Robotics systems

• US 8,452,448 B2
• Filed: 04/10/2008
• Issued: 05/28/2013
• Est. Priority Date: 04/02/2008
• Status: Active Grant

• Current Assignee: iRobot Corporation
• Original Assignee: iRobot Corporation
• Inventors: Pack, Robert T., Vale, Marshall J., Kearns, Justin H.
• Primary Examiner(s): Jen, Ian
• Application Number:
  US12/100,782
• Publication Number:
  US 20090254217A1
• Time in Patent Office: 1,874 Days
• Field of Search: None
• US Class Current: 700/246
• CPC Class Codes: G06N 3/008 based on physical entities controlled by simulated intelligence so as to replicate intelligent life forms, e.g. based on robots replicating pets or humans in their appearance or behaviour

Abstract

A method of controlling a robot includes running multiple applications on a processor, where each application has a robot controller and an action selection engine. Each application is in communication with at least one behavior and at least one action model of at least part of the robot. The method includes running periodic action selection cycles on each action selection engine. Each action selection cycle includes selecting a command for each action space of each action model, generating a single overall command based on the accumulated commands for each action model, and sending the overall command to the robot controller for execution on the robot.

Claims

1. A method performed by one or more processors for generating a command for a robot controller, the method comprising:

calling at least one action model having at least one action space to generate one or more feasible commands for each action space;

calling each action model to generate an outcome for each command;

sending each outcome to at least one behavior for evaluation, each behavior providing an outcome evaluation for each outcome;

selecting a winning outcome for each action space based on the at least one outcome evaluation;

selecting the command corresponding to the winning outcome for each action space;

generating a single overall command based on one or more accumulated commands for each action space; and

sending the overall command to the robot controller.

2. The method of claim 1, further comprising:

obtaining a system state from the robot controller; and

informing each action model and each behavior of the system state.

3. The method of claim 1, wherein calling the action model to generate one or more feasible commands for a first action space comprises executing a heuristic search on the first action space to find the one or more feasible commands.

4. The method of claim 3, wherein the search comprises a hill climbing search technique to search the first action space near a last known command.

5. The method of claim 3, wherein the search comprises a search technique utilizing a command history of the first action space.

6. The method of claim 3, wherein the search comprises a randomized search based on a current command.

7. The method of claim 1, wherein each action model is sequentially called in a predetermined order and each action space within each action model is sequentially called in a predetermined order.

8. The method of claim 7, further comprising considering the winning outcomes of any preceding action spaces when selecting the winning outcome for each action space.

9. The method of claim 1, wherein the outcome evaluations are weighted according to weights associated with each behavior.

10. The method of claim 1, further comprising generating an overall outcome for the overall command, and sending the overall outcome to each behavior as feedback.

11. A method performed by one or more processors for controlling a robot, the method comprising:

running multiple applications on a processor, each application having a robot controller and an action selection engine, each application being in communication with at least one behavior and at least one action model of at least part of the robot; and

running periodic action selection cycles on each action selection engine, each action selection cycle comprising;

selecting a command for each action space of each action model;

generating a single overall command based on the one or more accumulated commands for each action model; and

sending the overall command to the robot controller for execution on the robot.

12. The method of claim 11, wherein the action selection cycle further comprises:

obtaining a system state from the robot controller;

informing each action model and each behavior of the system state; and

informing each action model and each behavior of the start of the action selection cycle.

13. The method of claim 11, wherein selecting a command for each action space comprises:

calling the corresponding action model to generate one or more feasible commands for the action space;

calling the corresponding action model to generate outcomes for the one or more feasible commands; and

calling each behavior to evaluate and provide an outcome evaluation for each outcome;

accumulating the outcome evaluations of each behavior;

selecting a winning outcome for the action space; and

selecting the command corresponding to the winning outcome.

14. The method of claim 13, wherein calling the action model to generate one or more feasible commands for the action space comprises executing a heuristic search on the action space to find the one or more feasible commands.

15. The method of claim 14, wherein the search comprises a hill climbing search technique to search the action space near a last known command.

16. The method of claim 14, wherein the search comprises a search technique utilizing a command history of the action space.

17. The method of claim 14, wherein the search comprises a randomized search based on a current command.

18. The method of claim 13, wherein each action model is sequentially called in a predetermined order and each action space within each action model is sequentially called in a predetermined order.

19. The method of claim 18, further comprising considering the winning outcomes of any preceding action spaces when selecting the winning outcome for each action space.

20. The method of claim 13, wherein the outcome evaluations are weighted according to weights associated with each behavior.

21. The method of claim 11, further comprising generating an overall outcome for the overall command, and sending the overall outcome to each behavior as feedback.

22. The method of claim 11, further comprising implementing an application priority policy that determines which application has exclusive control of resources of the robot required by that application at a given time.