IP Library › Granted Patent US 12,220,826
Granted Patent B2
US 12,220,826 · App. 18/401,931 · Granted Feb 11, 2025

Balancing compute for robotic operations

Inventors: William Wilder (Austin, TX); Spencer Voiss (Austin, TX)
Assignee: WILDER SYSTEMS INC.
B25J9/1697B25J9/0081B25J9/161B25J9/163B25J9/1661B25J9/1664B25J9/1666B25J9/1671B25J9/1679B25J13/006B64F5/40G06F16/22G06F18/23G06T7/70G05B2219/33002G05B2219/45066G06T2207/20081
View Patent ↗
Loading inventors, assignments & file history…
Monitor This Case
Get email alerts when status or documents change.
Order Certified Copies
Most orders are placed with the USPTO same day — all within 24 business hours.
Order via The Patent Place →
Pre-filled with this patent's details
Quick Facts
Patent No.
US 12,220,826
App. No.
18/401,931
Filed
Jan 2, 2024
Granted
Feb 11, 2025
Kind
B2
Examiner
JOS, BASIL T
Art Unit
3658
USPC
700/248
Abstract

The present disclosure relates to a multi-tiered computing environment for balancing compute resources in support of robot operations. In an example, a robot is tasked with performing an operation associated with an airplane having an airplane model. To do so, the robot may need another operation that is computationally complex to be performed. An on-premises server can execute a process that corresponds to this computationally-complex operation based on sensor data of the robot and can output the resulting data to the robot. Next, the robot can use the resulting data to execute another process corresponding to its operation and can indicate performance of this operation to the on-premises network. The on-premises network can send the indication about the operation performance to a top-tier server that is also associated with the airplane model.

Claims (62)

1. A system comprising:

a robot located on a premises and configured to perform operations on an airplane;

a first server located on the premises and communicatively coupled with the robot; and

a second server located remotely from the premises and communicatively coupled with the first server,

wherein the robot is configured to:

generate, using one or more sensors, first data for an operation to be performed on at least a portion of the airplane;

send the first data to the first server;

receive, from the first server based on the first data, second data associated with the operation;

execute locally, based on the second data, a second process to perform the operation; and

send, to the first server, third data associated with performance of the operation;

wherein the first server is configured to:

receive the first data and the third data from the robot;

execute locally, based on the first data, a first process associated with the operation to generate the second data;

send the second data to the robot; and

send, to the second server, fourth data that corresponds to the third data and that is associated with the performance of the operation, and wherein the second server is configured to:

store the fourth data in association with a tail number of the airplane.

2. The system of claim 1 , wherein the fourth data includes sensor data generated by the one or more sensors of the robot.

3. The system of claim 1 , wherein the fourth data includes multi-dimensional data generated based on sensor data of the one or more sensors of the robot.

4. The system of claim 1 , wherein the fourth data includes image data generated based on sensor data of the one or more sensors of the robot.

5. The system of claim 1 , wherein the portion includes an airplane part of the airplane, and wherein the fourth data includes multi-dimensional data generated based on sensor data of the one or more sensors of the robot, the tail number of the airplane, and an identifier of the airplane part.

6. The system of claim 1 , wherein the portion includes an airplane part of the airplane, and wherein the fourth data includes information about the operation performed, the tail number of the airplane, and an identifier of the airplane part.

7. The system of claim 6 , wherein the information indicates a timing of when the operation was performed and the premises where the operation was performed.

8. The system of claim 7 , wherein the information further includes an identifier of the robot.

9. The system of claim 1 , wherein the first server is further configured to store at least one of status data indicating a status of the robot or capability data indicating a capability of the robot but not operational data indicating operations performed by the robot on one or more airplanes.

10. The system of claim 9 , wherein the second server is further configured to store the operational data.

11. A server located on a premises and comprising:

one or more processors; and

one or more memory storing instructions that, upon execution by the one or more processors, configure the server to:

receive, from a robot located on the premises, first data associated with an operation to be performed on at least a portion of an airplane, the first data generated by one or more sensors of the robot, the server associated with an airplane model, and the airplane being of the airplane model;

execute locally, based on the first data, a first process associated with the operation to generate second data;

send the second data to the robot, the second data causing the robot to execute locally a second process to perform the operation on at least the portion of the airplane;

receive, from the robot, third data indicating performance of the operation; and

send, to another server associated with the airplane model, fourth data that corresponds to the third data and that is associated with the performance of the operation.

12. The server of claim 11 , wherein the one or more memory store further instructions that, upon execution by the one or more processors, configure the server to:

receive, from a device, sensor data generated by the device based on a scan or non-destructive inspection (NDI) of at least the portion of the airplane; and

send, to the other server, at least one of an outcome of processing, by the server, of the sensor data or the sensor data such that the other server at least one of the outcome or the sensor data in association with a tail number of the airplane.

13. The server of claim 11 , wherein the one or more memory store further instructions that, upon execution by the one or more processors, configure the server to:

receive operational instructions associated with performing the operation, wherein the second data is generated based on the operational instructions.

14. The server of claim 11 , wherein the one or more memory store further instructions that, upon execution by the one or more processors, configure the server to:

receive, from the other server, information about at least the portion of the airplane the operation, wherein the second data is generated based on the information.

15. The server of claim 11 , wherein the one or more memory store further instructions that, upon execution by the one or more processors, configure the server to:

receive, from the other server after the fourth data is sent, information about at least the portion of the airplane;

generate, based at least in part on the information, sixth data associated with performing the operation or another operation on at least the portion of the airplane; and

send the sixth data to the robot.

16. A method implemented on a server that is located on a premises, the method comprising:

receiving, from a robot located on the premises, first data associated with an operation to be performed on at least a portion of an airplane, the first data generated by one or more sensors of the robot, the server associated with an airplane model, and the airplane being of the airplane model;

executing locally, based on the first data, a first process associated with the operation to generate second data;

sending the second data to the robot, the second data causing the robot to execute locally a second process to perform the operation on at least the portion of the airplane;

receiving, from the robot, third data indicating performance of the operation; and

sending, to another server associated with the airplane model, fourth data that corresponds to the third data and that is associated with the performance of the operation.

17. The method of claim 16 , wherein the first data includes sensor data generated by the one or more sensors, and wherein executing the first process comprises inputting the sensor data to an artificial intelligence model that is hosted on the server and that outputs the second data.

18. The method of claim 16 , further comprising:

receiving, from the robot, a request indicating the operation and a tail number of airplane.

19. The method of claim 16 , wherein the portion of the airplane includes an airplane part, and wherein the method further comprises:

receiving, from a device located at a jig, an indication of a loading of the airplane part to the jig;

receiving, from the robot, a request indicating the operation;

determining that the request was received after the indication is received; and

determining that the request is permitted based on the request being received after the indication is received.

20. The method of claim 16 , further comprising:

receiving, from the other server after the fourth data is sent, information about at least the portion of the airplane;

generating, based at least in part on the information, sixth data associated with performing the operation or another operation on at least the portion of the airplane; and

sending the sixth data to the robot.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Oct 18, 2024
From: WILDER, WILLIAM; VOISS, SPENCER
To: WILDER SYSTEMS INC.
Reel/Frame 068934/0908 →
Continuity (8)
Continuation 18447175 · Aug 9, 2023
Provisional Application 63481576 · Jan 25, 2023
Provisional Application 63481563 · Jan 25, 2023
Provisional Application 63377149 · Sep 26, 2022
Provisional Application 63398203 · Aug 15, 2022
Provisional Application 63398202 · Aug 15, 2022
Provisional Application 63396938 · Aug 10, 2022
Related Publication 20240149465A1 · May 9, 2024
References Cited (54)
US 11113567B1 · Durand et al. · 2021 [cited by applicant]
US 11331799B1 · Shafer · 2022 [cited by applicant]
US 11341596B2 · Lee et al. · 2022 [cited by applicant]
US 11407111B2 · Zhang et al. · 2022 [cited by applicant]
US 11553969B1 · Lang et al. · 2023 [cited by applicant]
US 11555903B1 · Kroeger · 2023 [cited by applicant]
US 11865696B1 · Wilder et al. · 2024 [cited by applicant]
US 11897145B1 · Oridate et al. · 2024 [cited by applicant]
US 11911921B1 · Oridate et al. · 2024 [cited by applicant]
US 11931910B2 · Oridate et al. · 2024 [cited by applicant]
US 11995056B2 · Magpantay et al. · 2024 [cited by applicant]
US 12036684B2 · Oridate et al. · 2024 [cited by applicant]
US 20020169586A1 · Rankin, II et al. · 2002 [cited by applicant]
US 20030149502A1 · Rebello et al. · 2003 [cited by applicant]
US 20040039465A1 · Boyer et al. · 2004 [cited by applicant]
US 20140184786A1 · Georgeson et al. · 2014 [cited by applicant]
US 20140305217A1 · Tapia et al. · 2014 [cited by applicant]
US 20150314888A1 · Reid et al. · 2015 [cited by applicant]
US 20170182666A1 · Szarski et al. · 2017 [cited by applicant]
US 20180060364A1 · Zengerle et al. · 2018 [cited by applicant]
US 20190392595A1 · Uhlenbrock et al. · 2019 [cited by applicant]
US 20200134860A1 · Haven et al. · 2020 [cited by applicant]
US 20200164531A1 · Wagner et al. · 2020 [cited by applicant]
US 20200311616A1 · Rajkumar et al. · 2020 [cited by applicant]
US 20200383734A1 · Dahdouh · 2020 [cited by applicant]
US 20210138600A1 · Sato et al. · 2021 [cited by applicant]
US 20210229835A1 · Carberry et al. · 2021 [cited by applicant]
US 20210248289A1 · Fasano · 2021 [cited by applicant]
US 20210356572A1 · Kadambi et al. · 2021 [cited by applicant]
US 20210370509A1 · Pivac · 2021 [cited by applicant]
US 20220066456A1 · Ebrahimi Afrouzi et al. · 2022 [cited by applicant]
US 20220187841A1 · Ebrahimi Afrouzi et al. · 2022 [cited by applicant]
US 20220193894A1 · Barry et al. · 2022 [cited by applicant]
US 20220197306A1 · Cella et al. · 2022 [cited by applicant]
US 20220395985A1 · Maeda et al. · 2022 [cited by applicant]
US 20230108488A1 · Humayun et al. · 2023 [cited by applicant]
US 20230109541A1 · Ando · 2023 [cited by applicant]
US 20230114137A1 · Wu et al. · 2023 [cited by applicant]
US 20230124599A1 · Fan · 2023 [cited by applicant]
US 20230131458A1 · Isonni et al. · 2023 [cited by applicant]
US 20230158716A1 · Nishimuta et al. · 2023 [cited by applicant]
US 20230347509A1 · Terasawa · 2023 [cited by applicant]
CN 112907672B · 2021 [cited by applicant]
CN 114330030A · 2022 [cited by applicant]
WO 2022070186A1 · 2022 [cited by applicant]
WO WO2022186777A1 · 2022 [cited by examiner]
PCT/US2024/012997 , “International Search Report and Written Opinion”, Apr. 3, 2024, 14 pages. [cited by applicant]
PCT/US2024/012988 , “International Search Report and the Written Opinion”, Apr. 19, 2024, 8 pages. [cited by applicant]
U.S. Appl. No. 18/447,175 , “Notice of Allowance”, filed Nov. 13, 2023, 11 pages. [cited by applicant]
U.S. Appl. No. 18/447,230 , “Non-Final Office Action”, filed Oct. 30, 2023, 24 pages. [cited by applicant]
U.S. Appl. No. 18/447,244 , “Notice of Allowance”, filed Nov. 9, 2023, 11 pages. [cited by applicant]
U.S. Appl. No. 18/447,295 , “Notice of Allowance”, filed Oct. 16, 2023, 9 pages. [cited by applicant]
U.S. Appl. No. 18/447,300 , “Non-Final Office Action”, filed Oct. 26, 2023, 35 pages. [cited by applicant]
Oridate , “Velocity-Based Robot Motion Planner for Under-Constrained Trajectories with Part-Specific Geometric Variances”, The University of Texas, Dec. 2021, 178 pages. [cited by applicant]
Cited By (2)
US 12,390,937 US 12,447,626