Part modeling for path generation to guide robotic end effector
Aspects of the disclosure are directed towards path generation. A method includes a computing system registering a first coordinate system of a target object with a second coordinate system of a robot. The computing system can generate a trajectory over the surface of the target object based on the registration. The computing system can generate a robot job file based at least in part on the generated trajectory. The computing system can transmit the robot job file to a robot controller.
1. A method performed by a computing system, comprising:
generating a path for a representation of a surface of a target object, wherein the path comprises a virtual point, wherein the virtual point is associated with a normal, and wherein the virtual point corresponds to a real-world target;
generating a trajectory over the surface of the target object based on the path, the normal, and a registration a first coordinate system of the target object with a second coordinate system of a robot, wherein the trajectory comprises a set of robot joint parameters used to traverse the surface of the target object, and wherein the trajectory traverses the real-world target;
classifying a target type for the real-world target using a machine learning model based on scanned data of the surface of the target object; and
generating a robot job file, wherein the robot job file comprises the trajectory and an autonomous operation instruction.
2. The method of claim 1 , wherein the computing system comprises a user device in communication with a server, and wherein the method further comprises:
transmitting the robot job file to the server, wherein the server is in communication with the robot, wherein the robot is configured to perform an autonomous operation using the robot job file.
3. The method of claim 2 , wherein the method further comprises:
processing a first input, received via a user interface (UI), for generating the path over the representation of the surface of the target object, wherein the path is generated based on the first input; and
processing a second input, received via the UI, for generating the trajectory over the surface of the target object, wherein the trajectory is further generated based on the second input and the path.
4. The method of claim 2 , wherein the method further comprises:
processing control instructions for updating a visualization on the UI based on performance of the autonomous operation by the robot; and
updating the visualization on the UI based on the autonomous operation.
5. The method of claim 1 , wherein the method further comprises:
classifying a target type for the real-world target using a machine learning model based on scanned data of the surface of the target object, and wherein classifying the target type for the real-world target comprises:
receiving scanned data of the surface of the target object;
inputting the scanned data into the machine learning model, wherein the machine learning model is trained to identify a target object class; and
executing the machine learning model to classify the target type of the real-world target.
6. The method of claim 1 , wherein the method further comprises:
processing information that a position of the virtual point in relation to the target object has been modified;
generating a second path based on the modification of the position of the virtual point; and
generating a second trajectory based on the second path.
7. The method of claim 1 , wherein the computing system comprises a user device in communication with a server, and wherein the method further comprises:
causing the server to perform an operation associated with an autonomous operation based on a frequency of performing the operation in relation to the autonomous operation.
8. A computing system comprising:
one or more processors;
one or more computer readable media including instructions that, when executed by the one or more processors, cause the one or more processors:
generate a path for a representation of a surface of a target object, wherein the path comprises a virtual point, wherein the virtual point is associated with a normal, and wherein the virtual point corresponds to a real-world target;
generate a trajectory over the surface of the target object based on the path, the normal, and a registration a first coordinate system of the target object with a second coordinate system of a robot, wherein the trajectory comprises a set of robot joint parameters used to traverse the surface of the target object, and wherein the trajectory traverses the real-world target;
classify a target type for the real-world target using a machine learning model based on scanned data of the surface of the target object; and
generate a robot job file, wherein the robot job file comprises the trajectory and an autonomous operation instruction.
9. The computing system of claim 8 , wherein the computing system comprises a user device in communication with a server, and wherein the instructions that, when executed by the one or more processors, further cause the one or more processors to:
transmit the robot job file to the server, wherein the server is in communication with a robot, wherein the robot is configured to perform an autonomous operation using the robot job file.
10. The computing system of claim 9 , wherein the instructions that, when executed by the one or more processors, further cause the one or more processors to:
process a first input, received via a user interface (UI), for generating the path over the representation of the surface of the target object, wherein the path is generated based on the first input; and
process a second input, received via the UI, for generating the trajectory over the surface of the target object, wherein the trajectory is further generated based on the second input and the path.
11. The computing system of claim 9 , wherein the instructions that, when executed by the one or more processors, further cause the one or more processors to:
process control instructions for updating a visualization on the UI based on performance of the autonomous operation by the robot; and
update the visualization on the UI based on the autonomous operation.
12. The computing system of claim 8 , wherein the instructions that, when executed by the one or more processors, further cause the one or more processors to classify a target type for the real-world target using a machine learning model based on scanned data of the surface of the target object, and wherein classifying the target type for the real-world target comprises:
receiving scanned data of the surface of the target object;
inputting the scanned data into the machine learning model, wherein the machine learning model is trained to identify a target object class; and
executing the machine learning model to classify the target type of the real-world target.
13. The computing system of claim 8 , wherein the instructions that, when executed by the one or more processors, further cause the one or more processors to:
process information that a position of the virtual point in relation to the target object has been modified;
generate a second path based on the modification of the position of the virtual point; and
generate a second trajectory based on the second path.
14. The computing system of claim 8 , wherein the computing system comprises a user device in communication with a server, and wherein the instructions that, when executed by the one or more processors, further cause the one or more processors to:
cause the server to perform an operation associated with an autonomous operation based on a frequency of performing the operation in relation to the autonomous operation.
15. One or more computer-readable media including one or more sequences of instructions, when executed by one or more processors, cause a computing system to:
generate a path for a representation of a surface of a target object, wherein the path comprises a virtual point, wherein the virtual point is associated with a normal, and wherein the virtual point corresponds to a real-world target;
generate a trajectory over the surface of the target object based the path, the normal, and on a registration a first coordinate system of the target object with a second coordinate system of a robot, the path, and the normal, wherein the trajectory comprises a set of robot joint parameters used to traverse the surface of the target object, and wherein the trajectory traverses the real-world target;
classify a target type for the real-world target using a machine learning model based on scanned data of the surface of the target object; and
generate a robot job file, wherein the robot job file comprises the trajectory and an autonomous operation instruction.
16. The one or more computer-readable media of claim 15 , wherein the computing system comprises a user device in communication with a server, and wherein the instructions that, when executed by the one or more processors, further cause the one or more processors to:
transmit the robot job file to the server, wherein the server is in communication with a robot, wherein the robot is configured to perform an autonomous operation using the robot job file.
17. The one or more computer-readable media of claim 16 , wherein the instructions that, when executed by the one or more processors, further cause the one or more processors to:
process a first input, received via a user interface (UI), for generating the path over the representation of the surface of the target object, wherein the path is generated based on the first input; and
process a second input, received via the UI, for generating the trajectory over the surface of the target object, wherein the trajectory is further generated based on the second input and the path.
18. The one or more computer-readable media of claim 16 , wherein the instructions that, when executed by the one or more processors, further cause the one or more processors to:
process control instructions for updating a visualization on the UI based on performance of the autonomous operation by the robot; and
update the visualization on the UI based on the autonomous operation.
19. The one or more computer-readable media of claim 15 , wherein the instructions that, when executed by the one or more processors, further cause the one or more processors to classify a target type for the real-world target using a machine learning model based on scanned data of the surface of the target object, and wherein classifying the target type for the real-world target comprises:
receiving scanned data of the surface of the target object;
inputting the scanned data into the machine learning model, wherein the machine learning model is trained to identify a target object class; and
executing the machine learning model to classify the target type of the real-world target.
20. The one or more computer-readable media of claim 15 , wherein the instructions that, when executed by the one or more processors, further cause the one or more processors to:
process information that a position of the virtual point in relation to the target object has been modified;
generate a second path based on the modification of the position of the virtual point; and
generate a second trajectory based on the second path.