Deere & Company

Tyler David Schleicher

Curtis A Maeder

Christopher D Birkland

Noel Wayne Anderson

Matthew D Potter

Noah J Rasmussen

Jeffrey E Runde

Nanjun Liu

Andrew Alleyne

David L Mulder

Frederick William Nelson

James Joseph Phelan

Brandon C Carlson

Joshua T Lawson

Andy Dwayne Beck

William Daniel Robinson

Brandon G Leiran

Timothy J Roszhart

Kirk Jacob Chervenka

William Allen Brett

James Lawrence Sachs

Mikel A Bezdek

Austin H Phan

Joel D Hergenreter

Hichem Bouguerra

Todd A Kreykes

Bin Shi

Joshua Dale Wood

Barton Jay Stevens

Monte E Miller

Dale O Pellatz

Shriraam Prabu Subramanian

James L Sachs

Barton J Stevens

A computer-implemented method of operating an agricultural work machine is provided. The method includes initiating row sensing guidance for the agricultural work machine to guide steering of the agricultural work machine based at least one signal from a row sensor; obtaining contextual information; determining whether a row is present based on the contextual information; and selectively ignoring the at least one signal from the row sensor based on whether a row is present. An agricultural work machine and a control system for an agricultural work machine are also provided.

Row sense operations with context sensing

An implement management system detects implement wear and monitors implement states to modify operating modes of a vehicle. The system can determine implement wear using the pull of the implement on the vehicle, the force and angle of which is represented by an orientation vector. The system may measure a current orientation vector and determine an expected orientation vector using sensors and a model (e.g., a machine learned model). Additionally, the implement management system can determine an implement state based on images of the soil and the implement captured by a camera onboard the vehicle during operation. The system may apply different models to the images to determine a likely state of the implement. The difference between the expected and current orientation vectors or the determined implement state may be used to determine whether and how the vehicle's operating mode should be modified.

Implement management system for implement wear detection and estimation

Implement management system for determining implement state

A definition of a boundary corresponding to a geographic area in which an autonomous off-road vehicle (AOV) operates is accessed. The boundary definition includes a classification of each of a plurality of portions of the boundary as being of one or more boundary types, each boundary type associated with one or more vehicle behavior limitations. The AOV navigates to a target portion of the boundary corresponding to the geographic area. The target portion of the boundary is classified as being one or more target boundary types. In response to being within a threshold distance of the target portion of the boundary, the AOV modifies a behavior of the AOV based on the one or more vehicle behavior limitations associated with the one or more target boundary types.

Modifying vehicle behavior based on boundary type

A roll sensor is configured to detect a first roll angle at a corresponding first point of a path plan of a vehicle, or its implement, based on the estimated current position. A roll sensor is configured to detect a second roll angle at a corresponding second point of a path plan of a vehicle, or its implement, based on the estimated current position. A data processor is configured to determine a roll angle delta or difference between the first roll angle and the second roll angle. If the roll angle delta is greater than a reference roll value, the roll angle delta is evaluated over an evaluation period. For example, if the roll angle is greater than the reference roll value for equal to or greater than a target percentage or target ratio for the evaluation period, the data processor is configured to designate the roll angle sensor for possible recalibration.

Method and system for detection of roll sensor bias

A computer-implemented method of controlling a mobile agricultural machine includes obtaining prior field data representing a position of plants in a field, obtaining in situ plant detection data from operation of the mobile agricultural machine in the field, determining a position error in the prior field data based on the in situ plant detection data, and generating a control signal that controls the mobile agricultural machine based on the determined position error.

Agricultural machine map-based control system with position error rectification

A method and system for estimating surface roughness of a ground for an off-road vehicle to control ground speed comprises detecting motion data of an off-road vehicle traversing a field or work site during a sampling interval. A pitch sensor is adapted to detect pitch data of the off-road vehicle for the sampling interval to obtain a pitch acceleration. A roll sensor is adapted to detect roll data of the off-road vehicle for the sampling interval to obtain a roll acceleration. An electronic data processor or surface roughness index module determines or estimates a surface roughness index based on the detected motion data, pitch data and roll data for the sampling interval. The surface roughness index can be displayed on the graphical display to a user or operator of the vehicle, or applied to control or execute a ground speed setting of the vehicle.

Method and system for estimating surface roughness of ground for an off-road vehicle to control ground speed

A method and system for estimating surface roughness of a ground for an off-road vehicle to control an implement comprises detecting motion data of an off-road vehicle traversing a field or work site during a sampling interval. A first sensor is adapted to detect pitch data of the off-road vehicle for the sampling interval to obtain a pitch acceleration. A second sensor is adapted to detect roll data of the off-road vehicle for the sampling interval to obtain a roll acceleration. An electronic data processor or surface roughness index module determines or estimates a surface roughness index based on the detected motion data, pitch data and roll data for the sampling interval. The surface roughness index can be displayed on the graphical display to a user or operator of the vehicle.

Method and system for estimating surface roughness of ground for an off-road vehicle to control an implement

A method and system for estimating surface roughness of a ground for an off-road vehicle to control steering of a vehicle, an implement, or both, comprises detecting motion data of an off-road vehicle traversing a field or work site during a sampling interval. A first sensor is adapted to detect pitch data of the off-road vehicle for the sampling interval to obtain a pitch acceleration. A second sensor is adapted to detect roll data of the off-road vehicle for the sampling interval to obtain a roll acceleration. An electronic data processor or surface roughness index module determines or estimates a surface roughness index based on the detected motion data, pitch data and roll data for the sampling interval. The surface roughness index can be displayed on the graphical display to a user or operator of the vehicle.

Method and system for estimating surface roughness of ground for an off-road vehicle to control steering

An agricultural machine has a set of front wheels and a set of rear wheels that are independently steerable relative to one another. Distance sensors are mounted to the agricultural vehicle to sense a distance between the front wheels, and the adjacent row crops, and between the rear wheels, and the adjacent row crops. Automatic steering control signals are generated to automatically steer the front wheels, and rear wheels, based upon the sensed distances.

Crop row sensing on vehicle with multiple, independently steerable axles/wheels

A stability index is determined based on the estimated position, the estimating heading, and the estimated yaw rate. A surface roughness estimator is adapted to estimate a variance or standard deviation of the roll of roll angle change based on the measured roll angle. A wheel-slip estimator estimates the wheel slip indicator based on numerator of drivetrain-derived wheel speed minus estimated velocity, which numerator is collectively divided by the drivetrain-derived wheel speed. A performance evaluation module determines a performance rating based on the determined stability index, the estimated surface roughness index and the estimated wheel slip.

Monitoring steering conditions of an off-road vehicle

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Ankeny, IA, United States of America

Tyler David Schleicher