2 research outputs found
Urban Drone Navigation: Autoencoder Learning Fusion for Aerodynamics
Drones are vital for urban emergency search and rescue (SAR) due to the
challenges of navigating dynamic environments with obstacles like buildings and
wind. This paper presents a method that combines multi-objective reinforcement
learning (MORL) with a convolutional autoencoder to improve drone navigation in
urban SAR. The approach uses MORL to achieve multiple goals and the autoencoder
for cost-effective wind simulations. By utilizing imagery data of urban
layouts, the drone can autonomously make navigation decisions, optimize paths,
and counteract wind effects without traditional sensors. Tested on a New York
City model, this method enhances drone SAR operations in complex urban
settings.Comment: 47 page