Material identification using vibration signals

Patent metadata

Field	Value
Publication number	US-12546752-B2
Application number	US-202217985371-A
Country	US
Kind code	B2
Filing date	Nov 11, 2022
Priority date	Nov 18, 2021
Publication date	Feb 10, 2026
Grant date	Feb 10, 2026

Abstract

Described herein are systems, methods, and other techniques for determining a material type while 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. The material type of the ground surface is predicted based on the model output.

First claim

What is claimed is:

1 . A computer-implemented method of determining a material type while an implement of a construction machine is interacting with a ground surface, the computer-implemented method comprising:
causing a movement of the implement using one or more control signals; capturing a vibration signal that is indicative of the movement of the implement; extracting one or more features from the vibration signal; estimating one or more positions of the implement during the movement of the implement; providing the one or more features and the one or more positions of the implement to a machine-learning model to generate a model output; and predicting the material type of the ground surface based on the model output.

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

3 . The computer-implemented method of claim 2 , wherein the vibration sensor includes one or both of an accelerometer or a gyroscope, and wherein the vibration signal includes one or both of an acceleration signal or a rotation signal.

4 . The computer-implemented method of claim 2 , wherein the vibration sensor is mounted to the implement.

5 . The computer-implemented method of claim 1 , wherein the one or more features include one or both of signal amplitude features or signal frequency features.

6 . The computer-implemented method of claim 1 , wherein the machine-learning model is a pre-trained artificial recurrent neural network, a feed-forward neural network, or a support-vector machine.

7 . The computer-implemented method of claim 1 , further comprising:
predicting a first material type of a first portion of the ground surface based on the model output; and predicting a second material type of a second portion of the ground surface based on the model output.

8 . The computer-implemented method of claim 1 , further comprising:
predicting a location of a boundary between a first material type of a first portion of the ground surface and a second material type of a second portion of the ground surface based on the model output.

9 . A system for determining a material type while an implement of a construction machine is interacting with a ground surface, the 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 perform operations comprising:
causing a movement of the implement using one or more control signals;
capturing a vibration signal that is indicative of the movement of the implement;
extracting one or more features from the vibration signal;
estimating one or more positions of the implement during the movement of the implement;
providing the one or more features and the one or more positions of the implement to a machine-learning model to generate a model output; and
predicting the material type of the ground surface based on the model output.

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

11 . The system of claim 10 , wherein the vibration sensor includes one or both of an accelerometer or a gyroscope, and wherein the vibration signal includes one or both of an acceleration signal or a rotation signal.

12 . The system of claim 10 , wherein the vibration sensor is mounted to the implement.

13 . The system of claim 9 , wherein the one or more features include one or both of signal amplitude features or signal frequency features.

14 . The system of claim 9 , wherein the machine-learning model is a pre-trained artificial recurrent neural network, a feed-forward neural network, or a support-vector machine.

15 . The system of claim 9 , wherein the operations further comprise:
predicting a first material type of a first portion of the ground surface based on the model output; and predicting a second material type of a second portion of the ground surface based on the model output.

16 . The system of claim 9 , wherein the operations further comprise:
predicting a location of a boundary between a first material type of a first portion of the ground surface and a second material type of a second portion of the ground surface based on the model output.

17 . 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 material type while an implement of a construction machine is interacting with a ground surface, the operations comprising:
causing a movement of the implement using one or more control signals; capturing a vibration signal that is indicative of the movement of the implement; extracting one or more features from the vibration signal; estimating one or more positions of the implement during the movement of the implement; providing the one or more features and the one or more positions of the implement to a machine-learning model to generate a model output; and predicting the material type of the ground surface based on the model output.

18 . The non-transitory computer-readable medium of claim 17 , wherein the vibration signal is captured using a vibration sensor mounted to the construction machine.

19 . The non-transitory computer-readable medium of claim 18 , wherein the vibration sensor includes one or both of an accelerometer or a gyroscope, and wherein the vibration signal includes one or both of an acceleration signal or a rotation signal.

20 . The non-transitory computer-readable medium of claim 18 , wherein the vibration sensor is mounted to the implement.

Assignees

• Caterpillar Trimble Control Tech Llc

Inventors

• Jun Jung Hyun
• France Peter

Classifications

• G01N29/14

  using acoustic emission techniques {(echo of particles G01N29/046; measuring mechanical vibrations or acoustic waves in solids in general G01H1/00)} · CPC title

• G01N29/44

  Processing the detected response signal {, e.g. electronic circuits specially adapted therefor (digital signal processing per se G06F17/00)} · CPC title

• G01N29/24

  Probes {(transducers for acoustic waves B06B, G10K; for measuring G01H)} · CPC title

• G06N3/08

  Learning methods · CPC title

• E02F9/261

  Surveying the work-site to be treated · CPC title