Urban Drone Navigation: Autoencoder Learning Fusion for Aerodynamics

• URL: http://arxiv.org/abs/2310.08830v1
• Date: Fri, 13 Oct 2023 02:57:35 GMT
• Title: Urban Drone Navigation: Autoencoder Learning Fusion for Aerodynamics
• Authors: Jiaohao Wu, Yang Ye, Jing Du
• Abstract summary: 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. Tested on a New York City model, this method enhances drone SAR operations in complex urban settings.
• Score: 2.868732757372218
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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