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This article focuses on the integration of magnetic and visual navigation systems to enable autonomous drone operation in GPS-denied environments, a critical advancement for AI-driven autonomy in contested spaces. By replacing reliance on external signals with resilient internal positioning, this development directly addresses the limitations of current machine learning models that depend on satellite data. The successful pairing of these technologies on an attritable platform marks a significant step forward in ensuring navigation reliability for unmanned systems without requiring central processing or massive datasets.

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SandboxAQ Tests GPS-Free Navigation on Northrop Grumman Drone

The Quantum InsiderSeptember 9, 20264 min read

Insider Brief PRESS RELEASE — – SandboxAQ announced it has successfully completed the world’s first reported test of a magnetic navigation (MagNav) system on an attritable platform – Northrop Grumman’s Lumberjack®, a Group 3 UAS attritable drone. It was also the world’s first reported instance of a MagNav system being paired with a visual navigation system on an […]

SandboxAQ and Northrop Grumman completed what they described as the first test combining magnetic and visual navigation on an attritable, one-way attack drone. SandboxAQ’s AQNav system provided Northrop Grumman’s Lumberjack drone with positioning capabilities that do not depend on GPS or other externally transmitted signals. Engineers installed the hardware-agnostic AQNav software on the drone’s existing computing systems in less than an hour, according to SandboxAQ. PRESS RELEASE — – SandboxAQ announced it has successfully…

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