US 12,299,656 B2
Machine-learned robot fleet management for value chain networks
Charles H. Cella, Pembroke, MA (US); Teymour S. El-Tahry, Detroit, MI (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 Jun. 12, 2023, as Appl. No. 18/333,369.
Application 18/333,369 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 2023/0339108 A1, Oct. 26, 2023
Int. Cl. G06Q 10/00 (2023.01); 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); G05B 19/4099 (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)] 20 Claims
OG exemplary drawing
 
1. A system, comprising:
a computer-readable storage system that stores:
a fleet resources data store that maintains a fleet resource inventory that indicates:
a plurality of fleet resources that can be assigned to perform at least one task in a set of tasks, and
for each respective fleet resource of the plurality of fleet resources, a set of features of the respective fleet resource and a status of the respective fleet resource; and
a set of task definitions that is accessible to an intelligence layer to facilitate improving task definition based on feedback from task-specific outcomes; and
a set of processors that executes a set of computer-readable instructions, wherein the set of processors collectively:
receives a job request for a robotic fleet to perform a job, wherein:
the robotic fleet includes a set of robot operating units,
at least one robot of the set of robot operating units includes a control interface module and a physical interface module, and
the control interface module controls the physical interface module;
determines a job definition data structure based on the job request, wherein the job definition data structure indicates a set of tasks that are to be performed in performance of the job;
determines a robotic fleet configuration data structure corresponding to the job based on the set of tasks and the fleet resource inventory, wherein the robotic fleet configuration data structure assigns a plurality of resources selected from the fleet resource inventory to the set of tasks indicated in the job definition data structure based on the set of task definitions, the respective set of features of each fleet resource, and the respective status of each fleet resource;
deploys the robotic fleet to perform the job, wherein performing the job includes performing at least one task by at least one resource of the plurality of resources assigned to perform the at least one task;
applies an outcome of performing the at least one task by at least one resource of the plurality of resources assigned to perform the at least one task to a machine learning system of the intelligence layer that facilitates improving, based on the outcome, the set of task definitions; and
updates the set of task definitions based on a result of applying the outcome to the machine learning system,
wherein:
the set of task definitions indicates a type of end effector of at least one robot operating unit of the set of robot operating units,
updating the set of task definitions includes communicating with the control interface module to update the type of end effector of the at least one robot operating unit of the set of robot operating units, and
updating the type of end effector includes controlling the physical interface module, by the control interface module, to update the type of end effector of the at least one robot operating unit of the set of robot operating units.