AI-Driven Path Planning for UAVs in GNSS-Denied and Adverse Weather Conditions

Abstract

This paper presents an AI-driven path planning approach for Unmanned Aerial Vehicles (UAVs) operating in Global Navigation Satellite System (GNSS)-denied and adverse weather conditions. We propose a framework that combines artificial intelligence algorithms with sensor data to enable UAVs to navigate challenging environments autonomously. The study focuses on developing robust path planning algorithms that account for dynamic obstacles and varying environmental conditions. Experimental results demonstrate the effectiveness of the proposed approach in achieving successful navigation in complex scenarios. This research contributes to the development of resilient UAV systems capable of operating effectively in GNSS-denied and adverse weather environments.