A Path Planning Method with Perception Optimization Based on Sky Scanning for UAVs.
Sensors (Basel)
; 22(3)2022 Jan 24.
Article
em En
| MEDLINE
| ID: mdl-35161639
ABSTRACT
Unmanned aerial vehicles (UAVs) are frequently adopted in disaster management. The vision they provide is extremely valuable for rescuers. However, they face severe problems in their stability in actual disaster scenarios, as the images captured by the on-board sensors cannot consistently give enough information for deep learning models to make accurate decisions. In many cases, UAVs have to capture multiple images from different views to output final recognition results. In this paper, we desire to formulate the fly path task for UAVs, considering the actual perception needs. A convolutional neural networks (CNNs) model is proposed to detect and localize the objects, such as the buildings, as well as an optimization method to find the optimal flying path to accurately recognize as many objects as possible with a minimum time cost. The simulation results demonstrate that the proposed method is effective and efficient, and can address the actual scene understanding and path planning problems for UAVs in the real world well.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Desastres
/
Dispositivos Aéreos não Tripulados
Tipo de estudo:
Prognostic_studies
Idioma:
En
Revista:
Sensors (Basel)
Ano de publicação:
2022
Tipo de documento:
Article
País de afiliação:
China