ROBOT AND METHOD TO RECOGNIZE AND HANDLE EXCEPTIONAL SITUATIONS

• US 20130184867A1
• Filed: 01/10/2013
• Published: 07/18/2013
• Est. Priority Date: 01/12/2012
• Status: Active Grant

• Current Assignee: Samsung Electronics Co. Ltd.
• Original Assignee: Samsung Electronics Co. Ltd.
• Inventors: Jang, Jun Won, Roh, Kyung Shik
• Granted Patent: US 9,132,547 B2
• Time in Patent Office: Days
• US Class Current: 700/253
• CPC Class Codes: B25J 9/163 learning, adaptive, model based, rule based expert control
• CPC Class Codes: B25J 9/1674 characterised by safety, monitoring, diagnostic
• CPC Class Codes: G05B 2219/33002 Artificial intelligence AI, expert, knowledge, rule based system KBS
• CPC Class Codes: G05B 2219/33056 Reinforcement learning, agent acts, receives reward, emotion, action selective
• CPC Class Codes: G05B 2219/45108 Aid, robot for aid to, assist human disabled

Abstract

A robot and method to recognize and handle abnormal situations includes a sensing unit to sense internal and external information of the robot, a storage unit to store the information sensed by the sensing unit, an inference model, a learning model, services providable by the robot, subtasks to provide the services, and handling tasks to handle abnormal situations, and a controller to determine whether an abnormal situation has occurred while the subtasks to provide the selected service are being performed through the learning model in the storage unit and to select a handling task to handle the abnormal situation through the inference model in the storage unit if it is determined that the abnormal situation occur. The robot may recognize and handle abnormal situations even though data other than that defined by a designer of the robot is input thereto due to noises or operational environment.

Claims

1. A robot comprising:

a sensing unit configured to sense information of the robot;

a storage unit configured to store the information sensed by the sensing unit, at least one inference model, at least one learning model, at least one service, at least one subtask, at least one handling task; and

a controller configured to select a service to be provided by the robot among the at least one service by using the inference model, to select a subtask to provide the selected service among the at least one subtask, and to determine whether an abnormal situation has occurred while the selected subtask are being performed by using the at least one learning model.

2. The robot according to claim 1, wherein the controller determines whether the service to be provided by the robot is selected, and determines whether the information satisfies rules included in the at least one inference model in order to select the service to be provided by the robot, if the service to be provided by the robot is not selected.

3. The robot according to claim 1, wherein the controller selects a learning model corresponding to the selected subtask among the at least one learning model, inputs the information to the selected learning model, and determines whether the abnormal situation has occurred based on an output from the selected learning model.

4. The robot according to claim 3, wherein the controller trains a learning model by mapping the information to occurrence/non-occurrence of abnormal situations if there is no learning model corresponding to the subtasks in the storage unit, and stores the trained learning model in the storage unit, in order to thereby create a learning model corresponding to the subtasks.

5. The robot according to claim 1, wherein the controller selects a handling task to handle the abnormal situation by using the at least one inference model if it is determined that the abnormal situation has occurred.

6. The robot according to claim 5, wherein the controller determines whether the information satisfies rules included in the at least one inference model in order to select the handling tasks to handle the abnormal situation.

13. The robot according to claim 1, wherein the information sensed by the sensing unit comprises internal and external information of the robot.

14. The robot according to claim 1, further comprising:

a drive unit configured to drive the robot in order to provide the services selected by the controller.

7. A method to recognize and handle abnormal situations in a robot, the method comprising:

sensing, by a sensor, information of the robot;

selecting, by a processor, a service to be provided by the robot by using an inference model;

selecting, by the processor, a subtask to provide the selected service; and

determining, by using a learning model, whether an abnormal situation has occurred while the subtask are being performed.

8. The method according to claim 7, wherein the selecting the service comprises:

determining whether the service is selected; and

determining whether the information satisfies rules included in the inference model in order to select the service, if a service to be provided by the robot is not selected.

9. The method according to claim 7, wherein the determining comprises:

selecting a learning model corresponding to the subtasks;

inputting the information to the selected learning model; and

determining whether the abnormal situation has occurred based on an output from the learning model.

10. The method according to claim 9, wherein the determining whether the abnormal situation has occurred further comprises:

training a learning model by mapping the information to occurrence/non-occurrence of abnormal situations if no learning model corresponds to the subtask; and

storing the trained learning model in order to create the learning model corresponding to the subtask.

11. The method according to claim 7, further comprising selecting a handling task to handle the abnormal situation by using the inference model if it is determined that an abnormal situation has occurred.

12. The method according to claim 11, wherein the selecting the handling task comprises:

determining whether the information satisfies rules included in the inference model in order to select the handling task.

15. The method according to claim 7, further comprising:

driving the robot in order to provide the selected services.

16. A non-transitory computer-readable recording medium storing a program to implement the method of claim 7.