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

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 b...

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Autores principales: de Alcantara Andrade, Fabio Augusto, Reinier Hovenburg, Anthony, Netto de Lima, Luciano, Dahlin Rodin, Christopher, Johansen, Tor Arne, Storvold, Rune, Moraes Correia, Carlos Alberto, Barreto Haddad, Diego
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6806177/
https://www.ncbi.nlm.nih.gov/pubmed/31547143
http://dx.doi.org/10.3390/s19194067
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author de Alcantara Andrade, Fabio Augusto
Reinier Hovenburg, Anthony
Netto de Lima, Luciano
Dahlin Rodin, Christopher
Johansen, Tor Arne
Storvold, Rune
Moraes Correia, Carlos Alberto
Barreto Haddad, Diego
author_facet de Alcantara Andrade, Fabio Augusto
Reinier Hovenburg, Anthony
Netto de Lima, Luciano
Dahlin Rodin, Christopher
Johansen, Tor Arne
Storvold, Rune
Moraes Correia, Carlos Alberto
Barreto Haddad, Diego
author_sort de Alcantara Andrade, Fabio Augusto
collection PubMed
description 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.
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spelling pubmed-68061772019-11-07 Autonomous Unmanned Aerial Vehicles in Search and Rescue Missions Using Real-Time Cooperative Model Predictive Control de Alcantara Andrade, Fabio Augusto Reinier Hovenburg, Anthony Netto de Lima, Luciano Dahlin Rodin, Christopher Johansen, Tor Arne Storvold, Rune Moraes Correia, Carlos Alberto Barreto Haddad, Diego Sensors (Basel) Article 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. MDPI 2019-09-20 /pmc/articles/PMC6806177/ /pubmed/31547143 http://dx.doi.org/10.3390/s19194067 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
de Alcantara Andrade, Fabio Augusto
Reinier Hovenburg, Anthony
Netto de Lima, Luciano
Dahlin Rodin, Christopher
Johansen, Tor Arne
Storvold, Rune
Moraes Correia, Carlos Alberto
Barreto Haddad, Diego
Autonomous Unmanned Aerial Vehicles in Search and Rescue Missions Using Real-Time Cooperative Model Predictive Control
title Autonomous Unmanned Aerial Vehicles in Search and Rescue Missions Using Real-Time Cooperative Model Predictive Control
title_full Autonomous Unmanned Aerial Vehicles in Search and Rescue Missions Using Real-Time Cooperative Model Predictive Control
title_fullStr Autonomous Unmanned Aerial Vehicles in Search and Rescue Missions Using Real-Time Cooperative Model Predictive Control
title_full_unstemmed Autonomous Unmanned Aerial Vehicles in Search and Rescue Missions Using Real-Time Cooperative Model Predictive Control
title_short Autonomous Unmanned Aerial Vehicles in Search and Rescue Missions Using Real-Time Cooperative Model Predictive Control
title_sort autonomous unmanned aerial vehicles in search and rescue missions using real-time cooperative model predictive control
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6806177/
https://www.ncbi.nlm.nih.gov/pubmed/31547143
http://dx.doi.org/10.3390/s19194067
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