Robotic fleet configuration method for additive manufacturing systems

Patent metadata

Field	Value
Publication number	US-12498680-B2
Application number	US-202318179948-A
Country	US
Kind code	B2
Filing date	Mar 7, 2023
Priority date	Dec 18, 2020
Publication date	Dec 16, 2025
Grant date	Dec 16, 2025

Abstract

A method of configuring robot fleets with additive manufacturing capabilities includes receiving a request for a robotic fleet to perform a job and determining a job definition data structure based on the request. The job definition data structure defines a set of tasks to be performed in furtherance of the job. The method includes determining a provisioning configuration for each additive manufacturing system based on the task to which the additive manufacturing system is assigned, the set of 3D printing requirements, the printing instructions, and the status of the additive manufacturing system. The method includes provisioning the additive manufacturing system based on the provisioning configuration and a set of additive manufacturing system provisioning rules that are accessible to an intelligence layer to ensure that provisioned systems comply with the provisioning rules. The method includes deploying the robotic fleet based on the robotic fleet configuration data structure to perform the job.

First claim

The invention claimed is:

1 . A method of configuring robot fleets with additive manufacturing capabilities, the method comprising:
receiving a request for a robotic fleet to perform a job; determining a job definition data structure based on the request, the job definition data structure defining 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, and for each respective additive manufacturing system, a set of 3D printing requirements, printing instructions that define configuring an on-demand production system for 3D printing, and a status of the additive manufacturing system, wherein the robotic fleet configuration data structure assigns one or more additive manufacturing systems selected from the fleet resource inventory to one or more of the set of tasks defined in the job definition data structure; determining a respective provisioning configuration for each respective additive manufacturing system based on the respective task to which the additive manufacturing system is assigned, the set of 3D printing requirements, the printing instructions, and the respective status of the additive manufacturing system; provisioning the respective additive manufacturing system based on the respective provisioning configuration and a set of additive manufacturing system provisioning rules that are accessible to an intelligence layer to ensure that provisioned additive manufacturing systems comply with the provisioning rules; and deploying the robotic fleet based on the robotic fleet configuration data structure to perform the job.

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

3 . The method 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 task to which the additive manufacturing system is assigned.

4 . The method of claim 1 , wherein the robotic fleet configuration data structure assigns control of at least one transportable 3D printing additive manufacturing system to at least one robot operating unit.

5 . The method of claim 1 , wherein determining the robotic fleet configuration data structure is further based on availability and job site locality of 3D printing resources.

6 . The method of claim 5 , wherein at least one of the availability or job site locality of the 3D printing resource is identified by a logistics system.

7 . The method of claim 1 , wherein determining the robotic fleet configuration data structure includes assignment of at least one additive manufacturing system indicated in the fleet resource inventory based on proximity to a job site for the requested job.

8 . The method of claim 1 , wherein determining a 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.

9 . The method of claim 8 , wherein the artificial intelligence system automates design for 3D printing based on contextual task recognition.

10 . The method of claim 8 , wherein the artificial intelligence system automates design for 3D printing based on automated shape recognition capabilities.

11 . The method of claim 8 , wherein provisioning the respective additive manufacturing system includes provisioning a 3D printing control capability to produce an end effector based on a visual and sensed analysis of an object for manipulation of which the end effector is to be 3D printed.

12 . The method of claim 1 , wherein deploying the robotic fleet includes use of a fleet configuration scheduling resource for allocation of the respective additive manufacturing system to perform the job.

13 . The method of claim 1 , wherein deploying the robotic fleet includes deploying a 3D printing robot to a smart container for remote, on-demand additive manufacturing.

14 . The method of claim 1 , wherein determining a respective provisioning configuration for each respective additive manufacturing system is further based on one or more keywords of the job definition data structure that are indicative of an operating condition for the respective additive manufacturing system.

15 . The method of claim 1 , wherein deploying the robotic fleet includes deploying a set of autonomous 3D printing additive manufacturing system to points of service work indicated in the job definition data structure.

16 . The method of claim 1 , wherein determining a respective provisioning configuration for each respective additive manufacturing system includes configuring 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.

17 . The method of claim 1 , wherein deploying the robotic fleet includes deploying the respective additive manufacturing system as a 3D printing resource shared among a plurality of tasks.

18 . The method of claim 1 , wherein provisioning the respective additive manufacturing system includes interacting with at least one of a fleet operating system, a fleet configuration system, a fleet resource scheduling system, and a fleet utilization system.

19 . The method of claim 18 , wherein interacting includes ensuring that the provisioning rules are followed.

20 . The method of claim 1 , wherein the provisioning rules are defined in a governance standards library and an intelligence service ensures that the provisioned resources comply with the provisioning rules.

Assignees

• Strong Force Vcn Portfolio 2019 Llc

Inventors

• Cella Charles Howard
• Kell Brad
• El-Tahry Teymour S
• Cardno Andrew
• Fortin Jr Leon

Classifications

• G05D1/69

  Coordinated control of the position or course of two or more vehicles · CPC title

• G06Q2220/00

  Business processing using cryptography · CPC title

• G06Q10/087

  Inventory or stock management, e.g. order filling, procurement or balancing against orders · CPC title

• G06Q10/06316

  Sequencing of tasks or work · CPC title

• G06F9/5055

  considering software capabilities, i.e. software resources associated or available to the machine · CPC title