A Path Planning Method with Perception Optimization Based on Sky Scanning for UAVs

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 enoug...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2022-01, Vol.22 (3), p.891
Main Authors: Yuan, Songhe, Ota, Kaoru, Dong, Mianxiong, Zhao, Jianghai
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Computer Simulation
Datasets
Deep learning
Disaster recovery
Disasters
Drone aircraft
Drones
Emergency management
Fuzzy sets
Methods
Neural networks
Neural Networks, Computer
Optimization
Path planning
Perception
Planning
Scene analysis
scene understanding
Sensors
Tobacco
Unmanned Aerial Devices
unmanned aerial vehicle (UAV)
Unmanned aerial vehicles
Vehicles
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Summary: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.
ISSN:1424-8220
1424-8220
DOI:10.3390/s22030891