Apparatus and method for calculating position of robot

• US 8,255,081 B2
• Filed: 09/12/2008
• Issued: 08/28/2012
• Est. Priority Date: 09/14/2007
• Status: Active Grant

• Current Assignee: Samsung Electronics Co. Ltd.
• Original Assignee: Samsung Electronics Co. Ltd.
• Inventors: Lee, Hyoung-Ki, Choi, Ki-Wan, Park, Ji-Young, Bang, Seok-Won
• Primary Examiner(s): Trammell, James
• Assistant Examiner(s): Marc, McDieunel
• Application Number:
  US12/232,255
• Publication Number:
  US 20090076651A1
• Time in Patent Office: 1,446 Days
• Field of Search: 700/245, 700/248, 318/568.12, 701/23, 701/196, 701/207, 701/213, 701/300, 425/62, 425/72.1, 425/110, 425/E21.228, 425/326.1, 425/387.1, 414/744.5, 342/357.31, 219/125.12, 901/1, 901/40, 702/196, 427/348, 118/62, 257/E21.228, 134/182, 134/902, 744/900.3
• US Class Current: 700/245
• CPC Class Codes: B25J 13/089 Determining the position of the robot with reference to its environment
• CPC Class Codes: G05D 1/024 in combination with a laser lasers per se H01S
• CPC Class Codes: G05D 1/0246 using a video camera in combination with image processing means
• 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
• CPC Class Codes: G05D 1/0274 using mapping information stored in a memory device navigation using map-matching G01C21/30

Abstract

Provided are an apparatus and method of calculating the position of a robot, which determine the position of a robot and create a map of the surroundings of the robot which account for the travel state of the robot. The apparatus includes a state variable calculation module which calculates a plurality of state variables regarding a travel state of the robot by using a plurality of Kalman filters; a travel state determination module which determines the travel state of the robot based on the results of the calculation performed by the state variable calculation module; and a state variable update module which updates the state variables according to the result of the determination performed by the travel state determination module.

Claims

1. An apparatus to calculate a position of a robot, comprising:

a state variable calculation module which calculates a plurality of state variables regarding a plurality of possible travel states of the robot, comprising a plurality of Kalman filters;

a travel state determination module which determines a travel state from among the plurality of possible travel states of the robot based on the calculated state variables; and

a state variable update module which updates the state variables according to the determined travel state,

wherein the plurality of possible travel states include a normal state and a slip state.

2. The apparatus of claim 1, wherein the state variable calculation module comprises a first Kalman filter and a second Kalman filter.

3. The apparatus of claim 2, wherein the first Kalman filter comprises a first predictor and a first corrector and the apparatus further comprises an encoder in communication with the first corrector and an inertial sensor in communication with the first predictor.

4. The apparatus of claim 2, wherein the second Kalman filter comprises a second predictor and a second corrector and the apparatus further comprises an inertial sensor in communication with the second corrector.

5. The apparatus of claim 4, wherein the second Kalman filter further comprises an encoder.

6. The apparatus of claim 2, wherein the travel state determination module determines whether the travel state of the robot is the normal state or the slip state based on the state variables provided by the first and second Kalman filters.

7. The apparatus of claim 2, wherein the travel state determination module applies different weights to the state variables provided by the first and second Kalman filters according to whether the travel state of the robot is the normal state or the slip state.

8. The apparatus of claim 7, wherein the state variable update module generates an updated state variable by combining the state variables to which the different weights are applied.

9. The apparatus of claim 8, wherein the state variable calculation module calculates a state variable for the normal state and a state variable for the slip state based on the updated state variable.

10. The apparatus of claim 1, wherein the slip state is a state in which one or more wheels of the robot slips, and a measured displacement of the robot corresponding to a rotation of the one or more wheels of the robot does not correspond to an actual displacement of the robot due to the slipping.

11. The apparatus of claim 1, wherein the travel state determination module calculates a probability density function for each of the plurality of possible travel states, and calculates a probability of the robot currently being in each of the plurality of possible travel states, to determine the travel state.

12. A method of calculating a position of a robot, comprising:

calculating a plurality of state variables regarding a plurality of possible travel states of the robot;

determining, by one or more processors, a travel state of the robot from among the plurality of possible travel states based on the calculating; and

updating the state variables according to the determining,

wherein the plurality of possible travel states include a normal state and a slip state.

13. The method of claim 12, wherein the calculating comprises calculating the state variables using a first Kalman filter and a second Kalman filter.

14. The method of claim 13, wherein the first Kalman filter comprises a first predictor and a first corrector and the calculating comprises using an encoder and an inertial sensor.

15. The method of claim 13, wherein the second Kalman filter comprises a second predictor and a second corrector and the calculating comprises using an inertial sensor.

16. The method of claim 15, wherein the second Kalman filter further comprises an encoder.

17. The method of claim 13, wherein the determining comprises determining whether the travel state of the robot is the normal state or the slip state based on the state variables calculated by the first and second Kalman filters.

18. The method of claim 13, wherein the determining comprises applying different weights to the state variables calculated by the first and second Kalman filters according to whether the travel state of the robot is the normal state or the slip state.

19. The method of claim 18, wherein the updating comprises generating an updated state variable by combining the state variables to which the different weights are applied.

20. The method of claim 19, wherein the calculating comprises calculating a state variable for the normal state and a state variable for the slip state based on the updated state variable.