AI-Driven GPS And Vision-Free Navigation for Military Drones: A Review of the CLAK Framework and Emerging Trends in GPS-Denied UAV Autonomy

This M.Tech project provides research direction into AI-driven, GPS-denied navigation for unmanned aerial vehicles (UAVs), particularly within military operational contexts characterized by electronic warfare and signal interference. Conventional UAV navigation systems, reliant on Global Positioning System (GPS) and optical sensing, exhibit vulnerabilities to jamming and low-visibility conditions. This study focuses on the CLAK framework, an artificial intelligence-based architecture designed to enable autonomous drone positioning without dependence on GPS or camera systems. The project involves a review and conceptual structuring of CLAK's sensor fusion methodology, which integrates LiDAR, inertial measurement units (IMUs), and barometric sensing with advanced deep learning models, including convolutional neural networks (CNNs), bidirectional long short-term memory (BiLSTM) networks,

and attention mechanisms. Furthermore, it situates CLAK within the broader landscape of GPS-denied navigation technologies, such as inertial navigation systems (INS), visual simultaneous localization and mapping (SLAM), visual-inertial odometry (VIO), and learning-based inertial navigation. The research also evaluates the operational implications for military UAV applications, including autonomous swarming, urban reconnaissance, subterranean operations, and low-signature missions, while considering limitations related to domain adaptation, adversarial vulnerability, computational integration, and verification challenges.

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