DISTRIBUTED KNOWLEDGE BASE FOR VEHICULAR LOCALIZATION AND WORK-SITE MANAGEMENT

• US 20100063626A1
• Filed: 09/11/2008
• Published: 03/11/2010
• Est. Priority Date: 09/11/2008
• Status: Active Grant

• Current Assignee: Deere & Company
• Original Assignee: Deere & Company
• Inventors: Anderson, Noel Wayne
• Granted Patent: US 8,195,342 B2
• Time in Patent Office: Days
• US Class Current: 700/253
• CPC Class Codes: G05D 1/0088 characterized by the autonomous decision making process, e.g. artificial intelligence, predefined behaviours using knowledge based models G06N5/00
• CPC Class Codes: G05D 1/0221 involving a learning process
• CPC Class Codes: G05D 1/0246 using a video camera in combination with image processing means
• CPC Class Codes: G05D 1/0255 using acoustic signals, e.g. ultra-sonic singals sonar systems designed for anti-collision purposes G01S15/93
• CPC Class Codes: G05D 1/0257 using a radar radar systems designed for anti-collision purposes between land vehicles or between land vehicle and fixed obstacles G01S13/931
• CPC Class Codes: G05D 1/0261 using magnetic plots
• 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 1/0278 using satellite positioning signals, e.g. GPS

Abstract

The illustrative embodiments provide an apparatus for controlling a vehicle. In an illustrative embodiment, a vehicle is comprised of a machine controller, a steering system, a propulsion system, a braking system, a sensor system, and a knowledge base used by the machine controller. The machine controller identifies a dynamic condition and sends commands to the steering system, the propulsion system, and the braking system to move the vehicle.

Claims

1. A vehicle comprising:

a machine controller;

a steering system;

a propulsion system;

a braking system;

a sensor system; and

a knowledge base used by the machine controller, wherein the machine controller is connected to the steering system, the propulsion system, the braking system; and

the sensor system, wherein the machine controller identifies a dynamic condition and sends commands to the steering system, the propulsion system, and the braking system to move the vehicle using the knowledge base comprising a fixed knowledge base and a learned knowledge base.

2. The vehicle of claim 1, wherein the machine controller identifies the dynamic condition in an environment around the vehicle using a set of sensors on the vehicle.

3. The vehicle of claim 1, wherein the fixed knowledge base further comprises an online knowledge base and wherein the machine controller identifies an environment around the vehicle using the online knowledge base.

4. The vehicle of claim 1, wherein the machine controller identifies an environment around the vehicle based on user input.

5. The vehicle of claim 1, wherein the machine controller receives data from a plurality of sensors for the vehicle to form received data and generates a thematic map using the received data and the knowledge base.

6. The vehicle of claim 5, wherein the machine controller identifies the location of the vehicle, identifies a map based on the location of the vehicle, identifies location information for the vehicle that is accurate within a threshold;

and places thematic features into the map using the received data and the knowledge base in order to generate the thematic map.

7. The vehicle of claim 6, wherein the machine controller moves the vehicle using the thematic map.

8. The vehicle of claim 1, wherein the fixed knowledge base further comprises an online knowledge base, wherein the machine controller receives sensor data from a plurality of sensors for the vehicle and applies weights to the sensor data using the online knowledge base to form weighted sensor data.

9. The vehicle of claim 8, wherein the machine controller selects a portion of the weighted sensor data to form selected sensor data and performs localization using the selected sensor data.

10. The vehicle of claim 9, wherein the step of performing localization using the selected sensor data comprises the further steps wherein the machine controller identifies a set of objects in an environment around the vehicle using the selected sensor data and identifies a location of the vehicle using the location of the set of objects.

11. The vehicle of claim 10, wherein the step of identifying the set of objects in the environment around the vehicle using the selected sensor data comprises the further step wherein the machine controller identifies the set of objects using the sensor data and the knowledge base.

12. The vehicle of claim 11, wherein the knowledge base specifies the set of objects based on the environment.

13. The vehicle of claim 12, wherein the knowledge base specifies identifying at least one of a curb and driveway if the season is summer, and wherein the knowledge base specifies identifying at least one of a tree trunk and a tree branch if the season is winter.

14. The vehicle of claim 12, wherein the knowledge base specifies identifying at least one of a fairway and a golfing green if the season is summer, and wherein the knowledge base specifies identifying at least one of a tree trunk and a tree branch if the season is winter.

15. The vehicle of claim 1, wherein the machine controller selectively updates the learned knowledge base using the dynamic conditions.

16. The vehicle of claim 15, wherein the machine controller updates the learned knowledge base with changes to a set of objects in the environment.

17. The vehicle of claim 16, wherein the set of objects comprises at least one of a tree, a street, a yard, and a parked car.

18. The vehicle of claim 1, wherein the fixed knowledge base comprises an a priori knowledge base and an environmental knowledge base.

19. The vehicle of claim 1, wherein the machine controller further identifies a set of differences between an environment as detected by a set of sensors for the vehicle and a description of the environment in the fixed knowledge base.

20. The vehicle of claim 19, wherein the machine controller updates the learned knowledge base with the set of differences.

21. The vehicle of claim 19, wherein the set of differences comprises at least one of a change in an object in the environment, a new object in the environment, and a missing object from the environment.

22. The vehicle of claim 19, wherein the machine controller further sends the set of differences to a server based on a policy, where the set of differences are used to update the static knowledge base.

23. The vehicle of claim 22, wherein the policy is at least one of supervised and unsupervised learning, wherein supervised learning requires user input to update the static knowledge base, and wherein unsupervised learning updates the static knowledge base without user input.

24. The vehicle of claim 19, wherein the set of sensors comprises at least one of a global positioning system, structured light sensor, two dimensional/three dimensional lidar, dead reckoning, far and medium infrared camera, visible light camera, radar, ultrasonic sonar, and radio frequency identification reader.

25. A vehicle comprising:

a machine controller;

a steering system;

a propulsion system;

a braking system;

a sensor system; and

a knowledge base used by the machine controller, wherein the machine controller is connected to the steering system, the propulsion system, the braking system; and

the sensor system, wherein the machine controller identifies an environment using a set of sensors on the vehicle, wherein the set of sensors comprises at least one of a global positioning system, structured light sensor, two dimensional/three dimensional lidar, dead reckoning, far and medium infrared camera, visible light camera, radar, ultrasonic sonar, and radio frequency identification reader;

identifies the location of the vehicle;

identifies a map based on the location of the vehicle;

identifies a set of objects in the environment using data received from the set of sensors and a knowledge base, wherein the knowledge base specifies the set of objects based on the environment;

places thematic features into the map using the set of objects identified and the map identified;

controls the vehicle using the knowledge base comprising a fixed knowledge base and a learned knowledge base, wherein the fixed knowledge base further comprises an online knowledge base and an a priori knowledge base;

selectively updates the learned knowledge base with changes to the set of objects in the environment; and

sends the changes to the set of objects in the environment to a server based on a policy, where the changes to the set of objects in the environment are used to update the a priori knowledge base.