Deployment system for additive manufacturing robot fleet

Patent metadata

Field	Value
Publication number	US-12589498-B2
Application number	US-202318180015-A
Country	US
Kind code	B2
Filing date	Mar 7, 2023
Priority date	Dec 18, 2020
Publication date	Mar 31, 2026
Grant date	Mar 31, 2026

Abstract

A robotic fleet platform includes a fleet resources data store with a fleet resource inventory indicating additive manufacturing systems that can be provisioned with a set of fleet resources. The fleet resource inventory indicates 3D printing requirements, printing instructions, and a status of each additive manufacturing system. Provisioning rules are accessible to an intelligence layer to ensure compliance. The platform receives a request for a robotic fleet to perform a job and determines a job definition data structure defining tasks. The platform determines a robotic fleet configuration data structure that assigns additive manufacturing systems to one or more of the tasks. The platform determines a respective provisioning configuration for each of the additive manufacturing systems. The platform provisions each additive manufacturing system based on the respective provisioning configuration and the provisioning rules. The platform deploys the robotic fleet based on the robotic fleet configuration data structure to perform the job.

First claim

The invention claimed is:

1 . A robotic fleet platform for configuring a robot fleet with additive manufacturing capabilities, the robotic fleet platform comprising:
a non-transitory computer-readable storage system that stores:
a fleet resources data store that maintains a fleet resource inventory that indicates a plurality of additive manufacturing systems that can be provisioned with a set of fleet resources, wherein, for each respective additive manufacturing system, the fleet resource inventory indicates:
a set of three-dimensional (3D) printing requirements,
printing instructions that define configuring an on-demand production system for 3D printing, and
a status of the respective additive manufacturing system; and

a set of provisioning rules that are accessible to an intelligence layer to ensure that provisioned additive manufacturing systems comply with the set of provisioning rules; and
a set of one or more processors that execute a set of computer-readable instructions, wherein the set of one or more processors collectively:
receives a request for the robotic fleet to perform a job;
determines a job definition data structure based on the request, wherein the job definition data structure defines 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 set of additive manufacturing systems selected from the fleet resource inventory to one or more tasks of the set of tasks defined in the job definition data structure;
determines a respective provisioning configuration for each respective additive manufacturing system of the set of additive manufacturing systems of the one or more additive manufacturing systems based on:
the one or more tasks to which the respective additive manufacturing system is assigned,
the set of 3D printing requirements,
the printing instructions, and
the status of the respective additive manufacturing system;

for each respective additive manufacturing system of the one or more additive manufacturing systems, provisions the respective additive manufacturing system based on the respective provisioning configuration and the set of provisioning rules;
deploys at least one about operating unit of the robotic fleet to specified geolocations to specified geolocations based on the robotic fleet configuration data structure to perform the job; and
deploys at least one manufacturing system of the plurality of additive manufacturing systems to the specified geolocations based on the robotic fleet configuration data structure to perform the job.

2 . The platform of claim 1 , wherein the provisioning of at least one respective additive manufacturing system includes provisioning a 3D printing capable robot.

3 . The platform of claim 1 , wherein the respective provisioning configuration for each respective additive manufacturing system includes a set of 3D printing instructions for at least one of: a job-specific end effector or an adaptor based on a context of the one or more tasks to which the respective additive manufacturing system is assigned.

4 . The platform of claim 1 , wherein the robotic fleet configuration data structure assigns control of the at least one manufacturing system to the at least one robot operating unit.

5 . The platform of claim 1 , wherein the determination of the respective provisioning configuration for each respective additive manufacturing system includes use of an artificial intelligence system to automate design for 3D printing of one or more robotic accessories.

6 . The platform of claim 5 , wherein the artificial intelligence system automates design for 3D printing based on at least one of: a contextual task recognition or an automated shape recognition capability.

7 . The platform of claim 1 , wherein the deployment of the at least one robot operating unit of the robotic fleet includes a deployment of a 3D printing robot to a smart container for remote on-demand additive manufacturing.

8 . The platform of claim 1 , wherein the determination of the respective provisioning configuration for each respective additive manufacturing system includes a configuration of a 3D printing system to receive a tokenized instance of a set of 3D printing instructions associated with a corresponding token on a distributed ledger.

9 . The platform of claim 1 , wherein the deployment of the at least one robot operating unit of the robotic fleet includes a deployment of each respective additive manufacturing system as a 3D printing resource shared among a plurality of tasks.

10 . A computer-implemented method of configuring a robotic fleet with additive manufacturing capabilities, the method comprising:
receiving a request for the robotic fleet to perform a job; determining a job definition data structure based on the request, wherein the job definition data structure defines a set of tasks that are to be performed in performance of the job; determining a robotic fleet configuration data structure corresponding to the job based on: the set of tasks and a fleet resource inventory that indicates a plurality of additive manufacturing systems that can be provisioned with a set of fleet resources, wherein:
for each respective additive manufacturing system, the fleet resource inventory indicates:
a set of three-dimensional (3D) printing requirements,
printing instructions that define configuring an on-demand production system for 3D printing, and
a status of the respective additive manufacturing system, and

the robotic fleet configuration data structure assigns a set of additive manufacturing systems selected from the fleet resource inventory to one or more tasks of the set of tasks defined in the job definition data structure;
determining a respective provisioning configuration for each respective additive manufacturing system of the one or more additive manufacturing systems based on:
the one or more tasks to which the respective additive manufacturing system is assigned,
the set of 3D printing requirements,
the printing instructions, and
the status of the respective additive manufacturing system;
for each repsective additive manufacturing system of the one or more additive manufacturing systems, provisioning the respective additive manufacturing system based on the respective provisioning configuration and a set of provisioning rules that are accessible to an intelligence layer to ensure that provisioned additive manufacturing systems comply with the set of provisioning rules; deploying at least one robot operating unit of the robotic fleet to at least one specified geolocation based on the robotic fleet configuration data structure to perform the job; and deploying at least additive manufacturing system of the plurality of additive manufacturing systems to the at least one specified geolocation based on the robotic fleet configuration data structure to perform the job.

11 . The method of claim 10 , wherein the provisioning of at least one respective additive manufacturing system includes provisioning a 3D printing capable robot.

12 . The method of claim 10 , wherein the respective provisioning configuration for each respective additive manufacturing system includes a set of 3D printing instructions for at least one of: a job-specific end effector or an adaptor based on a context of the one or more tasks to which the respective additive manufacturing system is assigned.

13 . The method of claim 10 , wherein the determining the respective provision

Assignees

• Strong Force Vcn Portfolio 2019 Llc

Inventors

• Cella Charles Howard
• El-Tahry Teymour S
• Fortin Jr Leon

Classifications

• G05B19/418

  Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM] · CPC title

• G06Q10/0631

  Resource planning, allocation, distributing or scheduling for enterprises or organisations · CPC title

• G06Q2220/00

  Business processing using cryptography · CPC title

• B29C64/393

  for controlling or regulating additive manufacturing processes · CPC title

• B33Y50/02

  for controlling or regulating additive manufacturing processes · CPC title