Robot end-effector sensing and identification

1. A system for identifying an end effector of a robot, and processing a workpiece using the identified end effector, in a processing environment, the system comprising:

at least one sensor for digitally recording visual information, the at least one sensor being positioned to record at least one image of the end effector in an arbitrary orientation;

a computer memory for storing a digital model of each of a plurality of end effectors; and

a processor configured to (i) identify the end effector based on analyzing the at least one recorded image and the stored digital models, (ii) computationally determine a protective separation distance based at least in part on the end effector identity, and (iii) operate the robot to manipulate a workpiece, using the end effectors while maintaining the protective separation distance between a human and the robot.

2. The system of claim 1 , wherein the processor is configured to computationally generate, from at least one recorded image, a 3D spatial representation of the end effector.

3. The system of claim 1 , wherein the processor is further configured to generate a 3D voxel-grid volumetric representation of the end effector.

4. The system of claim 1 , wherein the processor is configured to recognize an end effector based on at least one recorded image and comparison thereof to the stored digital models.

5. The system of claim 4 , wherein the processor is configured to recognize an end effector based further on a known position and pose.

6. The system of claim 1 , wherein the digital model is a CAD representation.

7. The system of claim 1 , wherein the digital model is a machine-learning representation.

8. A method for identifying an end effector of a robot, and processing a workpiece with the robot using the identified end effector, in a processing environment, the method comprising the steps of:

digitally recording at least one image of the end effector in an arbitrary orientation;

storing a digital model of each of a plurality of end effectors;

computationally identifying the end effector based on analyzing at least one recorded image as corresponding to one of the stored digital models;

computationally determining a protective separation distance based at least in part on the end effector identity; and

operating a robot to manipulate a workpiece, using the end effectors while maintaining the protective separation distance between a human and the robot.

9. The method of claim 8 , further comprising the step of computationally generating, from at least one recorded image, a 3D spatial representation of the end effector.

10. The method of claim 8 , further comprising the step of computationally generating a 3D voxel-grid volumetric representation of the end effector.

11. The method of claim 10 , wherein the volumetric representation is generated following background subtraction.

12. The method of claim 8 , further comprising the step of computationally generating a point cloud representation of the end effector.

13. The method of claim 8 , wherein the end effector is recognized based on at least one recorded image and comparison thereof to the stored digital models.

14. The method of claim 13 , wherein the end effector is recognized based further on a known position and pose.

15. The method of claim 8 , wherein the digital model is a CAD representation.

16. The method of claim 8 , wherein the digital model is a machine-learning representation.

17. The method of claim 16 , wherein the machine-learning representation is a neural network.

18. The method of claim 8 , wherein the end effector is computationally identified during a configuration sequence.

19. The method of claim 18 , wherein the configuration sequence comprises computationally generating a space map of a workspace and a computational model of statis objects and machinery therein.