Estimation of terramechanical properties
A system for estimating tire parameters for an off-road vehicle in real time, the system including a processing circuit including a processor and memory, the memory having instructions stored thereon that, when executed by the processor, cause the processing circuit to measure a position of the vehicle at a first time, determine, based on the position, motion characteristics of the vehicle, predict, based on the motion characteristics, a position of the vehicle at a second time, measure a position of the vehicle at the second time, and generate a tire parameter associated with the vehicle based on the predicted position and the measured position of the vehicle at the second time.
1. A system for estimating tire parameters for an off-road vehicle in real time, the system comprising:
a processing circuit including a processor and memory, the memory having instructions stored thereon that, when executed by the processor, cause the processing circuit to:
measure a position of the vehicle at a first time;
determine, based on the position, motion characteristics of the vehicle;
predict, based on the motion characteristics, a position of the vehicle at a second time;
measure a position of the vehicle at the second time;
determine a correction factor based on the difference between the predicted position and the measured position of the vehicle at the second time; and
determine a tire parameter associated with the vehicle based on the correction factor, wherein the correction factor includes at least one of a slip angle, a tire stiffness, or a cornering stiffness.
2. The system of claim 1 , wherein the tire parameter is a cornering stiffness.
3. The system of claim 1 , wherein the tire parameter is a tire type.
4. The system of claim 1 , wherein the correction factor is associated with an amount of tire slip associated with the difference between the predicted position and the measured position of the vehicle at the second time.
5. The system of claim 4 , wherein determining the tire parameter includes adjusting the correction factor to account for the difference between the predicted position and the measured position of the vehicle at the second time, wherein the adjusted correction factor is the tire parameter.
6. The system of claim 4 , wherein the difference between the predicted position and the measured position of the vehicle at the second time includes two or more parameters associated with the vehicle position and wherein the method includes weighting each of the two or more parameters based on a contribution each of the two or more parameters make to the difference between the predicted position and the measured position of the vehicle at the second time.
7. The system of claim 1 , wherein the vehicle is an agricultural vehicle.
8. The system of claim 1 , wherein measuring the position of the vehicle at the first and second times includes receiving position information from a GPS receiver associated with the vehicle.
9. The system of claim 1 , wherein the tire parameter is determined further based on vehicle characteristics associated with the vehicle.
10. The system of claim 1 , wherein the processing circuit is further configured to control an operation of the vehicle based on the tire parameter.
11. The system of claim 1 , wherein correction factor is determined based on the comparison of a yaw rate, the inertial heading, the lateral speed of the body frame, and the lateral speed of the off-road vehicle.
12. A method of estimating tire parameters for an off-road vehicle in real time, the method comprising:
measuring a position of the vehicle at a first time;
determining, based on the position, motion characteristics associated with the vehicle;
predicting, based on the motion characteristics, a position of the vehicle at a second time;
measuring a position of the vehicle at the second time;
determining a correction factor based on the difference between the predicted position and the measured position of the vehicle at the second time; and
determining a tire parameter associated with the vehicle based on the correction factor, wherein the correction factor includes at least one of a slip angle, a tire stiffness, or a cornering stiffness.
13. The method of claim 12 , wherein the tire parameter is a cornering stiffness.
14. The method of claim 12 , wherein the tire parameter is a tire type.
15. The method of claim 12 , wherein the correction factor is associated with an amount of tire slip associated with the difference between the predicted position and the measured position of the vehicle at the second time.
16. The method of claim 15 , wherein determining the tire parameter includes adjusting the correction factor to account for the difference between the predicted position and the measured position of the vehicle at the second time, wherein the adjusted correction factor is the tire parameter.
17. The method of claim 15 , wherein the difference between the predicted position and the measured position of the vehicle at the second time includes two or more parameters associated with the vehicle position and wherein the method includes weighting each of the two or more parameters based on a contribution each of the two or more parameters make to the difference between the predicted position and the measured position of the vehicle at the second time.
18. The method of claim 12 , wherein the vehicle is an agricultural vehicle.
19. The method of claim 12 , wherein correction factor is determined based on the comparison of a yaw rate, the inertial heading, the lateral speed of the body frame, and the lateral speed of the off-road vehicle.
20. An agricultural vehicle having one or more tires and a vehicle control system including a processor and memory, the memory having instructions stored thereon that, when executed by the processor, cause the processor to:
receive a position measurement associated with the agricultural vehicle at a first time;
determine, based on the position, motion characteristics associated with the agricultural vehicle;
generate, based on the motion characteristics, a predicted position of the agricultural vehicle at a second time;
measure a position of the agricultural vehicle at the second time;
determine a correction factor based on the difference between the predicted position and the measured position of the vehicle at the second time; and
determine a cornering stiffness associated with at least one of the one or more tires based on the correction factor, wherein the correction factor includes at least one of a slip angle, a tire stiffness, or a cornering stiffness.
21. A method of operating an off-road vehicle in real time, the method comprising:
measuring a first position of the off-road vehicle at a first time;
determining, based on the first position, motion characteristics associated with the off-road vehicle;
predicting, based on the motion characteristics, a second position of the off-road vehicle at a second time;
measuring a third position of the off-road vehicle at the second time;
determining a correction factor based on a difference between the second position and the third position of the off-road vehicle at the second time;
determining a tire parameter associated with the off-road vehicle based on the correction factor; and
adjusting a steering angle of the off-road vehicle based in part on the determined tire parameter.
22. The method of claim 21 , wherein the correction factor includes at least one of a slip angle, a tire stiffness, or a cornering stiffness.
23. The method of claim 21 , wherein the method further comprises:
continuously determining the correction factor; and
displaying the determined correction factor to a vehicle operator in real time.
24. The method of claim 21 , wherein the method further comprises:
training a neural network using a dataset of vehicle motion corresponding to different vehicle parameters; and
determining the correction factor through machine learning using the neural network.
25. The method of claim 21 , wherein the correction factor is associated with an amount of tire slip associated with the difference between the second position and the third position of the off-road vehicle at the second time.
26. The method of claim 25 , wherein determining the tire parameter includes adjusting the correction factor to account for the difference between the second position and the third position of the off-road vehicle at the second time, wherein the adjusted correction factor is the tire parameter.
27. The method of claim 25 , wherein the difference between the second position and the third position of the off-road vehicle at the second time includes two or more parameters associated with the third position and wherein the method further includes weighting each of the two or more parameters based on a contribution each of the two or more parameters make to the difference between the second position and the third position of the off-road vehicle at the second time.
28. The method of claim 21 , wherein the off-road vehicle is an agricultural vehicle.
29. A system for autonomously controlling a vehicle in real time, the system comprising:
a processing circuit including a processor and memory, the memory having instructions stored thereon that, when executed by the processor, cause the processing circuit to:
measure a first position of the vehicle at a first time;
determine, based on the first position, motion characteristics associated with the vehicle;
predict, based on the motion characteristics, a second position of the vehicle at a second time;
measure a third position of the vehicle at the second time;
determine a correction factor based on a difference between the second position and the third position of the vehicle at the second time;
determine a tire parameter associated with the vehicle based on the correction factor; and
autonomously control operation of the vehicle by adjusting autonomous steering control signals based on the tire parameter.
30. The system of claim 29 , wherein the correction factor includes at least one of a slip angle, a tire stiffness, or a cornering stiffness.
31. The system of claim 30 , wherein the instructions further cause the processing circuit to:
continuously determine the correction factor; and
display the determined correction factor to a vehicle operator in real time.
32. The system of claim 29 , wherein the instructions further cause the processing circuit to:
train a neural network using a dataset of vehicle motion corresponding to different vehicle parameters; and
determine the correction factor through machine learning using the neural network.
33. The system of claim 29 , wherein the correction factor is associated with an amount of tire slip associated with the difference between the second position and the third position of the vehicle at the second time.
34. The system of claim 29 , wherein measuring the first position and the third position of the vehicle at the first and second times includes receiving position information from a GPS receiver associated with the vehicle.
35. The system of claim 29 , wherein the vehicle is an agricultural vehicle.
36. The system of claim 29 , wherein the tire parameter is generated further based on vehicle characteristics associated with the vehicle.
37. An agricultural vehicle having one or more tires and a vehicle control system including a processor and memory, the memory having instructions stored thereon that, when executed by the processor, cause the processor to:
measure a first position of the agricultural vehicle at a first time;
determine, based on the first position, motion characteristics associated with the agricultural vehicle;
predict, based on the motion characteristics, a second position of the agricultural vehicle at a second time;
measure a third position of the agricultural vehicle at the second time;
determine a correction factor based on a difference between the second position and the third position of the agricultural vehicle at the second time;
determine a tire parameter associated with the agricultural vehicle based on the correction factor; and
autonomously control operation of the agricultural vehicle by adjusting autonomous steering control signals based on the tire parameter.
38. The agricultural vehicle of claim 37 , wherein the instructions further cause the processor to:
continuously determine the correction factor; and
display the determined correction factor to a vehicle operator in real time.
39. The agricultural vehicle of claim 37 , wherein the instructions further cause the processor to:
train a neural network using a dataset of vehicle motion corresponding to different vehicle parameters; and
determine the correction factor through machine learning using the neural network.