MINING AUTOMATION
only a decade ago , a LiDAR system cost $ 50,000 , while today you can buy a LiDAR unit for $ 5,000 . In a few years , with technological advancements and higher production volumes , its cost may drop down to roughly $ 500 .”
At the same time , in recent years tremendous computing power has been brought to mining in the form of edge computing to the pit and onboard machines . “ Efforts of chip producers , from traditional ones like Intel , through new leaders like NVIDIA and Alphabet , to more specialised players like Mythic and Xilinx , are bringing tremendous amounts of computing power to vehicles . For example , NVIDIA ’ s current edge computing power champion AGX , with 30 trillion operations per second ( TOPS ), will soon lose its performance title to the NVIDIA Orin with 254 TOPS , and upcoming Atlan will provide more than 1,000 TOPS . With the cost of the edge device staying roughly the same , it will allow more computational load on a device , thus simplifying solution architecture , improving functionality and decreasing cost .”
Rapid software development in the field of decision-making algorithms , environment scanning and result interpretation also helps to create autonomous mining systems . “ The last decade has seen tremendous work , scientific and technological , put into the field of selfdriving cars . Of course , these efforts have been focused on vehicles operating on public roads , and most of the practical results cannot be transferred directly to the less structured , rough and hazardous environment of a mining pit . But advancing the autonomous sensing , path planning , situation analysis and internal diagnostics of the future driverless taxis have yielded also quite some results which can be used in developing software and systems for autonomous mining equipment , and this can lower development costs for the latter .”
Another area with outstanding progress highlighted by Yakovenko is Artificial Intelligence ( AI ). “ In ten years , AI grew from an opaque academic field of study to an almost ubiquitous wide-spread technology . It was not only due to the growth of the raw computing power – the theoretical advances and application practice resulted in the significant growth of computational efficiency of AI models and algorithms . OpenAI researchers found that the algorithmic efficiency of AI-based computer vision improved more than 40 times in seven years . These advancements allow us to implement more complex scenarios and to run a wide variety of AI methods on cost-effective hardware .”
Availability and speed of support play a crucial role in effective deployment of any technology in mining and automation can help here as well . “ Although maintenance and repair of the hardware requires ‘ old-fashioned work ,’ intelligent diagnostics integrated in the autonomous and tele-operated solutions can pinpoint the place of malfunction , and often also its cause . This can go as far as our system placing detailed support requests automatically right after equipment malfunction or even before , leading to quicker response and , ultimately , higher uptime .”
Today , almost all major mining equipment OEMs offer autonomous-ready machines , often together with controlling software , and some of them also retrofit their higher capacity range and newer models . “ This encourages third-party autonomous solutions providers to cover a wider range of equipment models for retrofitting . We at Zyfra Robotics , pursuing the concept of vendor-agnostic autonomous solutions , have already demonstrated our technology at work with equipment of many different manufacturers . Also , we participate in a number of ISO workgroups and other industry-relevant organisations to keep our solutions in line with current industry approaches and requirements . Overall , this competition is good for the mining industry , as mining operators can try several solutions and select one that fits in with all other systems already implemented on their
site .”
IM
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