arXiv Open Access 2023

Urban Drone Navigation: Autoencoder Learning Fusion for Aerodynamics

Jiaohao Wu Yang Ye Jing Du

Lihat Sumber

Abstrak

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.

Topik & Kata Kunci

cs.RO cs.AI

Penulis (3)

Jiaohao Wu

Yang Ye

Jing Du

Format Sitasi

APA MLA BibTeX

Wu, J., Ye, Y., Du, J. (2023). Urban Drone Navigation: Autoencoder Learning Fusion for Aerodynamics. https://arxiv.org/abs/2310.08830

Akses Cepat

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Informasi Jurnal

Tahun Terbit: 2023
Bahasa: en
Sumber Database: arXiv
Akses: Open Access ✓