Online machine learning for determining soil parameters

1. A method comprising:

accessing data representative of historical movements of tools of one earth moving vehicle (EMV) through a material;

generating a training set of data based on the accessed data, the training set of data describing one or more parameters of the material based on, for each tool, a speed and kinematic pressure associated with the tool as the tool moves through the material; and

training a machine-learning model using the generated training set of data, the machine-learning model configured to output a target movement of the tool of the target EMV based on a predicted set of current parameters of a current material that the tool is moving through and one or more of: a set of movements performed by the tool within a preceding time interval and movements performed by tools of other EMVs.

2. The method of claim 1 , wherein the one or more parameters of the material comprise one or more of: an expansion parameter, a cohesion parameter, a density parameter, and a traction level.

3. The method of claim 1 , wherein the one or more parameters of the material comprise a resistance factor indicating a resistance of the material to a tool being dragged through the material.

4. The method of claim 1 , wherein the material comprises one or more of: dirt, soil, gravel, construction aggregate, rubble, concrete, construction supplies, waste, and debris.

5. The method of claim 1 , further comprising:

determining, by the EMV, a current speed of the tool as the tool moves through the material and a current kinematic pressure exerted on the tool by the material as the tool moves through the material;

inputting the current speed and the current kinematic pressure to the trained machine-learning model to determine a target movement of the tool.

6. The method of claim 5 , wherein the current speed of the tool is recorded by one or more sensors of the EMV.

7. The method of claim 5 , wherein the current kinematic pressure is determined based on a magnitude of a control input applied to the EMV.

8. The method of claim 1 , further comprising:

measuring a final position of the tool at a conclusion of the target movement of the tool; and

retraining the machine-learning model based on the measured final position of the tool.

9. A non-transitory computer-readable storage medium storing executable instructions that, when executed by a hardware processor, cause the hardware processor to:

access data representative of historical movements of tools of one earth moving vehicle (EMV) through a material;

generate a training set of data based on the accessed data, the training set of data describing one or more parameters of the material based on, for each tool, a speed and kinematic pressure associated with the tool as the tool moves through the material; and

train a machine-learning model using the generated training set of data, the machine-learning model configured to output a target movement of the tool of the target EMV based on a predicted set of current parameters of a current material that the tool is moving through and one or more of: a set of movements performed by the tool within a preceding time interval and movements performed by tools of other EMVs.

10. The non-transitory computer-readable storage medium of claim 9 , wherein the one or more parameters of the material comprise one or more of: an expansion parameter, a cohesion parameter, a density parameter, and a traction level.

11. The non-transitory computer-readable storage medium of claim 9 , wherein the one or more parameters of the material comprise a resistance factor indicating a resistance of the material to a tool being dragged through the material.

12. The non-transitory computer-readable storage medium of claim 9 , wherein the material comprises one or more of: dirt, soil, gravel, construction aggregate, rubble, concrete, construction supplies, waste, and debris.

13. The non-transitory computer-readable storage medium of claim 9 , further comprising instructions that cause the processor to:

determine, by the EMV, a current speed of the tool as the tool moves through the material and a current kinematic pressure exerted on the tool by the material as the tool moves through the material;

input the current speed and the current kinematic pressure to the trained machine-learning model to determine a target movement of the tool.

14. The non-transitory computer-readable storage medium of claim 13 , wherein the current speed of the tool is recorded by one or more sensors of the EMV.

15. The non-transitory computer-readable storage medium of claim 13 , wherein the current kinematic pressure is determined based on a magnitude of a control input applied to the EMV.

16. The non-transitory computer-readable storage medium of claim 9 , further comprising instructions that cause the processor to:

measure a final position of the tool at a conclusion of the target movement of the tool; and

retrain the machine-learning model based on the measured final position of the tool.

17. A method comprising:

accessing data representative of historical movements of tools of one earth moving vehicle (EMV) through a material;

accessing a machine-learning model, wherein the machine-learning model is trained using a training set of data generated based on the accessed data, the training set of data describing parameters of the material based on a speed and kinematic pressure associated with a tool as the tool moves through the material and one or more of: a set of movements performed by the tool within a preceding time interval and movements performed by tools of other EMVs; and

inputting a set of current parameters of a current material that the tool is moving through to the machine-learning model to output a target movement of a tool of a target EMV.

18. The method of claim 17 , wherein the one or more parameters of the material comprise one or more of: an expansion parameter, a cohesion parameter, a density parameter, and a traction level.

19. The method of claim 17 , wherein the one or more parameters of the material comprise a resistance factor indicating a resistance of the material to a tool being dragged through the material.

20. The method of claim 17 , wherein the material comprises one or more of: dirt, soil, gravel, construction aggregate, rubble, concrete, construction supplies, waste, and debris.