Efficient Use of UAVs for Public Safety in Disaster and Crisis Management

Mehallegue, Noureddine; Djellab, Mourad; Loukhaoukha, Khaled

doi:10.1007/s11277-020-07719-y

Cited by 11 publications

(6 citation statements)

References 14 publications

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“…Governments can deploy multi-UAV systems for tasks such as safety monitoring of large-scale events and patrolling key areas, effectively preventing and combating criminal activities. Through coordinated flight, UAV swarms can achieve extensive real-time video surveillance, enhancing public safety and security [114,115].…”

Section: Public Safety Monitoringmentioning

confidence: 99%

A Review of Collaborative Trajectory Planning for Multiple Unmanned Aerial Vehicles

Wang,

Huang,

et al. 2024

Processes

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In recent years, the collaborative operation of multiple unmanned aerial vehicles (UAVs) has been an important advancement in drone technology. The research on multi-UAV collaborative flight path planning has garnered widespread attention in the drone field, demonstrating unique advantages in complex task execution, large-scale monitoring, and disaster response. As one of the core technologies of multi-UAV collaborative operations, the research and technological progress in trajectory planning algorithms directly impact the efficiency and safety of UAV collaborative operations. This paper first reviews the application and research progress of path-planning algorithms based on centralized and distributed control, as well as heuristic algorithms in multi-UAV collaborative trajectory planning. It then summarizes the main technical challenges in multi-UAV path planning and proposes countermeasures for multi-UAV collaborative planning in government, business, and academia. Finally, it looks to future research directions, providing ideas for subsequent studies in multi-UAV collaborative trajectory planning technology.

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Section: Public Safety Monitoringmentioning

confidence: 99%

A Review of Collaborative Trajectory Planning for Multiple Unmanned Aerial Vehicles

Wang,

Huang,

et al. 2024

Processes

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“…Numerous applications of UAVs have been studied and employed, including smart real estate investment (Ullah et al, 2018;Felli et al, 2018), intelligent buildings , smart healthcare (Islam and Shin, 2020), and many more. UAVs are used for disaster risk management, post-disaster phase transition using IoT, public security in disaster and crisis response, energy-efficient work scheduling and physical evaluation, and flood protection (Kumar et al, 2020;Ejaz et al, 2020;Mehallegue et al, 2020). Various machine-learning methods have also been investigated for effective UAVbased flood catastrophe control.…”

Section: Related Workmentioning

confidence: 99%

Urban Environmental Flood Susceptibility Using a Multi-Layered Neural Network Model From Satellite Imagery Sources

2023

Global NEST: The International Journal

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<p>Recently, there has been a rise in research based on data-driven based machine learning models to address the issue of processing speed posed by various physiological simulations. The majority of studies, however, have concentrated on creating models for particular water sources, drainage systems, and gauge stations area. Therefore, unless further data is supplied and the models are subsequently trained, their outcomes cannot be effectively applied to different scenarios. With its excellent spatio-temporal accuracy range with the advanced analytics, the forthcoming Global Navigations Satellite Systems (GNSSs) technology offers a novel method for the monitoring of flood disasters in dynamic manner. This study recommends a Multi-layered Neural Network (MNN) for flood monitoring, which combines a Convolutional Neural Networking (CNN) along with a backward propagation based (BP) learning network. The CNN component is used to obtain the abstracted features from full delay doppler maping images. The BP section is provided with GNSSs characteristics, such as reflection of surface with power range, and also the vegetation attributes from the Soil Moistening Active Passives (SMAP) satellite source data. In comparison to physiologically based models, the results shown that the CNN-oriented system makes accurate predictions on "unseen" areas of catchment with a substantially shorter processing time. The obtained results imply that, with respect to accuracy of prediction, the patch-enabled option is superior to the resizing-based option. Additionally, every experiment has demonstrated that the forecast of flow speed is more precise than the prognosis of water depth, indicating that the water deposition is more complex to information about global level elevations than the flow speed with velocity.</p>

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“…В связи с тем, что группа БпЛА, выполняющая задание, является открытой системой, взаимодействующей со стохастической средой с возмущениями, возможно возникновение незапланированных и кризисных ситуаций [12][13][14][15][16]. В общем случае под кризисными ситуациями понимаются случаи, когда режим автономного управления невозможно реализовать в полном объеме.…”

Section: Uav Group Management When Working Out Of Crisis Flight Situa...unclassified

Uav Group Management When Working Out of Crisis Flight Situations in Solving Transport Problems

Savelyev¹,

Lebedeva²,

Lebedev³

et al. 2022

IZVESTIYA SFedU. ENGINEERING SCIENCES

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Efficient Use of UAVs for Public Safety in Disaster and Crisis Management

Cited by 11 publications

References 14 publications

A Review of Collaborative Trajectory Planning for Multiple Unmanned Aerial Vehicles

A Review of Collaborative Trajectory Planning for Multiple Unmanned Aerial Vehicles

Urban Environmental Flood Susceptibility Using a Multi-Layered Neural Network Model From Satellite Imagery Sources

Uav Group Management When Working Out of Crisis Flight Situations in Solving Transport Problems

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