Robot and method and medium for localizing the same by using calculated covariance

• US 8,315,734 B2
• Filed: 03/21/2007
• Issued: 11/20/2012
• Est. Priority Date: 04/04/2006
• Status: Active Grant

• Current Assignee: Samsung Electronics Co. Ltd. (Samsung Group)
• Original Assignee: Samsung Electronics Co. Ltd. (Samsung Group)
• Inventors: Myeong, Hyeon, Lee, Yong-beom, Bang, Seok-won, Lee, Hyoung-ki, Choi, Ki-wan, Choe, Eun-young
• Primary Examiner(s): Tran, Khoi
• Assistant Examiner(s): Amin, Bhavesh V
• Application Number:
  US11/688,983
• Publication Number:
  US 20070244600A1
• Time in Patent Office: 2,071 Days
• Field of Search: None
• US Class Current: 700/245
• CPC Class Codes: G05D 1/027 comprising intertial navigation means, e.g. azimuth detector inertial navigation G01C21/16; inertial navigation combined with non-inertial navigation instruments G01C21/165
• CPC Class Codes: G05D 1/0272 comprising means for registering the travel distance, e.g. revolutions of wheels measuring distance traversed on the ground by vehicles, e.g. using odometers G01C22/00

Abstract

Provided are a robot capable of improving the computation rate by considering whether a parameter will diverge and modifying the experiment order of offspring during evolutionary computation, when the covariance of system noise and that of measurement noise are calculated for the purpose of localizing the robot by using a Kalman filter, and a method and medium of localizing a robot by using a calculated covariance. The robot includes a gyroscope module providing information regarding rotational angle; an encoder module providing information regarding velocity and information regarding rotational angle of a wheel by sensing motion of the wheel; and a control module estimating a current location according to a Kalman filter method based on information provided by the encoder module and the gyroscope module, a covariance of system noise and a covariance of measurement noise being calculated in an evolutionary computation and applied to the Kalman filter method by the control module, the covariance of system noise and the covariance of measurement noise satisfying a condition that no Kalman filter parameter diverges in the evolutionary computation.

Claims

1. A robot comprising:

a gyroscope module to provide rotational angle of the robot;

an encoder module to provide velocity and value related to rotational angle of a wheel by sensing motion of the wheel; and

a controller to estimate a current location according to a Kalman filter, to which a covariance of system noise and measurement noise calculated in an evolutionary computation are applied, based on the rotational angle of the robot, velocity, and value related to rotational angle of the wheel,

wherein the control module applies the covariance of system noise and measurement noise to the Kalman filter in order to satisfy a condition where no Kalman filter parameter diverges in the evolutionary computation,

wherein the covariance of system noise and the covariance of measurement noise are calculated by preferentially performing computation of a trial for obtaining measurements when the robot is caused to move in a specific path in a following evolutionary computation, the trial causing the Kalman filter parameter to diverge, in the evolutionary computation regarding a plurality of trials.

2. The robot of claim 1, wherein the condition where no Kalman filter parameter diverges in the evolutionary computation is that the Kalman filter parameter does not exceed a predetermined critical range.

7. The robot of claim 1, wherein the Kalman filter has constraints with the Kalman filter parameter having a defined range.

8. The robot of claim 7, wherein the Kalman filter satisfies Kuhn-Tucker optimality conditions and the penalty function theorem.

9. The robot of claim 7, wherein the constraints are expressed as penalty functions.

10. The robot of claim 1, wherein the rotational angle of the robot comprises a heading angle of the robot.

11. The robot of claim 1, wherein the rotational angle of the wheel comprises a traveling distance of the wheel.

23. At least one computer readable medium storing computer readable instructions that control at least one processor to implement the method of claim 10.

3. A robot comprising:

a gyroscope module to provide rotational angle of the robot;

an encoder module to provide velocity and value related to rotational angle of a wheel by sensing motion of the wheel; and

a controller to estimate a current location according to a Kalman filter, to which a covariance of system noise and measurement noise calculated in an evolutionary computation are applied, based on the rotational angle of the robot, velocity, and value related to rotational angle of the wheel,

wherein the covariance of system noise and the covariance of measurement noise are applied to the Kalman filter by the control module when error calculated in the evolutionary computation has a smallest mean value,

wherein the covariance of system noise and the covariance of measurement noise are calculated by preferentially performing computation of a trial for obtaining measurements when the robot is caused to move in a specific path in a following evolutionary computation, the trial causing the Kalman filter parameter to diverge, in the evolutionary computation regarding a plurality of trials.

5. The robot of claim 3, wherein the error comprises a root mean square between a true value calculated by a measurement obtained when the robot is caused to move in a specific path and a value calculated by a Kalman filter.

6. The robot of claim 3, wherein the error comprises error in distance and error in angle.

4. A robot comprising:

a gyroscope module to provide rotational angle of the robot;

an encoder module to provide velocity and value related to rotational angle of a wheel by sensing motion of the wheel; and

a controller to estimate a current location according to a Kalman filter, to which a covariance of system noise and measurement noise calculated in an evolutionary computation are applied, based on the rotational angle of the robot, velocity, and value related to rotation angle of the wheel,

wherein the covariance of system noise and the covariance of measurement noise are applied to the Kalman filter by the control module when the predetermined value which replaces an error calculated in the evolutionary computation when a Kalman filter parameter diverges in the evolutionary computation has a smallest mean value,

wherein the covariance of system noise and the covariance of measurement noise are calculated by preferentially performing computation of a trial for obtaining measurements when the robot is caused to move in a specific path in a following evolutionary computation, the trial causing the Kalman filter parameter to diverge, in the evolutionary computation regarding a plurality of trials.

12. A method of localizing a robot by using a calculated covariance, comprising:

providing rotational angle of the robot;

providing velocity and value relating to rotational angle of a wheel by sensing motion of the wheel; and

estimating a current location according to a Kalman filter, to which a covariance of system noise and measurement noise calculated in an evolutionary computation are applied, based on rotational angle of the robot, velocity, and value related to rotational angle of the wheel,

wherein the covariance of system noise and measurement noise are applied to the Kalman filter in order to satisfy a condition where no Kalman filter parameter diverges in the evolutionary computation,

wherein the covariance of system noise and the covariance of measurement noise are calculated by preferentially performing computation of a trial for obtaining measurements when the robot is caused to move in a specific path in a following evolutionary computation, the trial causing the Kalman filter parameter to diverge, in the evolutionary computation regarding a plurality of trials.

13. The method of claim 12, wherein the Kalman filter parameter is considered divergent when the Kalman filter parameter exceeds a predetermined critical range.

14. The method of claim 12, wherein the estimating involves applying the covariance of system noise and the covariance of measurement noise to the Kalman filter when an error calculated in the evolutionary computation has a smallest mean value.

15. The method of claim 14, wherein the error is replaced with a predetermined value when the Kalman filter parameter diverges in the evolutionary computation.

16. The method of claim 14, wherein the error comprises a root mean square between a true value calculated by a measurement obtained when the robot is caused to move in a specific path and a value calculated by the Kalman filter.

17. The method of claim 14, wherein the error comprises error in distance and error in angle.

18. The method of claim 12, wherein the Kalman filter has constraints with the Kalman filter parameter having a defined range.

19. The method of claim 18, wherein the Kalman filter satisfies Kuhn-Tucker optimality conditions and the penalty function theorem.

20. The method of claim 18, wherein the constraints are expressed as penalty functions.

21. The robot of claim 12, wherein the rotational angle of the robot comprises a heading angle of the robot.

22. The robot of claim 12, wherein the rotational angle of the wheel comprises a traveling distance of the wheel.

24. A robot comprising:

a control module to estimate a current location according to a Kalman filter, to which a covariance of system noise and measurement noise calculated in an evolutionary computation are applied, based on rotational angle of the robot, velocity, and value related to rotational angle of the wheel,

wherein the control module applies the covariance of system noise and measurement noise to the Kalman filter in order to satisfy a condition where no Kalman filter parameter diverges in the evolutionary computation,

wherein the covariance of system noise and the covariance of measurement noise are calculated by preferentially performing computation of a trial for obtaining measurements when the robot is caused to move in a specific path in a following evolutionary computation, the trial causing the Kalman filter parameter to diverge, in the evolutionary computation regarding a plurality of trials.