The industrial exploitation of the Arctic can be likened to living on other planets in mid-20th century science fiction works. On one hand, there are advanced technologies, a robust industry and a general understanding of the working environment, but on the other hand, there is still a need for risk-taking and bravery. A truck breakdown, getting caught in a blizzard, veering into a ditch, a flat tire—and suddenly you're alone with the vast tundra, where you could spend anywhere from a few hours to a couple of days. Naturally, what makes for an exciting story is not suitable for everyday life, hence in the Arctic, tests of automated devices designed to significantly reduce human risks are underway.

One of the most ambitious projects is Gazprom's driverless trucks that travel along the 140-kilometre road between the Tazovsky settlement and the Vostochno-Messoyakhskoye deposit. The KAMAZ trucks are equipped with an autonomous satellite navigation system and numerous sensors that scan the road within a 200-metre radius. They are capable of identifying both stationary and moving obstacles, making decisions to prevent accidents, and can essentially be referred to as a self-driving car in the truest sense. So far, this solution has proven to be more successful than the concept of heavy-duty drones. Three years ago, the company was conducting tests on a heavy aircraft designed to transport approximately 150 kg of cargo. Despite successful trials, air technology in the Arctic remains an unstable means of communication due to unpredictable weather conditions and the fact that air transport costs are significantly higher than those of ground transport. It is far more sensible to equip wheeled vehicles with automated control systems, thereby significantly reducing risks for drivers. Incidentally, the software for these trucks was developed by Russian IT specialists who have a substantial involvement in Gazprom's projects.

For example, in the autumn of 2023, an intelligent system for managing production facilities involved in oil extraction was implemented at the Vostochno-Messoyakhskoye deposit. This system enables remote and centralised control of processes at well pad sites. These sites consist of a group of wells and oil extraction equipment located within a compact area—it could be considered a single production unit of the field to some extent. Artificial intelligence, using numerous sensors, video camera data and so-called 'digital vision,' monitors equipment operation, identifying hazardous conditions and actions that could lead to malfunctions or accidents. The response time to a hazardous situation is approximately 1 minute.

Ultimately, everyone benefits—the company experiences a decrease in accidents and an increase in productivity, while employees spend more time studying statistics on modern tablets rather than navigating between pipes with a wrench in their mouths. Both parties involved in the production process are satisfied with the reduction in risks. Of course, just like with the unmanned trucks, Gazprom Neft is willing to share its developments with other companies for a reasonable fee. 

Speaking of the 'civilian' sector of unmanned cargo transport development in the Arctic, there have been several recent proposals for using UAVs as cargo delivery vehicles to remote areas, autonomous vessels for ice reconnaissance, among other solutions. Rosatom is testing a marine aircraft to monitor icebergs and ice cracks, the Arctic Research Centre has constructed a pilot project of an automated vessel for reconnaissance in coastal zones and rivers, and Yakutia is prepared to sign a contract with Rostec for the creation of a UAV system as part of the northern supply. The latter have taken this issue seriously and are soon ready to present a solution to one of the fundamental problems that prevents drones from flying in the Arctic—engineers have nearly perfected interference protection equipment that allows control of devices under challenging weather conditions. This was showcased at the International Naval Show this past summer.