Automatic Identification of Large Fragments in a Pile of Broken Rock Using a Time-of-Flight Camera

Abstract

This paper presents a solution to part of the problem of making robotic or semi-robotic digging equipment less dependant on human supervision. A method is described for identifying rocks of a certain size that may affect digging efficiency or require special handling. The process involves three main steps. First, by using range and intensity data from a time-of-flight (TOF) camera, a feature descriptor is used to rank points and separate regions surrounding high scoring points. This allows a wide range of rocks to be recognized because features can represent a whole or just part of a rock. Second, these points are filtered to extract only points thought to belong to the large object. Finally, a check is carried out to verify that the resultant point cloud actually represents a rock. Results are presented from field testing on piles of fragmented rock. Note to Practitioners-This paper presents an algorithm to identify large boulders in a pile of broken rock as a step towards an autonomous mining dig planner. In mining, piles of broken rock can contain large fragments that may need to be specially handled. To assess rock piles for excavation, we make use of a TOF camera that does not rely on external lighting to generate a point cloud of the rock pile. We then segment large boulders from its surface by using a novel feature descriptor and distinguish between real and false boulder candidates. Preliminary field experiments show promising results with the algorithm performing nearly as well as human test subjects.

The Fragmentation Energy-Fan Model in Quarry Blasts

“…The size distribution of blasting products can only be measured accurately enough by sieving the muckpile, a complicated, costly and disruptive to production task. Although enhanced 2D technologies (often called 3D though they are not really determining fragmentation of a rock volume) to monitor fragmentation are available, such as LiDAR imaging-laser imaging detection and ranging (McKinnon and Marshall 2014;Oñederra et al 2015;Thurley 2013;Thurley et al 2015) or photogrammetry (Noy 2013(Noy , 2015Bamford et al 2016), 2D image analysis is still the most common tool used. Image analysis systems may show a poor performance at small sizes especially when they have not been calibrated (Sanchidrián et al 2009) and it is advisable to use additional tools to correct raw data on a blast per blast basis in order to get a good estimation of the actual size distribution (Sanchidrián et al 2006).…”

An overview of current technologies and emerging trends in factory automation

“…Industrial vision systems play a role of 'electronic eyes' in factories. Besides, imaging-based automatic inspection is involved also in process control and robot guidance (McKinnon and Marshall 2014). The examples of visual inspection systems include verification of quality dimensional characteristics (e.g.…”

Towards Automatic Classification of Fragmented Rock Piles via Proprioceptive Sensing and Wavelet Analysis

“…Accurate and frequent information is needed to ensure design specifications are met and equipment is operating at optimal levels. Current rock size (fragmentation) estimation technologies use exteroceptive sensors (e.g., cameras, LiDAR) [6], [7]. Drawbacks to these sensors are that they are only able to see the surface of a rock pile, cameras require specific lighting conditions and positioning, and significant time to safely acquire the data.…”