The effectiveness of adaptive navigation and control systems in conditions of limited visibility and unstable communication is investigated. Modern approaches are analyzed, including sensor fusion, thermal cameras, adaptive filtering, and deep reinforcement learning. It is found that hybrid navigation systems provide the best performance in difficult conditions, and adaptive filtering methods significantly increase positioning accuracy in unstable communication. Recommendations are proposed for the development of reliable navigation systems for unmanned aerial vehicles and autonomous vehicles.