System and method for autonomous vehicle system planning

• US 10,649,457 B2
• Filed: 05/02/2017
• Issued: 05/12/2020
• Est. Priority Date: 05/02/2017
• Status: Active Grant

• Current Assignee: Autonomous Solutions, Inc., CNH Industrial America LLC (CNH Industrial N.V.)
• Original Assignee: CNH Industrial America LLC (CNH Industrial N.V.)
• Inventors: Foster, Christopher Alan, Posselius, John Henry, Turpin, Bret Todd, Morwood, Daniel John, Bunderson, Nathan Eric
• Primary Examiner(s): To, Tuan C
• Application Number:
  US15/585,002
• Publication Number:
  US 20180321683A1
• Time in Patent Office: 1,106 Days
• Field of Search: 701 25
• CPC Class Codes: A01B 69/008 automatic
• CPC Class Codes: G01C 21/1654 with electromagnetic compass
• CPC Class Codes: G05D 1/0219 ensuring the processing of the whole working surface
• CPC Class Codes: G05D 1/0221 involving a learning process
• CPC Class Codes: G06N 20/00 Machine learning

Abstract

A system includes a processor. The processor is configured to derive one or more partitions of a field based on a vehicle system data via a learning system. The processor is further configured to derive one or more turn features representative of vehicle turns in the field based on the vehicle system via the learning system. The processor is also configured to derive an autonomous vehicle plan based on the partitions of the field and the turn features via a planning system, wherein the autonomous vehicle plan comprises a planned route of the autonomous vehicle in the field.

Claims

1. A system, comprising:

a processor configured to;

derive one or more partitions of a field based on a vehicle system data via a learning system;

derive one or more turn features representative of vehicle turns in the field based on the vehicle system via the learning system;

derive an autonomous vehicle plan based on the partitions of the field and the turn features via a planning system, wherein the autonomous vehicle plan comprises a planned route of the autonomous vehicle in the field, wherein the learning system comprises a forward-chained expert system, a backward-chained expert system, a neural network, a data mining system, a state vector machine (SVM), a decision tree learning system, an association rule learning system, a deep learning system, an inductive logic programming system, a genetic algorithm, or a combination thereof.

2. The system of claim 1, wherein the vehicle system comprises an agricultural vehicle towing an agricultural implement and the agricultural implement is configured for agricultural operations on the field, and wherein the autonomous vehicle is configured to apply the autonomous vehicle plan for performing the agricultural operations.

3. The system of claim 1, comprising, deriving an order of operations for the one or more partitions of the field.

4. The system of claim 1, wherein the one or more partitions of the field comprise one or more headland partitions in the field, one or more interior field partitions, or a combination thereof, and wherein each of the one or more interior field partitions comprise an orientation of rows within each of the one or more field partitions.

5. The system of claim 1, wherein the processor is configured to derive one or more operational features, one or more driving features, one or more operating patterns, or a combination thereof, via the learning system based on the vehicle system data.

6. The system of claim 5, wherein the operational features comprise agricultural equipment settings for each of the one or more partitions, wherein the driving features comprise a plurality of rows for each of the one or more partitions, a direction of travel for each of the plurality of rows, a spacing between each of the plurality of rows, a preferred route around an obstacle in the field, or a combination thereof, and wherein the operating patterns comprise a one-way pattern, a gathering pattern, a casting pattern, a spiral pattern, a zig zag pattern, or a combination thereof.

7. The system of claim 1, comprising a user interface system configured to receive a drawing input from a user, wherein the processor is configured to derive the one or more partitions of a field based on the drawing input via the learning system;

and to derive the one or more turn features representative of vehicle turns in the field based on the drawing input via the learning system.

8. The system of claim 1, comprising a communications system communicatively coupled to the processor, wherein the processor is configured to receive the vehicle system data transmitted during operations of a vehicle in the field.

9. A method, comprising:

deriving one or more partitions of a field based on a vehicle system data via a learning system;

deriving one or more turn features representative of vehicle turns in the field based on the vehicle system via the learning system;

deriving an autonomous vehicle plan based on the partitions of the field and the turn features via a planning system, wherein the autonomous vehicle plan comprises a planned route of the autonomous vehicle in the field, wherein the learning system comprises a forward-chained expert system, a backward-chained expert system, a neural network, a data mining system, a state vector machine (SVM), a decision tree learning system, an association rule learning system, a deep learning system, an inductive logic programming system, a genetic algorithm, or a combination thereof.

10. The method of claim 9, wherein the vehicle system comprises an agricultural vehicle towing an agricultural implement and the agricultural implement is configured for agricultural operations on the field, and wherein the autonomous vehicle is configured to apply the autonomous vehicle plan for performing the agricultural operations.

11. The method of claim 9 comprising, transmitting, from a vehicle in the field, the vehicle system data during operations of the vehicle in the field.

12. The method of claim 9, comprising deriving an order of operations for the one or more partitions of the field, wherein the one or more partitions of the field comprise one or more headland partitions in the field, one or more interior field partitions, or a combination thereof, and wherein each of the one or more interior field partitions comprise an orientation of rows within each of the one or more field partitions.

13. The method of claim 9, comprising deriving one or more operational features, one or more driving features, one or more operating patterns, or a combination thereof, via the learning system based on the vehicle system data.

14. The method of claim 13, wherein the operational features comprise agricultural equipment settings for each of the one or more partitions, wherein the driving features comprise a plurality of rows for each of the one or more partitions, a direction of travel for each of the plurality of rows, a spacing between each of the plurality of rows, a preferred route around an obstacle in the field, or a combination thereof, and wherein the operating patterns comprise a one-way pattern, a gathering pattern, a casting pattern, a spiral pattern, a zig zag pattern, or a combination thereof.

15. A non-transitory, computer readable medium comprising instructions that when executed by a processor cause the processor to:

derive one or more partitions of a field based on a vehicle system data via a learning system;

derive one or more turn features representative of vehicle turns in the field based on the vehicle system via the learning system;

derive an autonomous vehicle plan based on the partitions of the field and the turn features via a planning system, wherein the autonomous vehicle plan comprises a planned route of the autonomous vehicle in the field, wherein the learning system comprises a forward-chained expert system, a backward-chained expert system, a neural network, a data mining system, a state vector machine (SVM), a decision tree learning system, an association rule learning system, a deep learning system, an inductive logic programming system, a genetic algorithm, or a combination thereof.

16. The non-transitory, computer readable medium of claim 15, wherein the vehicle system comprises an agricultural vehicle towing an agricultural implement and the agricultural implement is configured for agricultural operations on the field, and wherein the autonomous vehicle is configured to apply the autonomous vehicle plan for performing the agricultural operations.

17. The non-transitory, computer readable medium of claim 15, comprising instructions that when executed by the processor, cause the processor to receive, from a vehicle in the field, the vehicle system data during operations of the vehicle in the field.

18. The non-transitory, computer readable medium of claim 15, comprising instructions that when executed by the processor, cause the processor to derived one or more operational features, one or more driving features, one or more operating patterns, or a combination thereof, via the learning system based on the vehicle system data.

19. The non-transitory, computer readable medium of claim 18, wherein the operational features comprise agricultural equipment settings for each of the one or more partitions, wherein the driving features comprise a plurality of rows for each of the one or more partitions, a direction of travel for each of the plurality of rows, a spacing between each of the plurality of rows, a preferred route around an obstacle in the field, or a combination thereof, and wherein the operating patterns comprise a one-way pattern, a gathering pattern, a casting pattern, a spiral pattern, a zig zag pattern, or a combination thereof.