Carnegie Mellon University

Deere + Company

Eyesee360, Inc.

Other

Iowa State University Research Foundation, Inc.

Polaris Industries Inc.

Intelligrated Headquarters, LLC

360fly, Inc.

Eyesee380, Inc.

Herman Herman

Sanjiv Singh

Michael Rondinelli

Anthony Joseph Stentz

Zachary Thomas Bonefas

Carlos Vallespi-Gonzalez

John M Hageman

Gabriel Goldman

Aaron J Bruns

Jose Gonzalez-Mora

Tarik Loukili

Christopher Chandler Fromme

Alonzo James Kelly

Michael G Kean

James Joseph Phelan

Stephen R Corban

Mark Ollis

Mark Michael Chaney

Carl Knox Wellington

Jeremiah K Johnson

Cristian Dima

John E Bares

James Andrew Bagnell

Richard Joseph Connell

Matthew R Wicks

Jean-Sebastien Valois

Michael L Girtman

Bradley Roy Fishburn

Vladimir Altman

Bryan E Dugas

Neil Stegall

Srinivasa Narasimhan

Peter W Rander

Joshua T Lawson

Matthew J Darr

John E Simons

Matthew B Levine

Michael M Miller

Gabriel A Marshall

Blair A Donat

Hernan Badino

Michael J Fuchs

William D Todd

Eric Meyhofer

Thomas Michael Ferner

Andrew C Schmid

Alex J Nykamp

Zachary Pezzementi

Jaime W Bourne

Joan Campoy

Victor S Sierra

Jean-Philippe Tardif

Matt Levine

Alex NyKamp

Scott Miller

Frank Higgins

Bryan G Campbell

Karl Muecke

Johannes Josef Zametzer

Suryansh Saxena

Raphael Bouterige

David K Herdle

Hanke Boesch

Cason Robert Male

Halit Bener Suay

Trenton Tabor

Jonathan Chang

Jose Gonzalez Mora

Katharina Muelling

Daniel Joseph Corbett

Richard Pantaleo

Matthew Glisson

D W Wilson Hamilton

Christopher L Baker

Timothy Ennis Hegadorn

Nishant Pol

Elliot Allen Cuzzillo

Matthew Feld Glisson

Randon Warner

Brad Simon

Prathamesh Kini

Raymond Russell

James Ketterer

Bruce Digney

Richard McCargo Moore, Jr

Steven John Scheding

Jorgen Pederson

Prasanna Kannappan

Louis Hiener

Krishna Pandravada

Jean-Phillipe Tardif

Jason Kulk

Stuart Edwin Lawrence, Iii

Christopher Randolph Baker

David G Galati

Justin C Haines

Andrew Strat

Jeffrey David McMahill

Alonzo J Kelley

Michael Miller

A high-density storage system for goods is described in which totes carrying the goods are stored in a storage structure and stored and retrieved via stationary or mobile conveyors running along opposite ends of each layer of the storage structure. The totes may be moved to or from the conveyors as the rows move at a constant velocity toward or away from the conveyors. Totes at the ends of rows are quickly moved and stored in another row until the desired tote appears at the end of the row, at which point the desired tote is carried to an exit point of the storage structure by one of the conveyors.

High-density automated storage and retrieval system

A high-density storage system for goods is described in which totes carrying the goods are storage in a storage structure and stored and retrieved by robotic carriers. The carriers move laterally and/or longitudinally along the exterior of the support structure and retrieve totes from the interior of the structure by manipulating rows of coupled totes. Totes at the ends of rows are quickly removed and stored in another row until the desired tote appears at the end of the row, at which point the carrier proceeds with the tote to the exit point of the storage system. Storing totes is also a quick action by pushing them into any row. As a tote is pushed into the row, it will automatically couple with a tote inside the row that it comes into contact with.

An aquatic vessel, illustratively a pontoon boat including a thruster system is disclosed. The aquatic vessel executes a process to automatically position the aquatic vessel relative to a target location such as a mooring implement. Exemplary mooring implements include a dock, a slip, or a lift.

System and method for positioning an aquatic vessel

An intelligent, autonomous interior cleaning robot capable of autonomously mapping and cleaning multiple rooms in a house in an intelligent manner is described. Various combinations of passive and active sensors may be used to perform mapping, localization, and obstacle avoidance. In particular, the robot uses stereo cameras with a static projected light pattern to generate 3D data. In addition, the robot may use optical sensors in various locations, laser ToF sensors, inertial measurement units and visual odometry to enhance the localization and mapping capabilities.

Intelligent cleaning robot

A high-density storage system for goods is described in which totes carrying the goods are stored in a storage structure and stored and retrieved via stationary or mobile conveyers running along opposite ends of each layer of the storage structure. The totes may be moved to or from the conveyers as the rows move at a constant velocity toward or away from the conveyers. Totes at the ends of rows are quickly moved and stored in another row until the desired tote appears at the end of the row, at which point the desired tote is carried to an exit point of the storage structure by one of the conveyers.

A grain tailings elevator for a combine harvester includes a an elevator housing having an interior containing a conveyor arrangement configured to transport grain tailings through the elevator housing to a discharge outlet. The elevator housing has a side wall with a window to the interior of the elevator housing. A camera having a camera housing containing an image sensor is mounted to the side wall of the elevator housing over the window with the image sensor in registration with the window. The image sensor is trained on the conveyor arrangement and configured to image the grain tailings transported by the conveyor arrangement through the elevator housing.

Grain quality control system and method

A system and method for controlling a vehicle platform, the system comprising on onboard controller and an off-board controller that work together to provide autonomous navigation in fields or similar areas where the vehicle is deployed, perception for obstacle detection and avoidance, and a user interface for user/vehicle interaction and control.

System and method of control for autonomous or remote-controlled vehicle platform

A volume of contents in a container of a work vehicle can be estimated in various examples. One example involves a system with a 3D sensor on a work vehicle, where the sensor captures images of a material in a container of the work vehicle. A processor device that is in communication with the 3D sensor determines a volume of the material in the container using the images.

Estimating a volume of contents in a container of a work vehicle

One or more undesired objects can be detected in a restricted zone at worksite. In one example, a base station is configured to be placed in or around the restricted zone. The base station includes a housing, a sensor suite, and a base station controller. The sensor suite is configured to detect one or more undesired object in a subzone at least partially overlapping with the restricted zone and generate an output signal based on the undesired object. The subzone is limited by a range of detection from the sensor suite. The base station controller can receive the output signal from the sensor suite and transmit an alert signal. A warning indicator can receive the alert signal and activate in response to receiving the alert signal. The warning indicator can be positioned on the base station or on a mobile device located remote from the base station.

Detecting objects in a restricted zone

A computer vision camera unit comprising first and second pairs, of image capture assemblies, both of which have an approximately same orientation to a first axis. The first and second pairs are optically focused to capture first and second fields of view that, at least, overlap each other. Providing the first and second pairs with imager chips of differing light sensitivity permits high dynamic range to be realized. Having the second field of view within the first field of view permits specialization, between the first and second pairs, similar to the roles of peripheral and foveal vision, found in biological stereo vision systems. The image capture assemblies can be organized in a rectangular configuration that implies third and fourth pairs, orthogonal to the first and second pairs. The orthogonal but interlocking nature, of the first and second pairs in relation to the third and fourth pairs, enables an autocalibration technique.

Method and apparatus for a computer vision camera unit

The present disclosure relates to a material handling system for manipulating items. The material handling system includes a repositioning system comprising a robotic tool which includes a robotic arm portion and an end effector. The robotic tool is configured to manipulate an item in a first orientation and reorient the item to a second orientation. The material handling system further includes a vision system having one or more sensors positioned within the material handling system. The vision system is configured to generate inputs corresponding to the characteristics of the items. The material handling system may further include a controller executing instructions to cause the material handling system to identify the item in the first orientation, based on the one or more characteristics of the item, initiate, by the repositioning system, picking of the item in the first orientation, and re-orient the item in the second orientation.

Method and system for manipulating items

A method and system for controlling the quality of harvested grains include capturing, by one or more image sensors, one or more images of material at a sampling location within a grain elevator of the combine harvester. The captured images are defined by a set of image pixels represented by image data and having a classification feature indicative of grain or non-grain material. One or more controllers receive the image data associated with the one or more images captured by the image sensor(s) and select a sample image defined by a subset of image pixels of the set of image pixels. The controller(s) apply a convolutional neural network (CNN) algorithm to the image data of the subset of image pixels of the selected sample image to determine the classification feature. The controller(s) analyze the determined classification feature to adjust an operational parameter of the combine harvester.

A self-guided blossom picker uses a vision system to identify and locate blossoms or inflorescence growing on a plant. The device can be towed by a tractor or it can be self-propelled. Image data captured by the vision system is sent to an machine vision module, which interprets the data and identifies a location of blossom. A controller uses the location data to command a picker to the proper location. A cutter on the picker is actuated to remove the blossom.

Self-guided blossom picking device

A volume of contents in a container of a work vehicle can be estimated in various examples. One example involves a system with a 3D sensor on a work vehicle, where the 3D sensor captures images of the contents in a container of the work vehicle. The system also includes a sensor for detecting an angle of the container. A processor device that is in communication with the 3D sensor and the sensor determines a volume of the contents in the container using the images and the angle.

Growing community of inventors

Gibsonia, PA, United States of America

Herman Herman