scholarly journals Smart Search System of Autonomous Flight UAVs for Disaster Rescue

Sensors ◽  
2021 ◽  
Vol 21 (20) ◽  
pp. 6810
Author(s):  
Donggeun Oh ◽  
Junghee Han
Keyword(s):  
Genetic Algorithm ◽  
Unmanned Aerial Vehicles ◽  
Base Stations ◽  
Localization Algorithm ◽  
Tracking Accuracy ◽  
Long Distance ◽  
Search System ◽  
Autonomous Flight ◽  
Shadow Area ◽  
Aerial Vehicles

UAVs (Unmanned Aerial Vehicles) have been developed and adopted for various fields including military, IT, agriculture, construction, and so on. In particular, UAVs are being heavily used in the field of disaster relief thanks to the fact that UAVs are becoming smaller and more intelligent. Search for a person in a disaster site can be difficult if the mobile communication network is not available, and if the person is in the GPS shadow area. Recently, the search for survivors using unmanned aerial vehicles has been studied, but there are several problems as the search is mainly using images taken with cameras (including thermal imaging cameras). For example, it is difficult to distinguish a distressed person from a long distance especially in the presence of cover. Considering these challenges, we proposed an autonomous UAV smart search system that can complete their missions without interference in search and tracking of castaways even in disaster areas where communication with base stations is likely to be lost. To achieve this goal, we first make UAVs perform autonomous flight with locating and approaching the distressed people without the help of the ground control server (GCS). Second, to locate a survivor accurately, we developed a genetic-based localization algorithm by detecting changes in the signal strength between distress and drones inside the search system. Specifically, we modeled our target platform with a genetic algorithm and we re-defined the genetic algorithm customized to the disaster site’s environment for tracking accuracy. Finally, we verified the proposed search system in several real-world sites and found that it successfully located targets with autonomous flight.

Detection and localization of helipad in autonomous UAV landing: a coupled visual-inertial approach with artificial intelligence

2020 ◽  
Vol 71 (7) ◽  
pp. 828-839
Author(s):  
Thinh Hoang Dinh ◽  
Hieu Le Thi Hong
Keyword(s):  
Neural Network ◽  
Unmanned Aerial Vehicles ◽  
Fiducial Marker ◽  
Localization Algorithm ◽  
Challenging Problem ◽  
Autonomous Landing ◽  
Aerial Vehicles ◽  
Set Up ◽  
Detection And Localization ◽  
Rotary Wing

Autonomous landing of rotary wing type unmanned aerial vehicles is a challenging problem and key to autonomous aerial fleet operation. We propose a method for localizing the UAV around the helipad, that is to estimate the relative position of the helipad with respect to the UAV. This data is highly desirable to design controllers that have robust and consistent control characteristics and can find applications in search – rescue operations. AI-based neural network is set up for helipad detection, followed by optimization by the localization algorithm. The performance of this approach is compared against fiducial marker approach, demonstrating good consensus between two estimations

THE USE OF DECONVOLUTION TECHNIQUE FOR THE ANALYSIS OF GAMMA SPECTROMETRY DATA FROM FIELD MONITORING USING UNMANNED AERIAL VEHICLES

2019 ◽  
Vol 186 (2-3) ◽  
pp. 284-287
Author(s):  
Jaroslav Klusoň ◽  
Lenka Thinová
Keyword(s):  
Data Analysis ◽  
Unmanned Aerial Vehicles ◽  
Gamma Spectrometry ◽  
Long Distance ◽  
Air Kerma ◽  
Aerial Vehicles ◽  
Deconvolution Technique ◽  
Scan Data ◽  
Deconvolution Techniques ◽  
Short Time

ABSTRACT Airborne gamma spectrometry is an effective tool for prompt monitoring and mapping of large areas contaminated after NPP accident, radionuclides leakage cases, an impact of uranium ore mining and processing, etc. Airborne spectrometry data analysis using deconvolution technique enables to calculate air kerma rates and/or radionuclides concentrations as well as identification of radionuclides. Application of this technique on the airborne data (from manned as well as an unmanned survey using drones) is rather specific due to the requirements for short time of one scan data acquisition, a relatively long distance from the source and small detector size, due to the limited payload of the usually used drones. Application of deconvolution techniques for analysis of spectra with very poor statistics, methods and possibilities to improve the processing of such spectra are discussed.

scholarly journals Mission Planning for Unmanned Aerial Vehicles Based on Voronoi Diagram-Tabu Genetic Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Wei Tan ◽  
Yong-jiang Hu ◽  
Yue-fei Zhao ◽  
Wen-guang Li ◽  
Xiao-meng Zhang ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Unmanned Aerial Vehicles ◽  
Voronoi Diagram ◽  
Pso Algorithm ◽  
Mission Planning ◽  
Cooperative System ◽  
Aerial Vehicles ◽  
Rendezvous Problem ◽  
Aerial Vehicle ◽  
Multi Uav

Unmanned aerial vehicles (UAVs) are increasingly used in different military missions. In this paper, we focus on the autonomous mission allocation and planning abilities for the UAV systems. Such abilities enable adaptation to more complex and dynamic mission environments. We first examine the mission planning of a single unmanned aerial vehicle. Based on that, we then investigate the multi-UAV cooperative system under the mission background of cooperative target destruction and show that it is a many-to-one rendezvous problem. A heterogeneous UAV cooperative mission planning model is then proposed where the mission background is generated based on the Voronoi diagram. We then adopt the tabu genetic algorithm (TGA) to obtain multi-UAV mission planning. The simulation results show that the single-UAV and multi-UAV mission planning can be effectively realized by the Voronoi diagram-TGA (V-TGA). It is also shown that the proposed algorithm improves the performance by 3% in comparison with the Voronoi diagram-particle swarm optimization (V-PSO) algorithm.

scholarly journals Efficient Deployment of Small Cell Base Stations Mounted on Unmanned Aerial Vehicles for the Internet of Things Infrastructure

2020 ◽  
Vol 20 (13) ◽  
pp. 7460-7471 ◽  
Author(s):  
Mohammad Javad Sobouti ◽  
Zahra Rahimi ◽  
Amir Hossein Mohajerzadeh ◽  
Seyed Amin Hosseini Seno ◽  
Reza Ghanbari ◽  
...  
Keyword(s):  
Internet Of Things ◽  
Unmanned Aerial Vehicles ◽  
Small Cell ◽  
Base Stations ◽  
The Internet ◽  
Aerial Vehicles ◽  
The Internet Of Things ◽  
Cell Base

Towards a Predictive Analysis of UAV-Based Flying Base Station Decisions

2020 ◽  
Vol 16 (2) ◽  
pp. 20-52 ◽  
Author(s):  
Hamid Garmani ◽  
Driss Ait Omar ◽  
Mohamed El Amrani ◽  
Mohamed Baslam ◽  
Mostafa Jourhmane
Keyword(s):  
Nash Equilibrium ◽  
Unmanned Aerial Vehicles ◽  
Base Station ◽  
Base Stations ◽  
Practical Significance ◽  
Game Model ◽  
Activity Scheduling ◽  
Area Of Interest ◽  
Aerial Vehicles ◽  
Fairness Concern

The use of unmanned aerial vehicles (UAVs) as a communication platform is of great practical significance in the wireless communications field. This paper studies the activity scheduling of unmanned aerial vehicles acting as aerial base stations in an area of interest for a specific period. Specifically, competition among multiple UAVs is explored, and a game model for the competition is developed. The Nash equilibrium of the game model is then analyzed. Based on the analysis, an algorithm for Nash equilibrium computation is proposed. Then, a game model with fairness concern is established, and its equilibrium price is also analyzed. In addition, numerical examples are conducted to determine the factors that affect the strategies (price, quality of service, and beaconing duration) of the UAV and to show how the expected profits of UAVs change with that fairness concern point. The authors believe that this research paper will shed light on the application of UAV as a flying base station.

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