IP Library Granted Patent US 12,651,368
Granted Patent B2
US 12,651,368 · App. 17/755,878 · Granted Jun 9, 2026

Unmanned vehicle navigation, and associated methods, systems, and computer-readable medium

Inventors: Ahmad Y. Al Rashdan (Ammon, ID); Michael L. Wheeler (Greenwood, IN); Roger Lew (Moscow, ID); Dakota Roberson (Shelley, ID); Lloyd M. Griffel (Idaho Falls, ID); Roger Boza (Miami, FL); Michael W. Thompson (Bokeelia, FL)
Assignees: Battelle Energy Alliance, LLC; University of Idaho
G06T7/73G06K7/1413G06K7/1417G06T7/50G06T7/74B64U2101/31G06T2207/10032G06T2207/20081G06T2207/30204
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Quick Facts
Patent No.
US 12,651,368
App. No.
17/755,878
Granted
Jun 9, 2026
Kind
B2
Abstract

Various embodiments relate to unmanned vehicle navigation. A navigation system may include one or more processors configured to communicatively couple with an unmanned vehicle. The one or more processors may be configured to receive an image from the unmanned vehicle and detect a feature within the image. The one or more processors may be further be configured to determine a location of the unmanned vehicle based on the feature and convey one or more commands to the unmanned vehicle based on the location of the unmanned vehicle. Associated methods and computer-readable medium are also disclosed.

Claims (41)

1 . A navigation system, comprising:

one or more processors configured to communicatively couple with an unmanned vehicle, the one or more processors further configured to:

receive an image from the unmanned vehicle;

detect one or more features of a number of features inserted into a non-GPS environment and depicted within the image;

determine a location of the unmanned vehicle based on the one or more features and a number of pixels of the one or more features depicted within the image;

cause, based on the location of the unmanned vehicle, one or more commands to be conveyed, via wireless communication, to the unmanned vehicle to position the unmanned vehicle at a desired location; and

in response to the unmanned vehicle being positioned at the desired location, cause at least one additional command to be conveyed, via wireless communication, to the unmanned vehicle to perform a task while positioned at the desired location.

2 . The navigation system of claim 1 , further comprising a first module including at least one processor of the one or more processors, the first module configured to:

receive the image from the unmanned vehicle;

detect the one or more features within the image;

generate a bounding box around at least one feature of the one or more features; and

decode information stored in the at least one feature.

3 . The navigation system of claim 2 , wherein the first module includes at least one of a deep learning module and a machine learning module configured to receive the image, identify the one or more features, and generate the bounding box around the at least one feature.

4 . The navigation system of claim 2 , wherein the one or more processors are further configured to at least one of crop or resize the bounding box in response to dimensions of the image exceeding a predetermined maximum size.

5 . The navigation system of claim 2 , wherein the one or more processors are further configured to filter the image.

6 . The navigation system of claim 2 , further comprising a second module including at least one processor of the one or more processors, the second module configured to:

receive the at least one feature including the bounding box; and

determine the location of the unmanned vehicle relative to the at least one feature based on a view of the at least one feature.

7 . The navigation system of claim 6 , wherein the second module is further configured to determine a number of view angles and a relative distance between the unmanned vehicle and known location of the one or more features to determine the location of the unmanned vehicle.

8 . The navigation system of claim 6 , further comprising a third module including at least one processor of the one or more processors, the third module configured to:

receive the location of the unmanned vehicle; and

generate the one or more commands to be conveyed, via wireless communication, to the unmanned vehicle based on a difference between the location of the unmanned vehicle and a desired location of the unmanned vehicle.

9 . The navigation system of claim 1 , wherein each feature of the number of features comprise either a quick response (QR) code or a bar code.

10 . The navigation system of claim 1 , further comprising the unmanned vehicle including a camera configured to capture the image.

11 . A method, comprising:

positioning a number of features within a non-GPS environment;

receiving an image from a vehicle;

detecting at least one feature of the number of features within the image;

determining a location of the vehicle based on the at least one feature and a number of pixels of the at least one feature depicted within the image;

conveying, via wireless communication and based on the location of the vehicle, one or more commands to the vehicle to position the vehicle at a desired location; and

conveying, via wireless communication and in response to the vehicle being positioned at the desired location, at least one additional command to the vehicle to perform a task while positioned at the desired location.

12 . The method of claim 11 , further comprising decoding the at least one feature to determine a location of the at least one feature.

13 . The method of claim 12 , wherein determining the location of the vehicle comprises determining the location of the vehicle relative to the location of the at least one feature.

14 . The method of claim 11 , wherein determining the location of the vehicle comprises determining one or more angles between the vehicle and the at least one feature within the image and a relative distance between the vehicle and the location of the at least one feature to determine the location of the vehicle.

15 . The method of claim 11 , further comprising generating a bounding box around the at least one feature in response to detecting the at least one feature.

16 . The method of claim 11 , wherein determining the location comprises determining the location based on two or more features in the image.

17 . The method of claim 11 , wherein determining the location of the vehicle based on the at least one feature comprises comparing known dimensions and a shape of a feature to the at least one feature within the image.

18 . The method of claim 11 , wherein conveying, via wireless communication, the one or more commands comprises conveying one or more of a roll input, a pitch input, a yaw input, and a throttle input to the vehicle based on the location of the vehicle.

19 . The method of claim 11 , wherein detecting the at least one feature within the image comprises detecting the at least one feature via at least one of a brightness, a shape, a size, and an orientation of the at least one feature.

20 . The method of claim 11 , further comprising controlling the vehicle via one or more navigation techniques selected from the group consisting of one or more of:

simultaneous localization and mapping (SLAM), target tracking, and global positioning.

Assignments (1)
CONFIRMATORY LICENSE Recorded Mar 6, 2023
From: BATTELLE ENERGY ALLIANCE IDAHO NATL LAB
To: UNITED STATES DEPARTMENT OF ENERGY
Reel/Frame 062956/0340 →
Continuity (2)
Provisional Application 62934976 · Nov 13, 2019
Related Publication 20220383541A1 · Dec 1, 2022
References Cited (24)
US 9964951B1 · Dunn · 2018 [cited by examiner]
US 11249492B2 · Zamora Esquivel · 2022 [cited by examiner]
US 11656090B2 · Soni · 2023 [cited by examiner]
US 12181892B2 · Kojima · 2024 [cited by examiner]
US 20050125142A1 · Yamane · 2005 [cited by examiner]
US 20130231779A1 · Purkayastha et al. · 2013 [cited by applicant]
US 20160124431A1 · Kelso · 2016 [cited by examiner]
US 20160321530A1 · Troy · 2016 [cited by examiner]
US 20180203467A1 · Zhou · 2018 [cited by examiner]
US 20180301045A1 · Pesik · 2018 [cited by examiner]
US 20180373950A1 · Gong · 2018 [cited by examiner]
US 20190156086A1 · Plummer · 2019 [cited by examiner]
US 20190235531A1 · Liu · 2019 [cited by examiner]
US 20190265721A1 · Troy · 2019 [cited by examiner]
US 20200005832A1 · Ko · 2020 [cited by examiner]
US 20200301445A1 · Jourdan · 2020 [cited by examiner]
US 20200349362A1 · Maloney · 2020 [cited by examiner]
WO 2016187757A1 · 2016 [cited by applicant]
WO 2019092418A1 · 2019 [cited by applicant]
WO 2019174213A1 · 2019 [cited by applicant]
WO 2021091989A1 · 2021 [cited by applicant]
International Search Report for International Application No. PCT/US20/60473, mailed Jun. 29, 2021, 2 pages. [cited by applicant]
International Written Opinion for International Application No. PCT/US20/60473, mailed Jun. 29, 2021, 6 pages. [cited by applicant]
Redmon et al., “YOLOv3: An Incremental Improvement”, arXiv:1804.02767v1 [cs.CV] (Apr. 8, 2018), 6 pages. [cited by applicant]