Digging for Data: Experiments in Rock Pile Characterization Using Only Proprioceptive Sensing in Excavation

2026-02-11

AI Summary

This study addresses the challenge of accurately estimating particle size distribution in fragmented rock piles within mining and quarrying operations without relying on external sensors. The authors propose a vision-free estimation method that leverages only onboard perception signals from a wheel loader. By applying wavelet analysis to extract dynamic response features from the loader’s motion and constructing an inertial response model, the approach infers the relative particle size of rock piles through feature ratios. Notably, this is the first method to enable assessment of fragmentation degree without external sensing modalities such as cameras or LiDAR. Full-scale excavation experiments conducted at an active quarry demonstrate strong agreement between the proposed method’s estimates and those obtained from visual analysis and sieve testing, confirming its effectiveness and practical utility.

Technology Category

• Intelligent Robots: State Estimation
• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Computer Vision: Motion & Tracking

Application Category

• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Security and Privacy: Large-scale security measurements
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Characterization of fragmented rock piles is a fundamental task in the mining and quarrying industries, where rock is fragmented by blasting, transported using wheel loaders, and then sent for further processing. This field report studies a novel method for estimating the relative particle size of fragmented rock piles from only proprioceptive data collected while digging with a wheel loader. Rather than employ exteroceptive sensors (e.g., cameras or LiDAR sensors) to estimate rock particle sizes, the studied method infers rock fragmentation from an excavator's inertial response during excavation. This paper expands on research that postulated the use of wavelet analysis to construct a unique feature that is proportional to the level of rock fragmentation. We demonstrate through extensive field experiments that the ratio of wavelet features, constructed from data obtained by excavating in different rock piles with different size distributions, approximates the ratio of the mean particle size of the two rock piles. Full-scale excavation experiments were performed with a battery electric, 18-tonne capacity, load-haul-dump (LHD) machine in representative conditions in an operating quarry. The relative particle size estimates generated with the proposed sensing methodology are compared with those obtained from both a vision-based fragmentation analysis tool and from sieving of sampled materials.

Problem

• rock fragmentation
• particle size estimation
• proprioceptive sensing
• excavation
• wheel loader

Innovation

• proprioceptive sensing
• rock fragmentation characterization
• wavelet analysis
• excavation dynamics
• particle size estimation