Urban Drone Navigation: Autoencoder Learning Fusion for Aerodynamics
It addresses navigation challenges for drones in urban emergency scenarios, but appears incremental as it builds on existing methods like MORL and autoencoders.
This paper tackled the problem of drone navigation in urban search and rescue by combining multi-objective reinforcement learning with a convolutional autoencoder to optimize paths and counteract wind effects, resulting in enhanced operations in complex settings like a New York City model.
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.