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

TL;DR

This study introduces a novel proprioceptive sensing method using wavelet analysis of inertial data to estimate rock pile fragmentation, eliminating the need for external sensors in quarry operations.

Contribution

The paper presents a new technique that infers rock particle size from excavator inertial responses, validated through extensive field experiments in real quarry conditions.

Findings

Wavelet features correlate with rock particle size ratios.

Method achieves comparable accuracy to vision-based analysis.

Proprioceptive sensing reduces reliance on external sensors.

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.