum number of positioning stations is determined where a portion of the machining tasks will be performed by the robotic devices. An ordered sequence for performing the machining tasks is calculated and path a path with the near-minimum distance is determined. Robotic control files are created that cause the robotic devices to perform the machining tasks. The robotic control files are output to the robotic devices to perform the machining tasks to form the plurality of holes.

Robotic task system

Growing community of inventors

Huntsville, AL, United States of America

Joshua David Kalin