Real time feedback and dynamic adjustment for welding robots

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

receiving, via at least one sensor, data of a workspace that includes a part to be welded;

transforming, via a processor, the data into a point cloud data representing a three-dimensional surface of the part;

identifying, via the processor and based on the point cloud data, a desired state indicative of a desired position of at least a portion of the welding robot with respect to the part; and

comparing, via the processor, an estimated state indicative of an estimated position of at least the portion of the welding robot with respect to the part to the desired state, the estimated state based on a model of the unwelded part that includes annotations representing the welding instructions for the welding robot; and

updating, via the processor, the welding instructions based on the comparison.

2. The computer-implemented method of claim 1 , wherein the estimated state is further based on the data of the workspace.

3. The computer-implemented method of claim 1 , wherein the at least one sensor includes a first sensor and a second sensor, the computer-implemented method further comprising:

synchronizing, via the processor, the first sensor and the second sensor.

4. The computer-implemented method of claim 1 , wherein the first sensor is a laser emitter and the second sensor is a camera.

5. The computer-implemented method of claim 1 , wherein the at least one sensor is from a plurality of sensors, the computer-implemented method further comprising:

fusing respective data received from each sensor of the plurality of sensors to generate fused data; and

determining the estimated state based on the fused data.

6. The computer-implemented method of claim 1 , wherein comparing the estimated state to the desired state comprises implementing a geometric comparator technique.

7. The computer-implemented method of claim 1 , further comprising:

updating the point cloud data based at least in part on the desired state.

8. The computer-implemented method of claim 1 , wherein identifying the desired state comprises inputting the point cloud data into an artificial neural network to generate the desired state.

9. The computer-implemented method of claim 1 , wherein updating the welding instructions includes adjusting a motion of the welding robot.

10. The computer-implemented method of claim 1 , wherein updating the welding instructions comprises adjusting a motorized fixture associated with the part.

11. The computer-implemented method of claim 1 , wherein updating the welding instructions comprises dynamically updating welding parameters in real time.

12. The computer-implemented method of claim 1 , further comprising:

detecting a tack weld in a candidate seam on the part to be welded; and

updating welding parameters based at least in part on the detection of the tack weld.

13. The computer-implemented method of claim 1 , further comprising:

detecting that a gap associated with a seam on the part varies; and

updating welding parameters based at least in part on detection of the variable gap.

14. The computer-implemented method of claim 1 , wherein updating welding instructions comprises updating welding parameters, and wherein the updated welding parameters include at least one of welder voltage, welder current, duration of an electrical pulse, shape of an electrical pulse, and material feed rate.

15. The computer-implemented method of claim 1 , wherein receiving the data of the workspace comprises receiving the data in real time.

16. The computer-implemented method of claim 15 , further comprising monitoring the desired state based at least in part on the data received in real time.

17. The computer-implemented method of claim 1 , wherein identifying the desired state further comprises implementing a particle filter on the point cloud data.

18. The computer-implemented method of claim 1 , wherein identifying the desired state further comprises implementing an online classifier on the point cloud data.

19. The computer-implemented method of claim 1 , further comprising:

implementing a particle filter and an online classifier on the point cloud data.

20. A computer-implemented method, comprising:

receiving, via a laser emitter, data of a workspace that includes a part to be welded;

transforming, via a processor, the data into a point cloud data representing a three-dimensional surface of the part;

identifying at least one occluded laser line from the laser emitter;

implementing a particle filter on the point cloud data in response to identifying the at least one occluded laser line to identify a desired state indicative of a desired position of at least a portion of the welding robot with respect to the part;

comparing, via the processor, an estimated state indicative of an estimated position of at least the portion of the welding robot with respect to the part to the desired state; and

updating, via the processor, the welding instructions based on the comparison.

21. A system, comprising:

a sensor configured to obtain data of a workspace, the workspace including a part to be welded;

a controller communicably coupled to the sensor, the controller configured to:

transform the data to a point cloud data representing a three-dimensional surface of the part,

identify a desired state indicative of a desired position of at least a portion of a welding robot with respect to the part based on the point cloud data,

compare an estimated state indicative of an estimated position of at least the portion of the welding robot with respect to the part to the desired state, the estimated state is based on a model of the unwelded part that includes annotations representing the welding instructions for the welding robot; and

generate welding instructions based on the comparison; and

the welding robot to implement the welding instructions to weld the part.