Blue River Technology Inc.

Deere + Company

Bonsai Robotics, Inc.

Tyler Niday

Jeremy D Krantz

Divya Sharma

Robert Joseph Plumeau

Michael Albert Elcano

Byron Gajun Ho

Troy K Maddox

Jeffrey E Runde

Margaux M Ascherl

Jordan L Zerr

Ugur Oezdemir

Kunal Saluja

Duane Needham

Rachit Aggarwal

The disclosure includes embodiments for an analysis system. A method according to some embodiments is executed by a graphics processing unit. The method includes generating input data including image data captured with a monocular camera operating in a field environment wherein the image data describes a two-dimensional image of the field environment. The method includes analyzing the input data to generate output data describing a three-dimensional graphic of the field environment depicted in the two-dimensional image. In some embodiments, the output data localizes objects, such as a mobile field device upon which the monocular camera is mounted, within the field environment. In some embodiments, the output data localizes any tangible object located within the field environment with an accuracy that satisfies a threshold for accuracy. The method includes modifying an operation of an autonomous control system of a mobile field device based on the output data.

Generating three-dimensional graphical data based on two-dimensional monocular camera sensor data

A rear perception module is utilized in conjunction with a work vehicle having a work vehicle cabin and a cabin roof. In an embodiment, the rear perception module includes an environmental depth perception (EDP) sensor system including a first EDP device having a field of view encompassing an environmental region to a rear of the work vehicle, a rear module housing mounted to an upper trailing edge portion of the cabin roof, and vents formed in exterior walls of the rear module housing to facilitate airflow through the rear module housing along a cooling airflow path. A heat-generating electronic component is electrically coupled to the first EDP device and positioned in or adjacent the cooling airflow path such that excess heat generated by the heat-generating electronic component is dissipated by convective transfer to airflow conducted along the cooling airflow path during operation of the rear perception module.

Work vehicle perception systems and rear modules

A system and a method are disclosed for calibrating an image acquisition system of a farming machine. The farming machine captures images of objects in an environment as the farming machine moves through an environment. Based on the captured images, a represented three-dimensional (3D) model of the environment representing the objects in a three-dimensional space is generated. From the represented 3D model, a represented location of an object (e.g., building, lamppost, tree) is determined. The represented location of the object is compared to a reference location of the object and the comparison is used to calibrate the image acquisition system by modifying positions, orientations, or optical settings of one or more cameras in the image acquisition system.

Calibration of autonomous farming vehicle image acquisition system

A front perception module is utilized in conjunction with a front ballast system, which is included in a work vehicle and which has a laterally-extending hanger bracket supporting a number of removable ballast weights. In various embodiments, the front perception module includes an environmental depth perception (EDP) sensor system including a first EDP device having a field of view (FOV) encompassing an environmental region forward of the work vehicle, a mounting base attached to the work vehicle, and a front module housing containing the EDP sensor system and joined to the work vehicle through the mounting base. The front module housing is positioned over and vertically spaced from the laterally-extending hanger bracket in a manner enabling positioning of the removable ballast weights beneath the front module housing.

Work vehicle perception systems and front modules

A detection system detects malfunctions in an autonomous farming vehicle during an autonomous routine using one or more models and data from sensors coupled to the autonomous farming vehicle. The models may include machine-learned models trained on the sensor data and configured to identify objects indicative of an operational or malfunctioning component within a tilling assembly such as a tilling shank or sweep. Additionally, a machine-learned model may be trained on sensor data to detect whether debris has plugged the tilling assembly of the autonomous farming vehicle. In response to detecting a malfunction or a plug, the detection system may modify the autonomous routine (e.g., pausing operation) or provide information for the malfunction to be addressed (e.g., the likely location of a malfunctioning sweep that has detached from the tilling assembly).

Machine-learned tillage shank malfunction in an autonomous farming vehicle

Machine-learned tillage sweep malfunction in an autonomous farming vehicle

Machine-learned tillage plug detection in an autonomous farming vehicle

Growing community of inventors

Santa Cruz, CA, United States of America

Tyler Niday