Multi-sensor Data Fusion for Autonomous Unmanned Aerial Vehicle Navigation in GPS Denied Environments
DOI:
https://doi.org/10.47839/ijc.23.4.3762Keywords:
Data fusion, Extended Kalman filter, multi-sensor, obstacle avoidance, optical flow, UAVAbstract
Although the Global Positioning System (GPS) is a cornerstone of modern navigation, its accuracy can diminish in urban areas, indoors, and intentionally jammed locations. This poses significant challenges for Unmanned Aerial Vehicles (UAVs) operating autonomously in these "GPS-denied" environments. In this context, multi-sensor data fusion (MSDF) is suggested as a viable technique, as it integrates inputs from various sensors to create a more robust and reliable navigation solution. In this paper, a system has been developed that enables an AUAV flying along a predetermined route to reliably detect both fixed and moving obstacles in challenging environments where GPS signals are weak or absent, and to perform effective avoidance maneuvers to prevent potential collisions, offering superior situational awareness and operational efficiency. The results obtained demonstrate that the AUAV can navigate safely and accurately in complex and continuously changing environments. The findings reveal that the proposed system has the ability to reliably detect both stationary and moving obstacles in challenging environments where GPS signals are absent or weak, and to perform effective avoidance maneuvers to prevent potential collisions in real time.
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