Trimble Navigation Limited

Trimble Inc.

Blue River Technology Inc.

Deere & Company

John William Peake

Stephan Pleines

James Patrick Ostrowski

Gurcan Aral

Arthur Francis Lange

Gregory C Best

Peter V Loomis

Gregory Daniel Chiocco

Michael Downing

Benjamin Ray Chostner

Erik Ehn

Jacqueline Gao Song

Kent M Anderson

Eric Karl Wilson

Rajesh Radhakrishnan

Uriel Aparecido Rosa

Douglas E Brewer

Gregory W White

Brad A Stronger

Brandon M Sights

The calibration system of the farming machine receives images from the camera array. The images comprise visual information representing a view of a portion of an area surrounding the farming machine. To calibrate the camera array, the system determines a relative pose between pairs of cameras by extracting relative position and orientation characteristics from visual information in images captured by the camera pairs. The calibration system determines a calibration error in part by propagating the relative poses between camera pairs. The calibration system may perform automated self-calibration by adjusting one or more of the cameras in the camera array, or may transmit remedial instructions to an operator to adjust the one or more cameras in the camera array.

Camera array calibration in a farming machine

As a farming machine travels through a field of plants, the farming machine operates in a normal operational state to perform one or more farming operations. The farming machine detects an operational failure of a component of the farming machine using measurements obtained from one or more sensors coupled to and monitoring the farming machine. The operational failure of the component impacts performance of a first farming operation of the farming operations. The farming machine configures the farming machine to operate in a remedial operational state. In the remedial operational state, the farming machine diagnoses the operational failure of the component using the obtained measurements. In the remedial operational state, the farming machine selects a solution operation to address the operational failure of the component based on the diagnosis. The farming machine performs the determined solution operation.

Compensatory actions for automated farming machine failure

A farming machine identifies and treats a plant as the farming machine travels through a field. The farming machine includes an array of tiled image sensors for capturing images of the field. A control system identifies an active region in the captured images and generates a tiled image that includes the active region. The control system applies image processing functions to identify the plant in the tiled image and actuates a treatment mechanism to treat the identified plant. The control system causes the array of image sensors to capture the image, identifies the plant, and actuates the treatment mechanism in real time as the farming machine travels through the field.

Plant identification using dynamic cropping

The calibration system of the farming machine receives images from each camera of the camera array. The images comprise visual information representing a view of a portion of an area surrounding the farming machine. To calibrate a pair of cameras including a first camera and second camera, the calibration system determines a relative pose between the pair of cameras by extracting relative position and orientation characteristics from visual information in both an image received from the first camera and an image received from the second camera. The calibration system identifies a calibration error for the pair of cameras based on a comparison of the relative pose with an expected pose between the first pair of cameras. The calibration system transmits a notification to an operator of the farming machine that describes the calibration error and instructions for remedying the calibration error.

A farming machine identifies and treats a plant as the farming machine travels through a field. The farming machine includes a pair of image sensors for capturing images of a plant. The image sensors are different, and their output images are used to generate a depth map to improve the plant identification process. A control system identifies a plant using the depth map. The control system captures images, identifies a plant, and actuates a treatment mechanism in real time.

Plant identification using heterogenous multi-spectral stereo imaging

Systems and methods for sharing convergence data between GNSS receivers are disclosed. Convergence data received at a GNSS receiver via a communication connection may be utilized to determine a position of the GNSS receiver.

Method and system for sharing convergence data

Efficient towed implement guidance to a desired path is achieved by guiding a towing vehicle toward a path on the opposite side of a desired path, then guiding the vehicle back to the desired path. Efficient forward implement guidance to a desired path is achieved by guiding a pushing vehicle along a tractrix. A vehicle leaves a straight line along a tractrix to keep a rear implement on the line as long as possible.

Implement guidance

A path of travel used by an autopilot operation system for auto-guidance of a mobile machine is defined, transparently to a human operator, in response to the human operator engaging and disengaging operation of an implement coupled with the mobile machine. The auto-guidance of the mobile machine is activated, transparently to the human operator, in response to the human operator engaging the implement a second time.

Transparently achieving auto-guidance of a mobile machine

Auto-guidance of a mobile machine is provided that does not require a graphical user interface display. Human interactions with the auto-guidance system of a mobile machine are performed solely with one or more electromechanical switches of the auto-guidance system while the mobile machine is operating. A first point for auto-guidance of a mobile machine is set by an auto-guidance system in response to a first electromechanical switch input. A second point for auto-guidance of the mobile machine is set by the auto-guidance system in response to a second electromechanical switch input after movement of the mobile machine from the first point. The auto-guidance system activates auto-guidance of the mobile machine along a path defined by the first point and the second point. The setting of the first point, the setting of the second point, and the activating are performed by one or more hardware processors.

Providing auto-guidance of a mobile machine without requiring a graphical interface display

Agricultural autopilot steering compensation provides improved steering control when steerable wheels lose traction and/or in tight turns.

Growing community of inventors

Mountain View, CA, United States of America

John William Peake