Caterpillar AHS on 775 trucks with Luck Stone: haulage design notes for quarries
Reviewed by Tom Sullivan
First reported on International Mining – News
30 Second Briefing
Caterpillar is extending its MineStar Command for hauling system with Luck Stone from the Bull Run Quarry to two additional Virginia sites and, for the first time, onto Cat 775 haul trucks. At Bull Run, a fleet of autonomous rigid trucks has already moved more than 3.5 million tons since November 2024, following an 18‑month ramp‑up. The deployment on mid‑size 775s signals AHS moving from large open‑pit fleets into smaller quarry-scale operations, with implications for traffic management, loading unit integration and mixed‑fleet haul road design.
Technical Brief
- Luck Stone’s autonomous programme remains focused on quarry-scale aggregates, not large greenfield metal mines.
- Integration at the two new Virginia sites must account for existing brownfield plant, crushers and stockpile layouts.
- Mixed-mode operations will require interaction protocols between autonomous trucks, manned loaders and ancillary light vehicles.
- Haul road design will need tighter control on berms, crossfall and delineation to satisfy autonomous pathing.
- Communications backbone at the new quarries must support continuous truck localisation and low-latency command traffic.
- Quarry-scale autonomous hauling on mid-size trucks opens retrofit potential for similar aggregates operations with constrained benches and short hauls.
Our Take
Caterpillar’s move to put AHS on 775-class trucks at Luck Stone’s Bull Run Quarry in Virginia aligns with its wider autonomy push seen in the FieldAI partnership, signalling that mid‑size quarry fleets are now a key test bed alongside large open‑pit mines.
More than 18 months of autonomous hauling and 3.5 Mt moved at Bull Run give Caterpillar a rare, data‑rich reference case in the aggregates sector, which operators at other US quarries can use to benchmark cycle times, staffing models and retrofit risk for similar 775 fleets.
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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