MOVING AND SEARCHING METHOD OF MOBILE ROBOT FOR FOLLOWING HUMAN

• US 20180181137A1
• Filed: 05/08/2017
• Published: 06/28/2018
• Est. Priority Date: 12/23/2016
• Status: Active Grant

• Current Assignee: Korea Advanced Institute of Science and Technology
• Original Assignee: Korea Advanced Institute of Science and Technology
• Inventors: CHOI, Jongsuk, YUN, Sang-Seok, DO, Hoang Minh
• Granted Patent: US 10,534,366 B2
• Time in Patent Office: Days
• CPC Class Codes: G05D 1/0212 with means for defining a desired trajectory involving a plurality of land vehicles G05D1/0287
• CPC Class Codes: G05D 1/0246 using a video camera in combination with image processing means
• CPC Class Codes: G05D 1/0274 using mapping information stored in a memory device navigation using map-matching G01C21/30
• CPC Class Codes: G06F 18/2411 based on the proximity to a decision surface, e.g. support vector machines
• CPC Class Codes: G06V 10/25 Determination of region of interest [ROI] or a volume of interest [VOI]
• CPC Class Codes: G06V 10/50 by performing operations within image blocks; by using histograms, e.g. histogram of oriented gradients [HoG]; by summing image-intensity values; Projection analysis
• CPC Class Codes: G06V 10/62 relating to a temporal dimension, e.g. time-based feature extraction; Pattern tracking
• CPC Class Codes: G06V 10/764 using classification, e.g. of video objects
• CPC Class Codes: G06V 20/10 Terrestrial scenes scenes under surveillance with static cameras G06V20/52; scenes perceived from the exterior of a vehicle G06V20/56; scenes perceived from the interior of a vehicle G06V20/59
• CPC Class Codes: G06V 20/64 Three-dimensional objects
• CPC Class Codes: G06V 2201/07 Target detection
• CPC Class Codes: G06V 2201/121 using special illumination
• CPC Class Codes: G06V 40/25 Recognition of walking or running movements, e.g. gait recognition
• CPC Class Codes: Y10S 901/01 Mobile robot

Abstract

A human-following robot for searching a following target when failing to track the following target includes a location estimating module configured to estimate a location of the following target based on map information and trajectory information, a search range setting module configured to set a search range of the following target based on the estimated location information and human walking pattern information, and tracking module configured to track the following target after moving to the search range. Since the robot estimates a location of a missed following target and the moves to the estimated location by utilizing map information and human walking pattern information including walking pattern data of persons in a surrounding environment, which is accumulated during a predetermined period, the following target may be detected again.

Claims

1. A human-following robot for searching a target to be followed (hereinafter, referred to as a following target) when failing to track the following target, the human-following robot comprising:

a location estimating module configured to estimate a location of the following target based on map information and trajectory information;

a search range setting module configured to set a search range of the following target based on the estimated location information and human walking pattern information; and

a tracking module configured to track the following target after moving to the search range.

2. The human-following robot for searching a following target when failing to track the following target according to claim 1,

wherein the map information includes at least one of building interior structure information, furniture arrangement information and surrounding obstacle location information.

3. The human-following robot for searching a following target when failing to track the following target according to claim 1,

wherein the trajectory information is based on at least one of a distance, a direction and a speed with which the following target moves before failing to track the following target.

4. The human-following robot for searching a following target when failing to track the following target according to claim 1,

wherein the human walking pattern information is learned through walking pattern data of persons in a surrounding environment, which is accumulated during a predetermined period.

5. The human-following robot for searching a following target when failing to track the following target according to claim 1,

wherein the search range of the following target is limited by at least one of a width of a passage, the number of passages and a location of an obstacle, included in the map information.

6. The human-following robot for searching a following target when failing to track the following target according to claim 1, further comprising:

a moving path calculating module configured to calculate a moving path of the robot based on the map information.

7. The human-following robot for searching a following target when failing to track the following target according to claim 1,

wherein the human-following robot further comprises an image receiving module configured to receive a depth image and a color image from a camera,

wherein the tracking module includes a detection unit configured to detect the following target by comparing the depth image and the color image with previously input following target-related information, and

wherein the following target-related information includes at least one of a face shape of the following target, color information of clothes worn by the following target, and height information of the following target.

8. A method for searching a following target when failing to track the following target, the method comprising:

estimating a location of the following target based on map information and trajectory information;

setting a search range of the following target based on the estimated location information and human walking pattern information;

moving to the search range; and

tracking the following target.

9. The method for searching a following target when failing to track the following target according to claim 8,

wherein the map information includes at least one of building interior structure information, furniture arrangement information and surrounding obstacle location information.

10. The method for searching a following target when failing to track the following target according to claim 8,

wherein the trajectory information is based on at least one of a distance, a direction and a speed with which the following target moves before failing to track the following target.

11. The method for searching a following target when failing to track the following target according to claim 8,

wherein the human walking pattern information is learned through walking pattern data of persons in a surrounding environment, which is accumulated during a predetermined period.

12. The method for searching a following target when failing to track the following target according to claim 8,

wherein the search range of the following target is limited by at least one of a width of a passage, the number of passages and a location of an obstacle, included in the map information.

13. The method for searching a following target when failing to track the following target according to claim 8,

wherein the moving to the estimated location includes calculating a moving path of the robot based on the map information.

14. The method for searching a following target when failing to track the following target according to claim 8,

wherein the tracking the following target includes:

receiving a depth image and a color image from a camera; and

detecting the following target by comparing the depth image and the color image with previously input following target-related information,

wherein the following target-related information includes at least one of a face shape of the following target, color information of clothes worn by the following target, and height information of the following target.

15. A computer program for executing the method for searching a following target when failing to track the following target according to claim 8.