Global optimization of networks of locally fitted objects

• US 10,628,533 B2
• Filed: 06/19/2017
• Issued: 04/21/2020
• Est. Priority Date: 06/27/2016
• Status: Active Grant

• Current Assignee: Faro Technologies Incorporated
• Original Assignee: Faro Technologies Incorporated
• Inventors: Tonn, Christian, Klawitter, Daniel, Bringmann, Oliver
• Primary Examiner(s): Phan, Thai Q
• Application Number:
  US15/626,380
• Publication Number:
  US 20170371982A1
• Time in Patent Office: 1,037 Days
• Field of Search: 703 1, 703 22, 703 2, 706 15, 706 16
• CPC Class Codes: G06F 2113/14 Pipes
• CPC Class Codes: G06F 30/00 Computer-aided design [CAD]
• CPC Class Codes: G06F 30/18 Network design, e.g. design based on topological or interconnect aspects of utility systems, piping, heating ventilation air conditioning [HVAC] or cabling circuit design at the physical level G06F30/39; network planning tools for wireless communication networks H04W16/18
• CPC Class Codes: G06F 30/20 Design optimisation, verification or simulation optimisation, verification or simulation of circuit designs G06F30/30
• CPC Class Codes: G06F 30/333 Design for testability [DFT], e.g. scan chain or built-in self-test [BIST]

Abstract

Aspects of the invention include global optimization of networks of locally fitted objects. An electronic representation of a network of intelligent objects is received. The network includes a plurality of intelligent objects and a plurality of gaps greater than a threshold between at least three of the intelligent objects. An aligned model of the network is created where all gaps in the aligned model of the network are less than the threshold. The creating includes optimizing a first plurality of the intelligent objects towards an axis of a second plurality of intelligent objects, and aligning the second plurality of intelligent objects towards the first plurality of intelligent objects. The optimizing and aligning are iteratively performed until a stopping condition is met. The aligned model of the network is output.

Claims

1. A system, comprising

a processing device;

and

a memory device in communication with the processing device, the memory device storing instructions that when executed by the processing device result in;

receiving an electronic representation of a network of intelligent objects, the network including a plurality of intelligent objects and a plurality of gaps greater than a threshold between at least three of the intelligent objects;

creating an aligned model of the network based at least in part on quaternion calculus, wherein all gaps in the aligned model of the network are less than the threshold, the creating comprising;

optimizing a first plurality of the intelligent objects towards an axis of a second plurality of intelligent objects, wherein displacements between the intelligent objects are represented as dual unit quaternions and the optimizing comprises adding dual vectors built based at least in part on the dual unit quaternions;

aligning the second plurality of intelligent objects towards the first plurality of intelligent objects, wherein the aligning comprises normalizing results of the adding dual vectors; and

continuing to iteratively perform the optimizing and the aligning until a stopping condition is met; and

outputting the aligned model of the network.

2. The system of claim 1, wherein the electronic representation is created by a three-dimensional (3D) metrology device.

3. The system of claim 2, wherein the 3D metrology device is a laser scanner.

4. The system of claim 1, wherein the electronic representation includes a point cloud model.

5. The system of claim 1, wherein the network of intelligent objects is a piping system.

6. The system of claim 1, wherein the creating is further based at least in part on a minimal displacement approach that minimizes movement of the first and second plurality of intelligent objects.

7. The system of claim 1, wherein an amount of movement of the first and second plurality of intelligent objects is limited by a movement threshold in each iteration of the optimizing and aligning.

8. The system of claim 1, wherein a size of an intelligent object is altered by the creating.

9. The system of claim 1, wherein a position of an intelligent object is fixed during the creating.

10. The system of claim 1, wherein the stopping condition includes a maximum deviation error at connection points between the first and second plurality of intelligent objects.

11. The system of claim 1, wherein the stopping condition includes a number of iterations of the optimizing and the aligning.

12. The system of claim 1, wherein the first and second plurality of intelligent objects are contiguous in the aligned model.

13. The system of claim 1, wherein the aligned model is output to a design package for performing isometrics and simulations.

14. A method comprising:

receiving an electronic representation of a network of intelligent objects, the network including a plurality of intelligent objects and a plurality of gaps greater than a threshold between at least three of the intelligent objects;

creating an aligned model of the network based at least in part on quaternion calculus, wherein all gaps in the aligned model of the network are less than the threshold, the creating comprising;

optimizing a first plurality of the intelligent objects towards an axis of a second plurality of intelligent objects, wherein displacements between the intelligent objects are represented as dual unit quaternions and the optimizing comprises adding dual vectors built based at least in part on the dual unit quaternions;

aligning the second plurality of intelligent objects towards the first plurality of intelligent objects, wherein the aligning comprises normalizing results of the adding dual vectors; and

continuing to iteratively perform the optimizing and the aligning until a stopping condition is met; and

outputting the aligned model of the network.

15. The method of claim 14, wherein the network of intelligent objects is a piping system.

16. The method of claim 14, wherein the creating is further based at least in part on a minimal displacement approach that minimizes movement of the first and second plurality of intelligent objects.

17. The method of claim 14, wherein an amount of movement of the first and second plurality of intelligent objects is limited by a movement threshold in each iteration of the optimizing and aligning.

18. The method of claim 14, wherein at least one of:

a size of an intelligent object is altered by the creating; and

a position of an intelligent object is fixed during the creating.

19. The method of claim 14, wherein the stopping condition includes at least one of:

a maximum deviation error at connection points between the first and second plurality of intelligent objects; and

a number of iterations of the optimizing and the aligning.

20. The method of claim 14, wherein the first and second plurality of intelligent objects are contiguous in the aligned model.

21. A computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor to cause the processor to perform operations comprising:

receiving an electronic representation of a network of intelligent objects, the network including a plurality of intelligent objects and a plurality of gaps greater than a threshold between at least three of the intelligent objects;

creating an aligned model of the network based at least in part on quaternion calculus, wherein all gaps in the aligned model of the network are less than the threshold, the creating comprising;

optimizing a first plurality of the intelligent objects towards an axis of a second plurality of intelligent objects, wherein displacements between the intelligent objects are represented as dual unit quaternions and the optimizing comprises adding dual vectors built based at least in part on the dual unit quaternions;

aligning the second plurality of intelligent objects towards the first plurality of intelligent objects, wherein the aligning comprises normalizing results of the adding dual vectors; and

continuing to iteratively perform the optimizing and the aligning until a stopping condition is met; and

outputting the aligned model of the network.