Hrl Laboratories, LLC

The Boeing Company

Other

Raytheon Company

Gm Global Technolgoy Operations, Inc.

Hughes Electronics Corporation

Yang Chen

Deepak Khosla

Kyungnam Kim

Lei Zhang

Shinko Yuanhsien Cheng

Ryan M Uhlenbrock

Alexander L Honda

Changsoo S Jeong

David J Huber

Fredy Monterroza

Swarup S Medasani

Qin Jiang

Kevin R Martin

Yuri Owechko

Yongqiang Cao

Brandon M Courter

Kurt Reiser

Tsai-Ching Lu

David W Payton

Michael D Howard

Michael J Daily

David L Allen

Jiejun Xu

Anthony Wayne Baker

Shane Edward Arthur

Hong Chen

Aleksey Nogin

Rashmi N Sundareswara

Le Chen

Fan Hin Hung

Sean Soleyman

Zhichao Chen

Sho Chen

Darrel J VanBuer

Chun Leon Chen

Tseu Min Chen

Roland J Menassa

James W Wells

Charles W Wampler, Ii

David B Shu

David Roderick Gerwe

Luke Charles Ingram

Michael A Warren

Navid Naderializadeh

Leon Nguyen

David M Doria

Haden Harrison Smith

Joshua G Fadaie

Todd L Baker

Deying Zhang

Charles Richard Tullock

Ignacio M Soriano

Scott Rad

Jeremy Daniel Howe

William K Leach

Chris A Cantrell

Huapeng Su

Jacob Moorman

Anders Eirik Heussy

Aaron Feldman

Charles McNary

David W Webster

Shawn Moffit

Calvin Chung

Benjamin Nuernberger

A method of monitoring an assembly process is disclosed herein. The method includes obtaining an event model for each of a plurality of objects in the assembly process with the event model for each of the plurality of objects including a predetermined time frame for a change in presence to occur. The method includes collecting an image sequence of the assembly process for monitoring and identifying if a change in presence for each of the plurality of objects occurred with a detector model. The method further includes reviewing the event model for each of the plurality of objects to determine if the predetermined time frame lapsed without the change in presence of a corresponding one of the plurality of objects being identified and issuing an alert if the predetermined time frame lapsed without the presence of a corresponding one of the plurality of objects being identified.

Method and system for process monitoring

Training adversarial aircraft controllers is provided. The method comprises inputting current observed states of a number of aircraft into a world model encoder, wherein each current state represents a state of a different aircraft, and wherein each current state comprises a missing parameter value. A number of adversarial control actions for the aircraft are input into the world model encoder concurrently with the current observed state, wherein the adversarial control actions are generated by competing neural network controllers. The world model encoder generates a learned observation from the current observed states and adversarial control actions, wherein the learned observation represents the missing parameter value from the current observed states. The learned observation and current observed states are input into the competing neural network controllers, wherein each current observed state is fed into a respective controller. The competing neural network controllers then generate next adversarial control actions.

Predictive modeling of aircraft dynamics

A system for refining a six degrees of freedom pose estimate of a target object based on a one-dimensional measurement includes a camera and a range-sensing device. The range-sensing device is configured to determine an actual distance measured between the range-sensing device and an actual point of intersection. The range-sensing device projects a line-of-sight that intersects with the target object at the actual point of intersection. The system also includes one or more processors in electronic communication with the camera and the range-sensing device and a memory coupled to the processors. The memory stores data into one or more databases and program code that, when executed by the processors, causes the system to predict the six degrees of freedom pose estimate of the target object. The system also determines a revised six degrees of freedom pose estimate of the target object based on at least an absolute error.

System for refining a six degrees of freedom pose estimate of a target object

Apparatuses, systems, and methods dynamically model intrinsic parameters of a camera. Methods include: collecting, using a camera having a focus motor, calibration data at a series of discrete focus motor positions; generating, from the calibration data, a set of constant point intrinsic parameters; determining, from the set of constant point intrinsic parameters, a subset of intrinsic parameters to model dynamically; performing, for each intrinsic parameter of the subset of intrinsic parameters, a fit of the point intrinsic parameter values against focus motor positions; generating a model of the intrinsic parameters for the camera based, at least in part, on the fit of the point intrinsic parameter values against the focus motor positions; and determining a position of a fiducial marker within a field of view of the camera based, at least in part, on the model of the intrinsic parameters for the camera.

Method and apparatus for modeling dynamic intrinsic parameters of a camera

Apparatuses and methods train a model and then use the trained model to determine a global three dimensional (3D) position and orientation of a fiduciary marker. In the context of an apparatus for training a model, a wider field-of-view sensor is configured to acquire a static image of a space in which the fiducial marker is disposed and a narrower field-of-view sensor is configured to acquire a plurality of images of at least a portion of the fiducial marker. The apparatus also includes a pan-tilt unit configured to controllably alter pan and tilt angles of the narrower field-of-view sensor during image acquisition. The apparatus further includes a control system configured to determine a transformation of position and orientation information determined from the images acquired by the narrower field-of-view sensor to a coordinate system for the space for which the static image is acquired by the wider field-of-view sensor.

Method and apparatus for determining a three-dimensional position and pose of a fiducial marker

Apparatuses and methods determine the three-dimensional position and orientation of a fiducial marker and tracking the three-dimensional position and orientation across different fields-of-view. Methods include: capturing an image of a first space in which the fiducial marker is disposed with a first sensor having a first field-of-view; determining the three-dimensional location and orientation of the fiducial marker within the first space based on the image of the first space in which the fiducial marker is disposed; capturing an image of a second space in which the fiducial marker is disposed with a second sensor having a second field-of-view; calculating pan and tilt information for the second sensor to move the second field-of-view of the second sensor to acquire an image of the fiducial marker; and determining the three-dimensional location and orientation of the fiducial marker within the second space based on the image of the second space.

Method and apparatus for hand-off and tracking for pose estimation of a fiducial marker

A device includes a control input generator and a neural network trainer. A flight simulator is configured to generate first state data responsive to a first control input from the control input generator and to provide the first state data to a first neural network to generate a candidate second control input. The control input generator is also configured to select, based on a random value, a second control input from between the candidate second control input and a randomized offset control input that is based on a random offset applied to the first control input. The flight simulator is configured to generate second state data responsive to the second control input from the control input generator. The neural network trainer is configured to update weights of the first neural network based, at least in part, on the first state data and the second state data.

Systems and methods for training a neural network to control an aircraft

Described is a system and method for accurate image and/or video scene classification. More specifically, described is a system that makes use of a specialized convolutional-neural network (hereafter CNN) based technique for the fusion of bottom-up whole-image features and top-down entity classification. When the two parallel and independent processing paths are fused, the system provides an accurate classification of the scene as depicted in the image or video.

System and method for the fusion of bottom-up whole-image features and top-down enttiy classification for accurate image/video scene classification

Dental implant

Dental implant with side-cutting grooves

Apparatus and methods for training a machine learning algorithm (MLA) to control a first aircraft in an environment that comprises the first aircraft and a second aircraft are described. Training of the MLA can include: the MLA determining a first-aircraft action for the first aircraft to take within the environment; sending the first-aircraft action from the MLA; after sending the first-aircraft action, receiving an observation of the environment and a reward signal at the MLA, the observation including information about the environment after the first aircraft has taken the first-aircraft action and the second aircraft has taken a second-aircraft action, the reward signal indicating a score of performance of the first-aircraft action based on dynamic and kinematic properties of the second aircraft; and updating the MLA based on the observation of the environment and the reward signal.

Autonomous behavior generation for aircraft

Described is a system for real-time object recognition. The system extracts a candidate target region representing a candidate object from an input image of a scene based on agglomeration of channel features. The candidate target region is classified using a trained convolutional neural network (CNN) classifier, resulting in an initial classified object. A multi-target tracker is used for tracking the classified objects for final classification of each classified object, resulting in a final output, and a device is controlled based on the final output.

Real-time object recognition using cascaded features, deep learning and multi-target tracking

Described is a system for visual activity recognition that includes one or more processors and a memory, the memory being a non-transitory computer-readable medium having executable instructions encoded thereon, such that upon execution of the instructions, the one or more processors perform operations including detecting a set of objects of interest in video data and determining an object classification for each object in the set of objects of interest, the set including at least one object of interest. The one or more processors further perform operations including forming a corresponding activity track for each object in the set of objects of interest by tracking each object across frames. The one or more processors further perform operations including, for each object of interest and using a feature extractor, determining a corresponding feature in the video data. The system may provide a report to a user's cell phone or central monitoring facility.

Neuromorphic system for real-time visual activity recognition

Described is a system for converting a convolutional neural network (CNN) designed and trained for color (RGB) images to one that works on infrared (IR) or grayscale images. The converted CNN comprises a series of convolution layers of neurons arranged in a set kernels having corresponding depth slices. The converted CNN is used for performing object detection. A mechanical component of an autonomous device is controlled based on the object detection.

Transfer learning of convolutional neural networks from visible color (RBG) to infrared (IR) domain

Described is a system for visual activity recognition. In operation, the system detects a set of objects of interest (OI) in video data and determines an object classification for each object in the set of OI, the set including at least one OI. A corresponding activity track is formed for each object in the set of OI by tracking each object across frames. Using a feature extractor, the system determines a corresponding feature in the video data for each OI, which is then used to determine a corresponding initial activity classification for each OI. One or more OI are then detected in each activity track via foveation, with the initial object detection and foveated object detection thereafter being appended into a new detected-objects list. Finally, a final classification is provided for each activity track using the new detected-objects list and filtering the initial activity classification results using contextual logic.

System and method for neuromorphic visual activity classification based on foveated detection and contextual filtering

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Westlake Village, CA, United States of America

Yang Chen