Machine Vision Boosts Ore Transport in Russia
ALROSA has introduced an AI-powered system that increases ore transport efficiency by up to 10 percent, marking a major step in the digital transformation of Russia’s mining industry.
Russian mining giant ALROSA has completed successful trials of a unique truck-loading control system based on machine vision and artificial intelligence. Pilot projects at the Udachny underground mine and the Yubileyny open-pit mine demonstrated a productivity increase of up to 10 percent, the company’s press service reported.
Traditional visual methods of estimating the volume of transported rock made it impossible to control and manage loading operations in real time. This often led to underloading of trucks by an average of 10 percent, reducing overall logistics efficiency at mining sites.
The new Russian system changes this entirely. It automatically scans each haul truck body using LiDAR (laser radar) to create an accurate 3D model. A specially trained neural network analyzes the data in real time, precisely calculating the volume of rock and the truck’s load level. All information is instantly transmitted to a central dispatch center, where an operator can direct loading equipment to make immediate adjustments.
Tests conducted at one of Udachny’s ore passes boosted haul truck productivity by 10 percent—equal to nearly 12,000 additional tons of diamond-bearing ore transported.
Earlier, ALROSA reported using artificial intelligence to identify new diamond deposits. The company is also actively integrating neural networks into other core processes, from transportation safety to exploration, enrichment, and sales.
