US 12,306,610 B2
Job parsing in robot fleet resource configuration
Charles H. Cella, Pembroke, MA (US); Brad Kell, Seattle, WA (US); Teymour S. El-Tahry, Detroit, MI (US); Andrew Cardno, San Diego, CA (US); and Leon Fortin, Jr., Providence, RI (US)
Assigned to STRONG FORCE VCN PORTFOLIO 2019, LLC, Fort Lauderdale, FL (US)
Filed by Strong Force VCN Portfolio 2019, LLC, Fort Lauderdale, FL (US)
Filed on Feb. 28, 2022, as Appl. No. 17/683,097.
Application 17/683,097 is a continuation of application No. PCT/US2021/064233, filed on Dec. 17, 2021.
Claims priority of provisional application 63/185,348, filed on May 6, 2021.
Claims priority of provisional application 63/127,983, filed on Dec. 18, 2020.
Claims priority of application No. 202111029964 (IN), filed on Jul. 3, 2021; and application No. 202111036187 (IN), filed on Aug. 10, 2021.
Prior Publication US 2022/0197306 A1, Jun. 23, 2022
Int. Cl. G05B 19/4099 (2006.01); B25J 9/16 (2006.01); B29C 64/386 (2017.01); B29C 64/393 (2017.01); B33Y 10/00 (2015.01); B33Y 50/00 (2015.01); B33Y 50/02 (2015.01); G02B 3/14 (2006.01); G02B 26/00 (2006.01); G05B 13/02 (2006.01); G05B 13/04 (2006.01); G05B 17/02 (2006.01); G05B 19/402 (2006.01); G05D 1/00 (2006.01); G06F 30/27 (2020.01); G06N 20/00 (2019.01); G06N 20/20 (2019.01); G06Q 10/0631 (2023.01); G06Q 10/0633 (2023.01); G06Q 20/14 (2012.01); G06T 7/70 (2017.01); H04L 9/00 (2022.01); H04L 9/32 (2006.01); H04L 9/40 (2022.01); G06F 113/10 (2020.01); G06Q 10/06 (2023.01); G06Q 10/0831 (2023.01); G06Q 10/0833 (2023.01); G06Q 10/087 (2023.01); G06Q 30/0201 (2023.01)
CPC G06Q 20/14 (2013.01) [B25J 9/163 (2013.01); B25J 9/1653 (2013.01); B25J 9/1661 (2013.01); B25J 9/1671 (2013.01); B25J 9/1682 (2013.01); B25J 9/1697 (2013.01); B29C 64/386 (2017.08); B29C 64/393 (2017.08); B33Y 10/00 (2014.12); B33Y 50/00 (2014.12); B33Y 50/02 (2014.12); G02B 3/14 (2013.01); G02B 26/00 (2013.01); G05B 13/0265 (2013.01); G05B 13/042 (2013.01); G05B 17/02 (2013.01); G05B 19/402 (2013.01); G05B 19/4099 (2013.01); G05D 1/0027 (2013.01); G05D 1/0297 (2013.01); G06F 30/27 (2020.01); G06N 20/00 (2019.01); G06N 20/20 (2019.01); G06Q 10/06311 (2013.01); G06Q 10/0633 (2013.01); G06T 7/70 (2017.01); H04L 9/3239 (2013.01); H04L 9/50 (2022.05); H04L 63/1441 (2013.01); G05B 2219/32015 (2013.01); G05B 2219/40113 (2013.01); G05B 2219/49023 (2013.01); G06F 2113/10 (2020.01); G06Q 10/06 (2013.01); G06Q 10/0631 (2013.01); G06Q 10/063114 (2013.01); G06Q 10/06313 (2013.01); G06Q 10/06316 (2013.01); G06Q 10/0831 (2013.01); G06Q 10/0833 (2013.01); G06Q 10/087 (2013.01); G06Q 30/0201 (2013.01); G06Q 2220/00 (2013.01); G06T 2207/20081 (2013.01)] 22 Claims
OG exemplary drawing
 
1. A robot fleet management platform for configuring robot fleet resources, the platform comprising:
a set of processors configured to execute a set of computer-readable instructions, wherein the set of computer-readable instructions collectively implements:
a job parsing system that applies a set of filters to job content received in association with a job request to identify portions of the job request suitable for robot automation;
a task definition system that establishes a set of robot tasks, wherein:
each of the set of robot tasks defines a type of robot operating unit and a task objective, and
the set of robot tasks is based on the portions of the job request that are suitable for robot automation and meet a first fleet objective of a set of fleet objectives corresponding to the job request;
a fleet configuration proxy service that processes the set of robot tasks and additional job content relating to the job request to produce a fleet resource configuration data structure for the job request that defines a set of task associations and a set of robot adaptation instructions, wherein:
each task association associates at least one robot operating unit of a robot fleet to a respective robot task of the set of robot tasks, and
the set of robot adaptation instructions defines a manner by which one or more robot operating units of the robot fleet are to be adapted to perform respective robot tasks;
a fleet intelligence layer that activates a set of intelligence services to produce at least one recommended robot task and associated contextual information that facilitates robot operating unit selection and task ordering in a workflow of the set of robot tasks;
a job workflow system that generates a workflow that defines an order of performance of the set of robot tasks based on the fleet resource configuration data structure and the set of robot tasks;
a workflow simulation system configured to simulate performance of the job request based on the workflow and a job execution simulation environment, wherein:
the workflow simulation system applies the workflow in the job execution simulation environment,
the job execution simulation environment includes digital models of the robot operating units of the robot fleet and digital models of the set of robot tasks to produce a simulation result, and
the simulation result is used to iteratively redefine at least one of the set of robot tasks, the fleet resource configuration data structure, and the workflow until the simulation result satisfies a second fleet objective of the set of fleet objectives;
a job execution plan generator that, in response to the simulation result satisfying the set of fleet objectives, generates a job execution plan based on the set of robot tasks, the fleet resource configuration data structure, and the workflow; and
a job execution system configured to execute the job execution play by controlling the robot operation units of the robot fleet.