Systems and methods for planning a robot grasp that can withstand task disturbances

• US 9,649,764 B1
• Filed: 12/13/2014
• Issued: 05/16/2017
• Est. Priority Date: 12/13/2013
• Status: Expired due to Fees

• Current Assignee: University of South Florida (State University System of Florida)
• Original Assignee: University of South Florida (State University System of Florida)
• Inventors: Sun, Yu, Lin, Yun
• Primary Examiner(s): Shafi, Muhammad
• Application Number:
  US14/569,694
• Time in Patent Office: 885 Days
• Field of Search: 700253, 700 19, 700 28, 700 97, 31856811, 31856812, 3185682, 701 98
• CPC Class Codes: B25J 13/025 comprising haptic means
• CPC Class Codes: B25J 9/1612 characterised by the hand, wrist, grip control
• CPC Class Codes: B25J 9/1671 characterised by simulation, either to verify existing program or to create and verify new program, CAD/CAM oriented, graphic oriented programming systems
• CPC Class Codes: G05B 2219/39542 Plan grasp points, grip matrix and initial grasp force
• CPC Class Codes: G05B 2219/39546 Map human grasps to manipulator grasps
• CPC Class Codes: G05B 2219/40072 Exert a screwing motion
• CPC Class Codes: Y10S 901/02 Arm motion controller
• CPC Class Codes: Y10S 901/04 Manual lead through

Abstract

In one embodiment, a system and method for planning a robot grasp involve measuring interaction forces imposed on an object by an environment while a task is demonstrated using the object to obtain a disturbance distribution dataset, modeling a task requirement based upon the disturbance distribution dataset, identifying robot grasp types that can be used to satisfy the task requirement, calculating a grasp wrench space for each identified robot grasp, and calculating a grasp quality of each grasp.

Claims

1. A method for planning a robot grasp, the method comprising:

measuring interaction forces imposed on an object by an environment while a task is demonstrated by a human user using the object to obtain a disturbance distribution dataset, wherein demonstrating the task comprises the user demonstrating the task with an actual object in an actual environment, wherein the object comprises one or more sensors that measure the forces, or comprises the user demonstrating the task with a virtual object in a virtual environment, wherein the virtual object is controlled in the virtual environment by the user with a haptic device;

a computer modeling a task requirement based upon the disturbance distribution dataset;

the computer identifying multiple robot grasp types stored in computer memory that can be used to satisfy the task requirement;

the computer calculating a grasp wrench space for each identified robot grasp type;

the computer calculating a grasp quality of each robot grasp type; and

the computer selecting the robot grasp type that has the highest grasp quality.

2. The method of claim 1, wherein measuring interaction forces comprises continually measuring the forces during performance of the task to obtain a task wrench space that comprises the set of all wrenches acting upon the object as the task is performed.

3. The method of claim 2, further comprising modifying the task wrench space to account for gravity.

4. The method of claim 1, wherein modeling a task requirement comprises modeling the task requirement as a non-parametric statistical distribution of disturbance.

5. The method of claim 4, further comprising downsampling the statistical distribution to reduce computational complexity.

6. The method of claim 1, wherein identifying robot grasp types comprises constraining the grasp types according to a human thumb location and direction relative to the object.

7. The method of claim 1, wherein calculating a grasp wrench space comprises calculating the grasp wrench space using contact points and contact normals of the grasp and a friction coefficient of the object.

8. The method of claim 1, wherein calculating a grasp quality comprises quantifying the portion of the disturbance distribution that the grasp covers.

9. The method of claim 8, wherein calculating the grasp quality comprises calculating a ratio of a task wrench space of the task and the grasp wrench space.

10. A non-transitory computer-readable medium comprising:

logic configured to receive interaction forces imposed on an object by an environment to obtain a disturbance distribution dataset, the forces having been measured while a task was demonstrated using the object, wherein demonstrating the task comprises the user demonstrating the task with an actual object in an actual environment, wherein the object comprises one or more sensors that measure the forces, or comprises the user demonstrating the task with a virtual object in a virtual environment, wherein the virtual object is controlled in the virtual environment by the user with a haptic device;

logic configured to model a task requirement based upon the disturbance distribution dataset;

logic configured to identify robot grasp types that can be used to satisfy the task requirement;

logic configured to calculate a grasp wrench space for each identified robot grasp type;

logic configured to calculate a grasp quality of each robot grasp type, and

logic configured to select the robot grasp type that has the highest grasp quality.

11. The non-transitory computer-readable medium of claim 10, wherein the logic configured to model a task requirement is configured to model the task requirement as a non-parametric statistical distribution of disturbance.

12. The non-transitory computer-readable medium of claim 10, wherein the logic configured to identify robot grasp types is configured to constrain the grasp types according to a human thumb location and direction relative to the object.

13. The non-transitory computer-readable medium of claim 10, wherein the logic configured to calculate a grasp wrench space is configured to calculate the grasp wrench space using contact points and contact normals of the grasp and a friction coefficient of the object.

14. The non-transitory computer-readable medium of claim 10, wherein the logic configured to calculate a grasp quality is configured to calculate a ratio of a task wrench space of the task and the grasp wrench space.

15. A system for planning a robot grasp, the system comprising:

a force measurement device comprising one or more sensors configured to measure interaction forces imposed on an actual object when the object is used by a human user to perform a task in an actual environment or a haptic device configured to measure interaction forces imposed on a virtual object when the haptic device is used by the human user to perform a task with the virtual object in a virtual environment;

a computer having a non-transitory computer-readable medium that stores a grasp planning system including;

logic configured to receive the interaction forces measured by the force measurement device to obtain a disturbance distribution dataset,

logic configured to model a task requirement based upon the disturbance distribution dataset,

logic configured to identify robot grasp types that can be used to satisfy the task requirement,

logic configured to calculate a grasp wrench space for each identified robot grasp type,

logic configured to calculate a grasp quality of each robot grasp type, and

logic configured to select the robot grasp type that has the highest grasp quality.

16. The system of claim 15, wherein the logic configured to calculate a grasp quality is configured to calculate a ratio of a task wrench space of the task and the grasp wrench space.

17. The system of claim 15, wherein the logic configured to model a task requirement is configured to modify the task requirement to account for gravity.

18. The system of claim 15, wherein the logic configured to model a task requirement is configured to model the task requirement as a non-parametric statistical distribution of disturbance.

19. The system of claim 18, wherein the logic configured to model a task requirement is further configured to downsample the statistical distribution to reduce computational complexity.

20. The system of claim 15, wherein the logic configured to identify robot grasp types is configured to constrain the grasp types according to a human thumb location and direction relative to the object.

21. The system of claim 15, wherein the logic configured to calculate a grasp quality is configured to calculate the grasp wrench space using contact points and contact normals of the grasp and a friction coefficient of the object.

22. The system of claim 15, wherein the logic configured to calculate a grasp quality is configured to quantify the portion of the disturbance distribution that the grasp covers.