IP Library › Granted Patent US 12,366,860
Granted Patent B2
US 12,366,860 · App. 17/548,497 · Granted Jul 22, 2025

Sensor fusion in agricultural vehicle steering

Inventors: Alex John Weidenbach (Sioux Falls, SD); Jonathan Eugene Mathews (Rapid City, SD)
Assignee: Raven Industries, Inc.
G05D1/0212A01B69/008G05D1/0231G05D1/0257G05D1/0278
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Quick Facts
Patent No.
US 12,366,860
App. No.
17/548,497
Filed
Dec 11, 2021
Granted
Jul 22, 2025
Kind
B2
Art Unit
3667
USPC
701/41
Abstract

A row steering system of an agricultural machine is provided. The row steering system includes a first sensor assembly configured to detect a first orientation of the agricultural machine relative to a path reference in a field using a first sensor configured to measure a first characteristic. The system also includes a second sensor assembly configured to detect a second orientation of the agricultural machine using a second sensor configured to measure a second characteristic. The system further includes a control module including a first evaluation module to obtain a first confidence in the detected first orientation, a second evaluation module to obtain a second confidence in the detected second orientation, and a selector module to selectively provide one or more of the detected first orientation or the detected second orientation to a machine controller of the agricultural machine based on the first and second confidences.

Claims (66)

1. A row steering system of an agricultural machine, the row steering system comprising:

a first sensor assembly configured to detect a first orientation of the agricultural machine relative to a path reference in a field using a first sensor configured to measure a first characteristic;

a second sensor assembly configured to detect a second orientation of the agricultural machine relative to crop rows in the field using a second sensor configured to measure a second characteristic different than the first characteristic; and

one or more processors configured to:

calculate a first confidence in the detected first orientation using a combination of signal quality and line-fit quality of the detected first orientation, wherein the signal quality is determined based on differentiation of a crop or row indicator from a soil or furrow indicator achieving a differentiation threshold, and the line-fit quality is determined based on a comparison of a fitted line to calculated row positions;

calculate a second confidence in the detected second orientation using a combination of signal quality and line-fit quality of the detected second orientation; and

automatically control the agricultural machine based on the first and second confidences.

2. The row steering system of claim 1 , wherein the first sensor is an optical sensor and the second sensor is a radar sensor.

3. The row steering system of claim 1 , wherein the first and second sensors are optical sensors.

4. The row steering system of claim 1 , wherein the first and second sensors are radar sensors.

5. The row steering system of claim 1 , wherein the first sensor or the second sensor comprises at least one of an optical sensor, radar sensor, a global positioning sensor, an acoustic sensor, a LIDAR sensor, or a tactile sensor.

6. The row steering system of claim 1 , wherein the first sensor assembly is configured to couple to the agricultural machine at an elevated location relative to the path reference, and the first sensor is configured to detect the first orientation from the elevated location directed toward the path reference.

7. The row steering system of claim 6 , wherein the second sensor assembly is configured to couple to the agricultural machine at a lateral location relative to the path reference, and the second sensor is configured to detect the second orientation from the lateral location directed across the path reference.

8. The row steering system of claim 1 , wherein the first and second orientation comprise:

a translational difference between a location of the agricultural machine and the path reference; and

an angular difference between an angular orientation of the agricultural machine and the path reference.

9. The row steering system of claim 1 , wherein the one or more processors are further configured to:

weight the first and second orientations based on one or more of the respective first or second confidences;

determine a combined orientation including the weighted first and second orientations; and

selectively provide one of the detected first orientation, the detected second orientation, or the combined orientation to a machine controller.

10. The row steering system of claim 1 , wherein the one or more processors are further configured to:

compare the first confidence to the second confidence; and

selectively provide the first orientation to a machine controller responsive to the first confidence being larger than the second confidence or provide the second orientation to the machine controller responsive to the second confidence being larger than the first confidence.

11. The row steering system of claim 1 , wherein the path reference includes at least one of a guidance path, a guidance line, or one or more crop rows.

12. A system for steering an agricultural machine relative to guidance path, the system comprising:

a sensor module including:

a first sensor assembly configured to obtain a first orientation of the agricultural machine relative to the guidance path based on a first measured characteristic; and

a second sensor assembly configured to obtain a second orientation of the agricultural machine relative to the guidance path based on a second measured characteristic;

one or more processors configured to automatically control the agricultural machine, the one or more processors are configured to:

calculate a first confidence in the first orientation based on the first sensor assembly using a combination of signal quality and line-fit quality of the first orientation and calculate a second confidence in the second orientation based on the second sensor assembly using a combination of signal quality and line-fit quality of the second orientation, wherein the signal quality is determined based on differentiation of a crop or row indicator from a soil or furrow indicator achieving a differentiation threshold, and the line-fit quality is determined based on a comparison of a fitted line to calculated row positions;

generate a combined orientation of the agricultural machine based on a weighted combination of the first orientation and the second orientation;

assign first and second weights to respective first and second orientations based on one or more of the respective first or second confidences; and

generate the combined orientation using the weighted first and second orientations;

an interface configured to communicate the combined orientation; and

a machine controller configured to automatically control the agricultural machine using the combined orientation received from the interface.

13. The system of claim 12 , wherein:

the first orientation of the agricultural machine includes a first position of the agricultural machine relative to the guidance path and a first angle between the agricultural machine and the guidance path as observed with the first sensor assembly; and

the second orientation of the agricultural machine includes a second position of the agricultural machine relative to the guidance path and second angle between the agricultural machine and the guidance path as observed with the second sensor assembly.

14. The system of claim 13 , wherein the one or more processors are further configured to:

generate a composite position from the first and second positions based on the weighted first and second orientations;

generate a composite angle from the first and second angles based on the weighted first and second orientations; and

generate a composite orientation including the composite position and the composite angle.

15. The system of claim 14 , wherein the weighted first orientation and the weighted second orientation include first and second gains, respectively, and the first and second gains are normalized to sum to 1.

16. The system of claim 15 , wherein the weighted first orientation and the weighted second orientation are based on the first confidence and the second confidence, respectively.

17. The system of claim 14 , wherein the weighting module is configured to set a first gain to zero (0) and a second gain to one (1) responsive to first confidence falling below a specified threshold value.

18. The system of claim 14 , wherein the one or more processors are further configured to set a first gain to zero (0) and a second gain to one (1) responsive to the second confidence exceeding the first confidence by a threshold value.

19. The system of claim 14 , wherein the one or more processors are further configured to:

adjust the first weight of the first orientation according to the second measured characteristic of the second sensor assembly.

20. The system of claim 19 , wherein the one or more processors are further configured to decrease the first weight of the first orientation according to the second measured characteristic of the second sensor assembly indicating a decreased capability of the first sensor assembly to measure the first measured characteristic.

21. A method for controlling an agricultural machine according to guidance path, the method comprising:

obtaining a first orientation of the agricultural machine relative to the guidance path with a first sensor;

obtaining a second orientation of the agricultural machine relative to the guidance path with a second sensor;

calculating a first confidence of the first orientation using a combination of signal quality and line-fit quality of the first orientation and calculating a second confidence of the second orientation using a combination of signal quality and line-fit quality of the second orientation;

calculating the signal quality based on differentiation of a crop or row indicator from a soil or furrow indicator achieving a differentiation threshold;

calculating the line-fit quality based on a comparison of a fitted line to calculated row positions;

generating a combined orientation of the agricultural machine by:

comparing the first confidence with the second confidence;

assigning a first and second weights to the respective first and second orientations based on the comparing; and

generating the combined orientation using the weighted first and second orientations; and

automatically controlling the agricultural machine using the combined orientation.

22. The method of claim 21 , further comprising:

configuring the first sensor to couple to the agricultural machine at an elevated location relative to a path reference for detecting the first orientation from the elevated location directed toward the path reference; and

configuring the second sensor to couple to the agricultural machine at a lateral location relative to the path reference for detecting the second orientation from the lateral location directed across the path reference.

23. The method of claim 22 , wherein obtaining the first confidence comprises decreasing the first confidence relative to the second confidence responsive to a detected increase in a height of crops in the guidance path.

24. The method of claim 22 , wherein obtaining the first confidence comprises decreasing the first confidence relative to the second confidence responsive to a detected increase in a size of density of a canopy of crops in the path reference.

25. The method of claim 22 , wherein obtaining the first confidence comprises increasing the first confidence relative to the second confidence responsive to a detected curvature in the path reference.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Aug 15, 2022
From: WEIDENBACH, ALEX; MATHEWS, JONATHAN EUGENE
To: RAVEN INDUSTRIES, INC.
Reel/Frame 060804/0858 →
Continuity (2)
Provisional Application 63124374 · Dec 11, 2020
Related Publication 20220187832A1 · Jun 16, 2022
References Cited (99)
US 3342274A · Wridt, Jr. · 1967 [cited by applicant]
US 4573547A · Yoshimur et al. · 1986 [cited by applicant]
US 5509486A · Andersen · 1996 [cited by applicant]
US 6336051B1 · Pangels et al. · 2002 [cited by applicant]
US 7904218B2 · Jochem et al. · 2011 [cited by applicant]
US 8019513B2 · Jochem et al. · 2011 [cited by applicant]
US 8121345B2 · Joche et al. · 2012 [cited by applicant]
US 8712144B2 · Mas et al. · 2014 [cited by applicant]
US 8725361B2 · Kellum · 2014 [cited by applicant]
US 9668420B2 · Anderson et al. · 2017 [cited by applicant]
US 10524407B2 · Schleicher et al. · 2020 [cited by applicant]
US 10627386B2 · Saez et al. · 2020 [cited by applicant]
US 11399531B1 · Sibley et al. · 2022 [cited by applicant]
US 11470760B2 · Van Roekel et al. · 2022 [cited by applicant]
US 20020106108A1 · Benson et al. · 2002 [cited by applicant]
US 20070001096A1 · Wei et al. · 2007 [cited by applicant]
US 20070003107A1 · Wei et al. · 2007 [cited by applicant]
US 20070014434A1 · Wei et al. · 2007 [cited by applicant]
US 20070271013A1 · Jochem et al. · 2007 [cited by applicant]
US 20080065286A1 · Han · 2008 [cited by examiner]
US 20080294309A1 · Kaprielian et al. · 2008 [cited by applicant]
US 20100063664A1 · Anderson · 2010 [cited by examiner]
US 20110231061A1 · Reeve et al. · 2011 [cited by applicant]
US 20120101861A1 · Lindores · 2012 [cited by applicant]
US 20120136549A1 · Bradai · 2012 [cited by examiner]
US 20120237083A1 · Lange et al. · 2012 [cited by applicant]
US 20130282200A1 · Anderson · 2013 [cited by applicant]
US 20140224377A1 · Bonefas · 2014 [cited by applicant]
US 20140230391A1 · Hendrickson et al. · 2014 [cited by applicant]
US 20150253427A1 · Slichter et al. · 2015 [cited by applicant]
US 20150321694A1 · Nelson, Jr. et al. · 2015 [cited by applicant]
US 20160185346A1 · Awamori et al. · 2016 [cited by applicant]
US 20170013777A1 · Posselius et al. · 2017 [cited by applicant]
US 20170018188A1 · Ono et al. · 2017 [cited by applicant]
US 20170089742A1 · Bruns et al. · 2017 [cited by applicant]
US 20170090068A1 · Xiang et al. · 2017 [cited by applicant]
US 20170223889A1 · Cavender-bares · 2017 [cited by applicant]
US 20170325443A1 · Crinklaw et al. · 2017 [cited by applicant]
US 20170325444A1 · Crinklaw et al. · 2017 [cited by applicant]
US 20170339827A1 · Anderson et al. · 2017 [cited by applicant]
US 20170357029A1 · Lakshmanan · 2017 [cited by applicant]
US 20180061040A1 · Beery · 2018 [cited by examiner]
US 20180168094A1 · Koch et al. · 2018 [cited by applicant]
US 20180188366A1 · Kemmer et al. · 2018 [cited by applicant]
US 20180199502A1 · Briquet-Kerestedjian et al. · 2018 [cited by applicant]
US 20180271016A1 · Milano et al. · 2018 [cited by applicant]
US 20180325012A1 · Ferrari · 2018 [cited by examiner]
US 20190094857A1 · Jertberg et al. · 2019 [cited by applicant]
US 20190129435A1 · Madsen et al. · 2019 [cited by applicant]
US 20200020103A1 · Sneyders et al. · 2020 [cited by applicant]
US 20200053962A1 · Dix · 2020 [cited by examiner]
US 20200100422A1 · Schleicher et al. · 2020 [cited by applicant]
US 20210022282A1 · Wallach et al. · 2021 [cited by applicant]
US 20210132618A1 · Van Roekel et al. · 2021 [cited by applicant]
US 20210195824A1 · Van Roekel et al. · 2021 [cited by applicant]
US 20210331695A1 · Ramakrishnan et al. · 2021 [cited by applicant]
US 20210357664A1 · Kocer et al. · 2021 [cited by applicant]
US 20220061236A1 · Guan et al. · 2022 [cited by applicant]
US 20220183208A1 · Sibley et al. · 2022 [cited by applicant]
US 20230025245A1 · Grover et al. · 2023 [cited by applicant]
BR 102021017293 · 2022 [cited by applicant]
CA 3073713 · 2019 [cited by applicant]
CA 3091297A1 · 2019 [cited by applicant]
DE 112014000906T5 · 2015 [cited by applicant]
EP 1473673A1 · 2004 [cited by applicant]
EP 1738630A1 · 2007 [cited by applicant]
EP 1738631A1 · 2007 [cited by applicant]
EP 3877301B2 · 2007 [cited by applicant]
EP 2517543A2 · 2012 [cited by applicant]
EP 3033933A1 · 2016 [cited by applicant]
EP 3342274A1 · 2018 [cited by applicant]
EP 3414982 · 2020 [cited by applicant]
WO 2013120062 · 2013 [cited by applicant]
WO 2017149526 · 2017 [cited by applicant]
WO 2020037003A1 · 2020 [cited by applicant]
WO 2022125999A1 · 2022 [cited by applicant]
“International Application Serial No. PCT/US2021/062969, International Search Report mailed Mar. 21, 2022”, 2 pgs. [cited by applicant]
“International Application Serial No. PCT/US2021/062969, Written Opinion mailed Mar. 21, 2022”, 11 pgs. [cited by applicant]
Erfani, S., et al., “Comparison of two data fusion methods for localization of wheeled mobile robot in farm conditions”, Artificial Intelligence in Agriculture vol. 1, (May 15, 2019), 48-55. [cited by applicant]
“U.S. Appl. No. 17/131,224, Response filed Jan. 24, 2023 to Non Final Office Action mailed Oct. 24, 2022”, 14 pgs. [cited by applicant]
“U.S. Appl. No. 17/131,224, Notice of Allowance mailed Feb. 16, 2023”, 8 pgs. [cited by applicant]
“U.S. Appl. No. 17/131,224, Corrected Notice of Allowability mailed Mar. 16, 2023”, 2 pgs. [cited by applicant]
“U.S. Appl. No. 17/131,224, Non Final Office Action mailed Oct. 24, 2022”, 13 pgs. [cited by applicant]
“European Application Serial No. 19850326.0, Response filed Nov. 25, 2022 to Extended European Search Report mailed May 3, 2022”, 15 pgs. [cited by applicant]
“U.S. Appl. No. 16/982,495, Response filed May 13, 2022 to Non Final Office Action mailed Feb. 15, 2022”, 19 pgs. [cited by applicant]
“European Application Serial No. 19850326.0, Extended European Search Report mailed May 3, 2022”, 16 pgs. [cited by applicant]
“U.S. Appl. No. 16/982,495, Notice of Allowance mailed Jun. 9, 2022”, 10 pgs. [cited by applicant]
“U.S. Appl. No. 16/982,495, Corrected Notice of Allowability mailed Aug. 3, 2022”, 2 pgs. [cited by applicant]
U.S. Appl. No. 16/982,495 U.S. Pat. No. 11,470,760, filed Sep. 18, 2020, Comparative Agricultural Obstacle Monitor and Guidance System and Method for Same. [cited by applicant]
U.S. Appl. No. 17/131,224, filed Dec. 22, 2020, Comparative Agricultural Obstacle Monitor and Guidance System and Method for Same. [cited by applicant]
“International Application Serial No. PCT/US2019/046420, International Search Report mailed Oct. 28, 2019”, 3 pgs. [cited by applicant]
“International Application Serial No. PCT/US2019/046420, Written Opinion mailed Oct. 28, 2019”, 26 pgs. [cited by applicant]
“U.S. Appl. No. 16/982,495, Preliminary Amendment filed Sep. 18, 2020”, 23 pgs. [cited by applicant]
“International Application Serial No. PCT/US2019/046420, International Preliminary Report on Patentability mailed Feb. 25, 2021”, 28 pgs. [cited by applicant]
“U.S. Appl. No. 16/982,495, Non-Final Office Action mailed Feb. 15, 2022”, 20 pgs. [cited by applicant]
“International Application Serial No. PCT/US2021/062969, International Preliminary Report on Patentability mailed Jun. 22, 2023”, 13 pgs. [cited by applicant]
“European Application Serial No. 19850326.0, Communication Pursuant to Article 94(3) EPC mailed Jul. 23, 2024”, 8 pgs. [cited by applicant]
“Canadian Application Serial No. 3,201,409, Examiners Rule 86(2) Report mailed Oct. 2, 2024”, 3 pgs. [cited by applicant]
“Canadian Application Serial No. 3,201,409, Response filed Jan. 15, 2025 to Examiners Rule 86(2) Report mailed Oct. 2, 2024”, 17 pgs. [cited by applicant]