Harris Corporation

Tomahawk Robotics

Eagle Technology, LLC

L3harris Technologies, Inc.

Matthew D Summer

Paul Michael Bosscher

William S Bowman

Loran J Wilkinson

Andrew D Falendysz

Michael E Bowman

Kevin Makovy

Daniel R Hedman

John B Rust

Brad Truesdell

Sean Wagoner

Jeffrey S Cooper

Bradley D Truesdell

Nicholas Murphy-DuBay

Michael W Holt

Laurance H Davis, Iii

Robert Fred Varley

Miguel Ortega-Morales

Timothy M Williams

Sean J Irvin

Michael J Summer

Matthew R Danko

Mark B Moffett

Jeffrey M Pollard

James A Stiver

Mark A Cory

Paul M Bosscher

Kevin M Makovy

Laurance H Iii Davis

Methods and systems are described herein for generating composite data streams. A data stream processing system may receive multiple data streams from, for example, multiple unmanned vehicles and determine, based on the type of data within each data stream, a machine learning model for each data stream for processing the type of data. Each machine learning model may receive the frames of a corresponding data stream and output indications and locations of objects within those data streams. The data stream processing system may then generate a composite data stream with indications of the detected objects.

Architecture for distributed artificial intelligence augmentation with objects detected using artificial intelligence models from data received from multiple sources

Systems and methods of manipulating/controlling robots. In many scenarios, data collected by a sensor (connected to a robot) may not have very high precision (e.g., a regular commercial/inexpensive sensor) or may be subjected to dynamic environmental changes. Thus, the data collected by the sensor may not indicate the parameter captured by the sensor with high accuracy. The present robotic control system is directed at such scenarios. In some embodiments, the disclosed embodiments can be used for computing a sliding velocity limit boundary for a spatial controller. In some embodiments, the disclosed embodiments can be used for teleoperation of a vehicle located in the field of view of a camera.

Spatial teleoperation of legged vehicles

Methods and systems are described herein for enabling aerial vehicle navigation in GPS-denied areas. The system may use a camera to record images of terrain as the aerial vehicle is flying to a target location. The system may then detect (e.g., using a machine learning model) objects within those images and compare those objects with objects within an electronic map that was loaded onto the aerial vehicle. When the system finds one or more objects within the electronic map that match the objects detected within the recorded images, the system may retrieve locations (e.g., GPS coordinates) of the objects within the electronic map and calculate, based on the coordinates, the location of the aerial vehicle. Once the location of the aerial vehicle is determined, the system may navigate to a target location or otherwise adjust a flight path of the aerial vehicle.

Computer vision classifier defined path planning for unmanned aerial vehicles

Methods and systems are described herein for hosting and arbitrating algorithms for the generation of structured frames of data from one or more sources of unstructured input frames. A plurality of frames may be received from a recording device and a plurality of object types to be recognized in the plurality of frames may be determined. A determination may be made of multiple machine learning models for recognizing the object types. The frames may be sequentially input into the machine learning models to obtain a plurality of sets of objects from the plurality of machine learning models and object indicators may be received from those machine learning models. A set of composite frames with the plurality of indicators corresponding to the plurality of objects may be generated, and an output stream may be generated including the set of composite frames to be played back in chronological order.

Classification parallelization architecture

Methods and systems are described herein for detecting motion-induced errors received from inertial-type input devices and for generating accurate vehicle control commands that account for operator movement. These methods and systems may determine, using motion data from inertial sensors, whether the hand/arm of the operator is moving in the same motion as the body of the operator, and if both are moving in the same way, these systems and methods may determine that the motion is not intended to be a motion-induced command. However, if the hand/arm of the operator is moving in a different motion from the body of the operator, these methods and systems may determine that the operator intended the motion to be a motion-induced command to a vehicle.

Inertially isolated spatial control

Methods and systems are described herein for determining three-dimensional locations of objects within identified portions of images. An image processing system may receive an image and an identification of location within an image. The image may be input into a machine learning model to detect one or more objects within the identified location. Multiple images may then be used to generate location estimations of those objects. Based on the location estimations, an accurate three-dimensional location may be calculated.

Point of interest tracking and estimation

Systems and methods of detecting intent of spatial control

Systems and methods of remote teleoperation of robotic vehicles

A common command and control architecture (alternatively termed herein as a 'universal control architecture') is disclosed that allows different unmanned systems, including different types of unmanned systems (e.g., air, ground, and/or maritime unmanned systems), to be controlled simultaneously through a common control device (e.g., a controller that can be an input and/or output device). The universal control architecture brings significant efficiency gains in engineering, deployment, training, maintenance, and future upgrades of unmanned systems. In addition, the disclosed common command and control architecture breaks the traditional stovepipe development involving deployment models and thus reducing hardware and software maintenance, creating a streamlined training/proficiency initiative, reducing physical space requirements for transport, and creating a scalable, more connected interoperable approach to control of unmanned systems over existing unmanned systems technology.

Universal control architecture for control of unmanned systems

Unmanned ground vehicle (UGV) includes a rotary joint having an axis of rotation. A rotary joint actuator is responsive to at least one control signal and is configured to cause a rotatable portion of the rotary joint to rotate relative to the vehicle chassis about the rotary joint axis of rotation. A stabilizer flipper having an elongated length is attached to the rotatable portion. Consequently, rotation of the rotatable portion about the rotary joint axis of rotation results in a change of orientation of the stabilizer flipper relative to the chassis. This change in orientation can range between a lateral direction and an longitudinal direction with respect to the vehicle chassis.

UGV with adaptive stabilizer

A recoil managed disruptor includes a disruptor device having a barrel from which a slug of material is fired. A piston is mechanically coupled to the disruptor device. A housing which supports the disruptor on a positioning device includes a deformable recoil absorber (DRA) constraint. The DRA constraint is configured to receive a sacrificial DRA structure comprised of a semi-rigid material. The piston is responsive to a recoil force produced when the disruptor device is fired to travel along an axial length of the housing and permanently deform the DRA structure within the DRA constraint.

Lightweight recoil management

Growing community of inventors

Melbourne, FL, United States of America

Matthew D Summer