Sandvik AutoMine Aura: integrated fleet automation explained for mine engineers
Reviewed by Tom Sullivan
First reported on International Mining – News
30 Second Briefing
Sandvik has launched AutoMine® Aura, a new underground mining automation platform described as the biggest upgrade to AutoMine® in over 20 years and engineered to provide full situational awareness across fleets. The system overhauls every layer of the existing AutoMine® stack, integrating data from multiple sensors and machines into a unified control environment to support autonomous loading, hauling and drilling. For mine operators, the platform signals a shift towards more tightly integrated, multi-equipment automation architectures that depend on robust underground communications and high-fidelity environmental modelling.
Technical Brief
- Sandvik describes the platform as "first-of-its-kind", indicating a new architecture rather than incremental add-ons.
- Sandvik frames Aura as "fully engineered for the future", implying hardware–software modularity for later capability drops.
- Full situational awareness focus points to heavy reliance on underground digital twins and persistent localisation.
- For multi-vendor mines, Aura’s stack-level redesign raises integration questions with legacy OEM and in-house systems.
- Rollout will likely drive upgrades to mine-wide communications, time synchronisation and sensor calibration workflows.
Our Take
The 2024 acquisition of Universal Field Robots, already being folded into the AutoMine® ecosystem, suggests Aura is likely designed to accommodate not just Sandvik loaders and trucks but also third-party or non-traditional robotic carriers, which matters for mines looking to automate mixed or legacy fleets.
Sandvik’s Digital Mining Technologies division, highlighted in a 2026-05-14 article, gives Aura an organisational home that can integrate it with mine-wide optimisation and interoperability initiatives, signalling that this is intended as a platform-level control layer rather than a point solution for a single machine class.
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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