MULTI-SENSOR, AUTONOMOUS ROBOTIC VEHICLE WITH LAWN CARE FUNCTION

• US 20180103579A1
• Filed: 12/16/2015
• Published: 04/19/2018
• Est. Priority Date: 12/17/2014
• Status: Active Grant

• Current Assignee: Husqvarna AB (HUSQVARNA)
• Original Assignee: Husqvarna AB (HUSQVARNA)
• Inventors: Grufman, Stefan, Mannefred, Bjrn, Jgenstedt, Patrik, hrlund, Magnus, Deimert, Johan
• Granted Patent: US 10,806,075 B2
• Time in Patent Office: Days
• CPC Class Codes: A01D 2101/00 Lawn-mowers
• CPC Class Codes: A01D 34/008 for automated or remotely controlled operation
• CPC Class Codes: A01D 34/78 electric
• CPC Class Codes: A01D 69/02 electric
• CPC Class Codes: G05D 1/0011 associated with a remote control arrangement
• 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 1/0246 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 1/0274 using mapping information stored in a memory device navigation using map-matching G01C21/30
• CPC Class Codes: G05D 1/0278 using satellite positioning signals, e.g. GPS

Abstract

A robotic vehicle may include one or more functional components configured to execute a lawn care function, a sensor network comprising one or more sensors configured to detect conditions proximate to the robotic vehicle, an object detection module configured to 5 detect objects proximate to the robotic vehicle using contact-less detection, a positioning module configured to determine robotic vehicle position, and a mapping module configured to generate map data regarding a parcel on which the robotic vehicle operates.

Claims

1. A robotic vehicle comprising:

one or more functional components configured to execute a lawn care function;

a sensor network comprising one or more sensors configured to detect conditions proximate to the robotic vehicle;

an object detection module configured to detect objects proximate to the robotic vehicle using contact-less detection;

a positioning module configured to determine robotic vehicle position; and

a mapping module configured to generate map data regarding a parcel on which the robotic vehicle operates.

2. The robotic vehicle of claim 1, wherein the object detection module is configured to receive object detection information from a 2.5D sensor.

3. The robotic vehicle of claim 2, wherein the 2.5D sensor comprises a mobile time-of-flight measurement device.

4. The robotic vehicle of claim 2, wherein the 2.5D sensor comprises a light emitting diode detection and ranging (LEDDAR) sensor.

5. The robotic vehicle of claim 1, wherein the positioning device is configured to receive positioning information from an inertial measurement unit (IMU).

6. The robotic vehicle of claim 1, wherein the positioning device is configured to receive positioning information from a real time kinematic (RTK)—

GPS receiver.

7. The robotic vehicle of claim 1, wherein the positioning device is configured to receive positioning information from an RF receiver.

8. The robotic vehicle of claim 1, wherein the positioning module is configured to assign a probability to each input from respective ones of different sensors of the sensor network to generate a composite position estimate for the robotic vehicle.

9. The robotic vehicle of claim 1, wherein the object detection module is configured to receive object detection information from a grass sensor.

10. The robotic vehicle of claim 9, wherein the grass sensor comprises a chlorophyll detector.

11. The robotic vehicle of claim 9, wherein the grass sensor employs a camera and analyzes hue, saturation and value (HSV) parameters of images to detect grass.

12. The robotic vehicle of claim 9, wherein the grass sensor employs a camera and analyzes edges in images to identify grass based on structure.

13. The robotic vehicle of claim 9, wherein the grass sensor employs a camera and identifies grass based on machine learning associated with matching of a current image with a known image of grass.

14. The robotic vehicle of claim 1, wherein the sensor network comprises a camera.

15. The robotic vehicle of claim 14, wherein the camera provides images of detected objects to the object detection module for classification based on stored images of known objects.

16. The robotic vehicle of claim 1, wherein object detection module is configured to detect static objects and dynamic objects, and

wherein the object detection module is configured to classify detected static objects or dynamic objects.

18. The robotic vehicle of claim 1, wherein the mapping module is configured to operate in a map generation mode or in a navigation mode.

19. The robotic vehicle of claim 18, wherein, in the map generation mode, the mapping module is configured to incorporate input from multiple sensors of the sensor network to determine current position of the robotic vehicle and at least one detected object or boundary to generate a map of the parcel responsive to traversal of the parcel by the robotic vehicle.

20. The robotic vehicle of claim 18, wherein the mapping module is configured to generate a map or correlate data gathered to map data to enable the robotic vehicle to reconcile its position with the map.

21. The robotic vehicle of claim 18, wherein the mapping module is configured to generate a map indicative of terrain features of the parcel, generate a two dimensional map, or generate a map indicating object classifications of classified objects.

17. (canceled)

22. (canceled)

23. (canceled)