Implement-on-ground detection using vibration signals

Patent metadata

Field	Value
Publication number	US-12332270-B2
Application number	US-202117493793-A
Country	US
Kind code	B2
Filing date	Oct 4, 2021
Priority date	Oct 4, 2021
Publication date	Jun 17, 2025
Grant date	Jun 17, 2025

Abstract

Described herein are systems, methods, and other techniques for determining a period during which an implement of a construction machine is interacting with a ground surface. A vibration signal that is indicative of a movement of the implement is captured. One or more features are extracted from the vibration signal. The one or more features are provided to a machine-learning model to generate a model output. An implement-on-ground (IOG) start time and an IOG end time are predicted based on the model output, the IOG start time and the IOG end time forming the period.

First claim

What is claimed is:

1. A method of determining a period during which an implement of a construction machine is interacting with a ground surface, the method comprising:
capturing a vibration signal that is indicative of a movement of the implement;
extracting one or more features from the vibration signal;
providing the one or more features to a machine-learning model to generate a model output, the model output including a set of implement-on-ground (IOG) candidates corresponding to times at which the implement is interacting with the ground surface and a set of implement-in-air (IIA) candidates corresponding to times at which the implement is not interacting with the ground surface;
predicting an IOG start time based on a transition between a first cluster of the set of IIA candidates and a cluster of the set of IOG candidates and an IOG end time based on a transition between the cluster of the set of IOG candidates and a second cluster of the set of IIA candidates, the IOG start time and the IOG end time forming the period during which the implement is interacting with the ground surface;
adjusting a ground surface map based on the period during which the implement is interacting with the ground surface and a path of the implement; and
generating control signals based on the ground surface map and sending the control signals to actuators of the construction machine to cause movement of the construction machine.

2. The method of claim 1 , wherein the vibration signal is captured using a vibration sensor mounted to the construction machine.

3. The method of claim 2 , wherein the vibration sensor includes an accelerometer and the vibration signal includes an acceleration signal.

4. The method of claim 2 , wherein the vibration sensor includes a gyroscope and the vibration signal includes a rotation signal.

5. The method of claim 2 , wherein the vibration sensor is mounted to the implement.

6. The method of claim 1 , wherein the one or more features include at least one of signal amplitude features or signal frequency features.

7. The method of claim 1 , wherein the machine-learning model is a pre-trained support-vector machine.

8. A system comprising:
one or more processors; and
a computer-readable medium comprising instructions that, when executed by the one or more processors, cause the one or more processors to:
capture a vibration signal that is indicative of a movement of an implement of a construction machine;
extract one or more features from the vibration signal;
provide the one or more features to a machine-learning model to generate a model output, the model output including a set of implement-on-ground (IOG) candidates corresponding to times at which the implement is interacting with a ground surface and a set of implement-in-air (IIA) candidates corresponding to times at which the implement is not interacting with the ground surface;
predict an IOG start time based on a transition between a first cluster of the set of IIA candidates and a cluster of the set of IOG candidates and an IOG end time based on a transition between the cluster of the set of IOG candidates and a second cluster of the set of IIA candidates, the IOG start time and the IOG end time forming a period during which the implement is interacting with the ground surface;
adjust a ground surface map based on the period during which the implement is interacting with the ground surface and a path of the implement; and
generate control signals based on the ground surface map and send the control signals to actuators of the construction machine to cause movement of the construction machine.

9. The system of claim 8 , wherein the vibration signal is captured using a vibration sensor mounted to the construction machine.

10. The system of claim 9 , wherein the vibration sensor includes an accelerometer and the vibration signal includes an acceleration signal.

11. The system of claim 9 , wherein the vibration sensor is mounted to the implement.

12. The system of claim 8 , wherein the one or more features include at least one of signal amplitude features or signal frequency features.

13. The system of claim 8 , wherein the machine-learning model is a pre-trained support-vector machine.

14. The system of claim 8 , wherein the IOG start time and the IOG end time are predicted further based on a set of implement positions.

15. A non-transitory computer-readable medium comprising instructions that, when executed by one or more processors, cause the one or more processors to perform operations for determining a period during which an implement of a construction machine interacts with a ground surface, the operations comprising:
capturing a vibration signal that is indicative of a movement of the implement;
extracting one or more features from the vibration signal;
providing the one or more features to a machine-learning model to generate a model output, the model output including a set of implement-on-ground (IOG) candidates corresponding to times at which the implement is interacting with the ground surface and a set of implement-in-air (IIA) candidates corresponding to times at which the implement is not interacting with the ground surface; and
predicting an IOG start time based on a transition between a first cluster of the set of IIA candidates and a cluster of the set of IOG candidates and an IOG end time based on a transition between the cluster of the set of IOG candidates and a second cluster of the set of IIA candidates, the IOG start time and the IOG end time forming the period during which the implement is interacting with the ground surface;
adjusting a ground surface map based on the period during which the implement is interacting with the ground surface and a path of the implement; and
generating control signals based on the ground surface map and sending the control signals to actuators of the construction machine to cause movement of the construction machine.

16. The non-transitory computer-readable medium of claim 15 , wherein the vibration signal is captured using a vibration sensor mounted to the construction machine.

17. The non-transitory computer-readable medium of claim 16 , wherein the vibration sensor includes an accelerometer and the vibration signal includes an acceleration signal.

18. The non-transitory computer-readable medium of claim 16 , wherein the vibration sensor includes a gyroscope and the vibration signal includes a rotation signal.

19. The non-transitory computer-readable medium of claim 16 , wherein the vibration sensor is mounted to the implement.

Assignees

• Caterpillar Trimble Control Tech Llc

Inventors

• Jun Jung Hyun
• France Peter
• Baker Alistair
• Daniell Colin
• Greenstreet Alan
• Wyn-Harris Jeremy

Classifications

• G05B13/048

  using a predictor · CPC title

• G05B13/0265

  the criterion being a learning criterion · CPC title

• G01C19/00

  Gyroscopes; Turn-sensitive devices using vibrating masses; Turn-sensitive devices without moving masses; Measuring angular rate using gyroscopic effects · CPC title

• E02F9/262

  with follow-up actions to control the work tool, e.g. controller · CPC title

• E02F3/435

  for dipper-arms, backhoes or the like · CPC title