Digital Twin-Enabled Obstacle Avoidance System for the MADNI Drone

Obstacle avoidance is an essential ability for unmanned aerial vehicles (UAVs) operating in complex and dynamic environments. This work uses the Manoeuvrable Autonomous Drone for Navigation and Intelligence (MADNI) and integrates the 3D Vector Field Histogram Plus (3D VFH+) algorithm with Light Detection and Ranging (LiDAR) sensors to enhance real-time obstacle avoidance implemented on a digital twin of a real life UAV system. The 3D VFH+ algorithm enables MADNI to calculate optimal azimuth and pitch angles for safe navigation through obstacle-dense environments. LiDAR sensors provide high-precision, real-time spatial data, generating detailed three-dimensional environmental maps essential for detecting and avoiding obstacles. Integrating the 3D VFH+ algorithm with the LiDAR system allows MADNI to autonomously navigate through challenging environments with varying obstacle densities and configurations. The proposed system demonstrates its versatility in applications such as search and rescue (SAR) missions, surveillance, and environmental monitoring, enhancing safety, efficiency, and operational autonomy for UAV platforms.