Autonomous welding robots

1. A computer-implemented method of generating instructions for a welding robot, the computer-implemented method comprising:

identifying an expected position of a candidate seam on a part to be welded based on a Computer Aided Design (CAD) model of the part;

scanning a workspace containing the part to produce a representation of the part;

identifying the candidate seam on the part based on the representation of the part and the expected position of the candidate seam;

determining an actual position of the candidate seam; and

generating welding instructions for the welding robot based at least in part on the actual position of the candidate seam.

2. The computer-implemented method of claim 1 , wherein determining the actual position of the candidate seam further comprises:

updating the expected position of the candidate seam based at least in part on the representation of the part.

3. The computer-implemented method of claim 1 , wherein determining the actual position of the candidate seam further comprises:

determining a tolerance for the expected position of the candidate seam based at least in part on the representation of the part; and

refining the expected position of the candidate seam based at least in part on the tolerance.

4. The computer-implemented method of claim 1 , wherein identifying the expected position of the candidate seam further comprises matching a representation of a component in the CAD model to the component on the part to be welded.

5. The computer-implemented method of claim 1 , wherein the CAD model includes an annotation of the candidate seam.

6. The computer-implemented method of claim 1 , wherein identifying the candidate seam further comprises:

identifying a plurality of points on the part to be welded based on the representation of the part, at least some points from the plurality of points forming a seam.

7. The computer-implemented method of claim 1 , wherein identifying the candidate seam further comprises:

identifying a plurality of points on the part to be welded based on the representation of the part, at least some points from the plurality of points forming a seam;

verifying whether the candidate seam is a seam; and

identifying, using a neural network, the candidate seam as a type of seam.

8. The computer-implemented method of claim 7 , wherein verifying whether the candidate seam is a seam further comprises:

analyzing at least a subset of image data, the subset of the image data including a plurality of images of the part, each image from the plurality of images capturing a portion of the part from a different angle;

for each image from the plurality of images:

determining a confidence value that the candidate seam is an actual seam; and

verifying, based on confidence values for each image from the plurality of images, that the candidate seam is the seam.

9. A computer-implemented method of generating welding instructions for a welding robot, the computer-implemented method comprising:

obtaining, via a sensor, image data of a workspace that includes a part to be welded;

identifying a plurality of points on the part to be welded based on the image data;

identifying a candidate seam on the part to be welded from the plurality of points; and

generating welding instructions for the welding robot based at least in part on the identification of the candidate seam.

10. The computer-implemented method of claim 9 , wherein identifying the candidate seam further includes:

localizing the candidate seam relative to the part based on the image data.

11. The computer-implemented method of claim 9 , wherein identifying the candidate seam further includes identifying a subset of points within the plurality of points to form the candidate seam.

12. The computer-implemented method of claim 9 , wherein the welding instructions include a welding path for the welding robot to weld the part.

13. The computer-implemented method of claim 9 , further comprising:

classifying, using a neural network, at least one object included in the workspace as at least one of a clamp or a fixture based on the image data.

14. The computer-implemented method of claim 13 , wherein classifying the at least one object further comprises:

performing pixel-wise classification on the image data.

15. The computer-implemented method of claim 14 , further comprising:

identifying a first portion in at least one image included in the image data that includes a representation of the at least one object based on the pixel-wise classification; and

identifying another candidate seam from a second portion in the at least one image.

16. The computer-implemented method of claim 9 , further comprising:

identifying the candidate seam as belonging to a group of a type of seams consisting of a butt joint, a corner joint, an edge joint, a lap joint, and a tee joint,

the welding instructions generated based at least in part on the type of seam.

17. The computer-implemented method of claim 9 , wherein identifying the candidate seam further comprises:

verifying whether the candidate seam is a seam; and

identifying, using a neural network, the candidate seam as a type of seam.

18. The computer-implemented method of claim 17 , wherein verifying whether the candidate seam is a seam further comprises:

analyzing at least a subset of the image data, the subset of the image data including a plurality of images of the part, each image from the plurality of images capturing a portion of the part from a different angle;

for each image from the plurality of images:

determining a confidence value that the candidate seam is an actual seam; and

verifying, based on confidence values for each image from the plurality of images, that the candidate seam is the seam.

19. The computer-implemented method of claim 9 , wherein identifying the candidate seam further comprises:

verifying whether the candidate seam is a seam;

identifying, using a neural network, the candidate seam as a type of seam; and

in response to verifying that the candidate seam is the seam, defining a cluster of a subset of points within the plurality of points, thereby forming the seam.

20. The computer-implemented method of claim 9 , wherein the candidate seam is a first candidate seam, the computer-implemented method further comprising:

displaying, to a user and via a user interface, a plurality of candidate seams that are available to be welded, the plurality of candidate seams including the first candidate seam;

receiving, via the user interface, an indication that a second candidate seam from the plurality of candidate seams is to be welded; and

updating the welding instructions for the welding robot such that the welding robot is instructed to weld the second candidate seam.

21. The computer-implemented method of claim 9 , further comprising:

receiving, via a user interface, a change in a welding parameter from a user; and

updating the welding instructions for the welding robot based at least in part on the change in the welding parameter.

22. The computer-implemented method of claim 9 , further comprising:

scanning the part to be welded;

receiving user input from a user including instructions to weld at least one candidate seam on the part to be welded; and

defining a 3D model of the part to be welded based at least in part on the user input and the scan.

23. The computer-implemented method of claim 9 , further comprising:

scanning the part to be welded;

receiving user input from a user including instructions to weld at least one candidate seam on the part to be welded; and

defining a 3D model of the part to be welded based at least in part on the user input and the scan; and

saving the 3D model of the part to be welded in a database.

24. A computer-implemented method of generating instructions for a welding robot, the computer-implemented method comprising:

scanning a workspace containing the part to:

determine a location of the part within the workspace; and

produce a representation of the part;

determining an expected position of a candidate seam on the part to be welded in accordance with a Computer Aided Design (CAD) model of the part and the representation of the part; and

determining an actual position of the candidate seam based at least in part on the representation of the part.

25. The computer-implemented method of claim 24 , wherein the CAD model includes an annotation of the candidate seam.

26. The computer-implemented method of claim 25 , wherein determining the actual position of the candidate seam further comprises updating the expected position of the candidate seam based at least in part on the representation of the part.