Epiroc 3D LiDAR truck automation: design and safety notes for mine planners
Reviewed by Tom Sullivan
First reported on International Mining – News
30 Second Briefing
Epiroc is extending its Deep Automation system so autonomous underground haul trucks can also operate on surface haul roads using 3D LiDAR for localisation, mapping and obstacle detection instead of relying on GPS. The upgrade allows trucks to transition directly from stope loading to surface tipping points in a single autonomous cycle, removing manual handover at portals and ramps. For mine planners and engineers, this enables integrated underground–surface haulage designs and demands tighter control of surface road geometry, berms and traffic segregation for sensor reliability.
Technical Brief
- 3D LiDAR point clouds are used as the primary localisation reference instead of satellite-based positioning.
- Deep Automation integrates LiDAR mapping with truck control, so path planning updates continuously from sensor data.
- Obstacle detection uses LiDAR range and reflectivity, enabling classification of berms, vehicles, personnel and loose rock.
- Surface operation logic is embedded in the same control stack used underground, avoiding dual software configurations.
Our Take
Epiroc’s use of 3D LiDAR on underground trucks fits a broader push in our database toward automation across its fleet, alongside systems like the Automatic Bit Changer now being rolled out to large DTH crawler rigs.
With Epiroc recently supplying major underground fleets to operations like MMG’s Khoemacau copper mine, LiDAR-enabled surface operation of underground trucks is likely to appeal to mines looking to centralise control rooms and reduce operator exposure in deep or geotechnically complex stopes.
The combination of advanced sensing (3D LiDAR) and Epiroc’s growing aftermarket and retrofit capabilities, reinforced by its acquisition of Eventspec in Johannesburg, suggests a pathway for upgrading existing truck fleets rather than relying solely on greenfield deployments.
Prepared by collating external sources, AI-assisted tools, and Geomechanics.io’s proprietary mining database, then reviewed for technical accuracy & edited by our geotechnical team.
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