scholarly journals Robust Cellular Communications for Unmanned Aerial Vehicles in Maritime Search and Rescue

2019 ◽  
Author(s):  
Philipp Gorczak ◽  
Caner Bektas ◽  
Fabian Kurtz ◽  
Thomas Lubcke ◽  
Christian Wietfeld
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Search And Rescue ◽  
Cellular Communications ◽  
Aerial Vehicles ◽  
Maritime Search And Rescue

scholarly journals Overview of the Development of the Maritime Search and Rescue System in Croatia

2019 ◽  
Vol 31 (2) ◽  
pp. 205-212
Author(s):  
Dario Medić ◽  
Anita Gudelj ◽  
Natalija Kavran
Keyword(s):  
Response Time ◽  
Unmanned Aerial Vehicles ◽  
Search And Rescue ◽  
Future Research ◽  
Search System ◽  
International Convention ◽  
Research Activities ◽  
Aerial Vehicles ◽  
Rescue System ◽  
Maritime Search And Rescue

According to the Convention for the Safety of Life at Sea and International Convention on Maritime Search and Rescue, saving human lives at sea is the duty of all signatory states. This paper analyzes and gives an overview of previous research activities in search and rescue system at sea and how the use of unmanned aerial vehicles (UAV) can improve search and rescue actions at sea. Research activities include development of the search system and placement of resources that are used in search and rescue actions (ships, planes etc.). Previous research is mainly related to minimizing response time when accidents at sea are detected in relation to search and rescue missions. Implementation of unmanned aerial vehicles into the search and rescue system enables improvement of these actions due to earlier detection and verification of accidents at sea and prevents unnecessary search and rescue units engagement in cases when an accident did not occur. The results of previous research point to the fact that future research should aim to explore the synthesis of unmanned aerial vehicles with the existing search and rescue system at sea in Croatia.

scholarly journals Design and Fabrication of Voice Controlled Unmanned Aerial Vehicle

2016 ◽  
Vol 5 (3) ◽  
pp. 205
Author(s):  
S. Sakthi Anand ◽  
R. Mathiyazaghan
Keyword(s):  
Control System ◽  
Unmanned Aerial Vehicles ◽  
Unmanned Aerial Vehicle ◽  
Voice Recognition ◽  
Search And Rescue ◽  
Voice Input ◽  
Recognition Process ◽  
Aerial Vehicles ◽  
Aerial Vehicle ◽  
The Voice

<p class="Default">Unmanned Aerial Vehicles have gained well known attention in recent years for a numerous applications such as military, civilian surveillance operations as well as search and rescue missions. The UAVs are not controlled by professional pilots and users have less aviation experience. Therefore it seems to be purposeful to simplify the process of aircraft controlling. The objective is to design, fabricate and implement an unmanned aerial vehicle which is controlled by means of voice recognition. In the proposed system, voice commands are given to the quadcopter to control it autonomously. This system is navigated by the voice input. The control system responds to the voice input by voice recognition process and corresponding algorithms make the motors to run at specified speeds which controls the direction of the quadcopter.</p>

scholarly journals Honeycomb Map: A Bioinspired Topological Map for Indoor Search and Rescue Unmanned Aerial Vehicles

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 907 ◽  
Author(s):  
Ricardo da Rosa ◽  
Marco Aurelio Wehrmeister ◽  
Thadeu Brito ◽  
José Luís Lima ◽  
Ana Isabel Pinheiro Nunes Pereira
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Prior Knowledge ◽  
Energy Use ◽  
Search And Rescue ◽  
Data Types ◽  
Topological Map ◽  
Exploration Time ◽  
Aerial Vehicles ◽  
Multiple Unmanned Aerial Vehicles ◽  
Time And Energy

The use of robots to map disaster-stricken environments can prevent rescuers from being harmed when exploring an unknown space. In addition, mapping a multi-robot environment can help these teams plan their actions with prior knowledge. The present work proposes the use of multiple unmanned aerial vehicles (UAVs) in the construction of a topological map inspired by the way that bees build their hives. A UAV can map a honeycomb only if it is adjacent to a known one. Different metrics to choose the honeycomb to be explored were applied. At the same time, as UAVs scan honeycomb adjacencies, RGB-D and thermal sensors capture other data types, and then generate a 3D view of the space and images of spaces where there may be fire spots, respectively. Simulations in different environments showed that the choice of metric and variation in the number of UAVs influence the number of performed displacements in the environment, consequently affecting exploration time and energy use.

scholarly journals Autonomous Unmanned Aerial Vehicles in Search and Rescue Missions Using Real-Time Cooperative Model Predictive Control

Sensors ◽  
2019 ◽  
Vol 19 (19) ◽  
pp. 4067 ◽  
Author(s):  
Fabio A. A. Andrade ◽  
Anthony Hovenburg ◽  
Luciano Netto de de Lima ◽  
Christopher Dahlin Rodin ◽  
Tor Arne Johansen ◽  
...  
Keyword(s):  
Model Predictive Control ◽  
Unmanned Aerial Vehicles ◽  
Real Time ◽  
Predictive Control ◽  
Kinematic Model ◽  
Search And Rescue ◽  
International Standards ◽  
Area Of Interest ◽  
Aerial Vehicles ◽  
Human Operators

Unmanned Aerial Vehicles (UAVs) have recently been used in a wide variety of applications due to their versatility, reduced cost, rapid deployment, among other advantages. Search and Rescue (SAR) is one of the most prominent areas for the employment of UAVs in place of a manned mission, especially because of its limitations on the costs, human resources, and mental and perception of the human operators. In this work, a real-time path-planning solution using multiple cooperative UAVs for SAR missions is proposed. The technique of Particle Swarm Optimization is used to solve a Model Predictive Control (MPC) problem that aims to perform search in a given area of interest, following the directive of international standards of SAR. A coordinated turn kinematic model for level flight in the presence of wind is included in the MPC. The solution is fully implemented to be embedded in the UAV on-board computer with DUNE, an on-board navigation software. The performance is evaluated using Ardupilot’s Software-In-The-Loop with JSBSim flight dynamics model simulations. Results show that, when employing three UAVs, the group reaches 50% Probability of Success 2.35 times faster than when a single UAV is employed.

scholarly journals Innovative Solutions for Searching, Rescuing, and Rendering Assistance to Cosmonauts on the Forced Landing Place of the Descent Module Under Extreme Conditions of the Northern Climatic Zone

2019 ◽  
pp. 76-95
Author(s):  
Aleksey Vasilievich Polyakov ◽  
Vitaly Mikhailovich Usov ◽  
Boris Ivanovich Kryuchkov ◽  
Yu.P. Chernyshev ◽  
A.I. Motienko
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Climatic Conditions ◽  
Search And Rescue ◽  
Extreme Conditions ◽  
Emergency Medical Care ◽  
Descent Module ◽  
Climatic Zones ◽  
Aerial Vehicles ◽  
Forced Landing ◽  
Innovative Solutions

The paper considers new approaches to the use of unmanned aerial vehicles (UAVs) and associated technologies of emergency warning under extreme conditions of the northern climatic zones for expanding the search and rescue capabilities in case of the forced landing of the descent module (DM). The paper also analyzes the innovative solutions on the human protection against adverse environmental effects and the means for emergency medical care that are delivered to the landing place of the descent module and allow mitigating risks for surviving under unfavorable climatic conditions prior the evacuation operations begin.

scholarly journals Secure UAV-Based System to Detect Small Boats Using Neural Networks

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Moisés Lodeiro-Santiago ◽  
Pino Caballero-Gil ◽  
Ricardo Aguasca-Colomo ◽  
Cándido Caballero-Gil
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Image Processing ◽  
Deep Learning ◽  
Unmanned Aerial Vehicles ◽  
High Sensitivity ◽  
Search And Rescue ◽  
Detection Systems ◽  
Aerial Vehicles ◽  
Current Detection

This work presents a system to detect small boats (pateras) to help tackle the problem of this type of perilous immigration. The proposal makes extensive use of emerging technologies like Unmanned Aerial Vehicles (UAV) combined with a top-performing algorithm from the field of artificial intelligence known as Deep Learning through Convolutional Neural Networks. The use of this algorithm improves current detection systems based on image processing through the application of filters thanks to the fact that the network learns to distinguish the aforementioned objects through patterns without depending on where they are located. The main result of the proposal has been a classifier that works in real time, allowing the detection of pateras and people (who may need to be rescued), kilometres away from the coast. This could be very useful for Search and Rescue teams in order to plan a rescue before an emergency occurs. Given the high sensitivity of the managed information, the proposed system includes cryptographic protocols to protect the security of communications.

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