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Russian ground drone uses laser to disable mines

Bottom line

The Russian Courier 2.0 ground drone demonstrated the ability to burn through TM-62 mine casings using a laser at 20km range without detonation.

Why it matters

NATO and allied forces should assess the threat of laser-equipped ground robots for mine countermeasures and consider the implications for minefield resilience and robotic warfare dynamics.

At the Center-2026 exhibition, the Russian Courier 2.0 ground drone showcased a new capability: using a laser to burn through the casings of TM-62 anti-tank mines in 5-7 seconds without triggering detonation. The system operated effectively at a range of 20km, suggesting a potential new method for Russian forces to clear or disable minefields remotely without risking personnel or traditional mine-clearing equipment.

Evidence — 1 independent origin

  • Victory Dronessource Borigin t.me
    Original text (excerpt)

    На стратегических учениях "Центр - 2026" были задействованы множество модификаций НРТК "Курьер" и НРТК "Курьер 2.0" Версия НРТК «Курьер 2.0» и одна из его модификаций - «Луч» на расстоянии трехсот метров лазерным лучом прожигает металлическую оболочку мин и неразорвавшихся снаряд

NamedCourier 2.0TM-62Center-2026Topicsground dronesrussia

Related findings

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History · version 1 · first published · last substantive update

Cite this finding
Robot War (2026). Russian ground drone uses laser to disable mines. Hetzner OSINT, 10 October 2026. https://robotwar.io/osint-hetzner/2026-10-10--russian-ground-drone-uses-laser-to-disable-mines/

@misc{robotwar-2026-10-10--russian-ground-drone-uses-laser-to-disable-mines,
  title = {Russian ground drone uses laser to disable mines},
  author = {Robot War},
  howpublished = {\url{https://robotwar.io/osint-hetzner/2026-10-10--russian-ground-drone-uses-laser-to-disable-mines/}},
  year = {2026},
  note = {Hetzner OSINT finding B3}
}

Grades: source reliability A–F from the channel register; information credibility 1–6 from independent origins — reposts count once. The bottom line and summary are written by the model from the cited posts; grades and source roles are rule-based. Methodology 2026-10-15.v6 · Corrections · JSON · RSS · API performance