Os Veículos Aéreos Não Tripulados (VANTs) são ideais para operações de risco e estressante para o ser humano são as chamadas dull, dirty and dangerous missions. Portanto, uma importante aplicação desse tipo de robô aéreo diz respeito a operações de busca envolvendo múltiplos VANTs cooperativos, em que há risco de colisões entre aeronaves e o tempo de um voo é limitado, entre outros fatores, pela capacidade de um piloto trabalhar sem descanso. Entretanto, apesar de atualmente verificar-se um crescente número de pesquisas envolvendo VANTs e do grande potencial existente na utilização de VANTs, operações de busca cooperativas ainda não estão ocorrendo. Esse assunto é uma área de estudo multidisciplinar e nascente, que possui diversas linhas de pesquisa. Diferentes algoritmos de navegação e padrões de busca foram estudados visando selecionar o(s) mais adequado(s). Além disso, apresenta-se, neste trabalho, uma visão geral sobre os mecanismos de coordenação multiagente e avalia a adequação de cada uma delas à coordenação distribuída de agentes (VANTs), visando cooperação. Assim, com o objetivo de melhorar o desempenho de uma operação de busca, esta pesquisa de mestrado propõe um modelo de VANTs cooperativos que combina mecanismos de coordenação multiagente, algoritmos de navegação e padrões de busca estabelecidos pelos principais órgãos responsáveis pelas operações de busca e salvamento. Visando avaliar a sensibilidade do percentual médio de detecção de objetos, bem como o tempo médio de busca, foi desenvolvido um simulador e milhares de simulações foram realizadas. Observou-se que, utilizando o modelo, VANTs cooperativos podem reduzir, em média, 57% do tempo de busca (comparando com uma busca de dois VANTs não cooperativos no mesmo cenário), mantendo a probabilidade média de detecção dos objetos próxima de 100% e sobrevoando apenas 30% do espaço de busca. / There are an increasing number of researches into UAV (Unmanned Aerial Vehicle) in the literature. These robots are quite suitable to dull, dirty and dangerous missions. Thus, an important application of these vehicles is the search operations involving multiple UAVs in which there is risk of collisions among aircrafts and the flight time is limited by the maximum time of pilot working hours. However, despite the huge potential use of the UAVs, cooperative search operations with this kind of flying robots are not yet occurring. This research topic is a new and multidisciplinary area of study in its beginning and there are several issues that can be studied, such as centralized versus decentralized control, path planning for cooperative flights, agent reasoning for UAV tactical planning, safety assessments, reliability in automatic target reconnaissance by cameras, agent coordination mechanisms applied to UAV cooperation and the application itself. Different path planning algorithms were studied aiming to attain the most suitable to these kinds of operations, and the conclusions are presented. In addition, official documents of Search and Rescue operations are also studied in order to know the best practices already established for this kind of operations, and, finally, an overview of the coordination multi-agent theory is presented and evaluated to achieve the UAV coordination. This work proposes a model that combines path planning algorithms, search patterns and multi-agent coordination techniques to obtain a cooperative UAV model. The great goal for cooperative UAV is to achieve such performance that the performance of the group overcomes the sum of the individual performances isolatedly. Then, aiming to analyze the average percentage of objects detection, and the average search time, a simulator was developed and thousands of simulations were run. It was observed that, using the proposed model, two cooperative UAVs can perform a search operation 57% faster than two non cooperative UAVs, keeping the average probability of objects detection approaching at 100% and flying only 30% of the search space.

Os Veículos Aéreos Não Tripulados (VANTs) são ideais para operações de risco e estressante para o ser humano são as chamadas dull, dirty and dangerous missions. Portanto, uma importante aplicação desse tipo de robô aéreo diz respeito a operações de busca envolvendo múltiplos VANTs cooperativos, em que há risco de colisões entre aeronaves e o tempo de um voo é limitado, entre outros fatores, pela capacidade de um piloto trabalhar sem descanso. Entretanto, apesar de atualmente verificar-se um crescente número de pesquisas envolvendo VANTs e do grande potencial existente na utilização de VANTs, operações de busca cooperativas ainda não estão ocorrendo. Esse assunto é uma área de estudo multidisciplinar e nascente, que possui diversas linhas de pesquisa. Diferentes algoritmos de navegação e padrões de busca foram estudados visando selecionar o(s) mais adequado(s). Além disso, apresenta-se, neste trabalho, uma visão geral sobre os mecanismos de coordenação multiagente e avalia a adequação de cada uma delas à coordenação distribuída de agentes (VANTs), visando cooperação. Assim, com o objetivo de melhorar o desempenho de uma operação de busca, esta pesquisa de mestrado propõe um modelo de VANTs cooperativos que combina mecanismos de coordenação multiagente, algoritmos de navegação e padrões de busca estabelecidos pelos principais órgãos responsáveis pelas operações de busca e salvamento. Visando avaliar a sensibilidade do percentual médio de detecção de objetos, bem como o tempo médio de busca, foi desenvolvido um simulador e milhares de simulações foram realizadas. Observou-se que, utilizando o modelo, VANTs cooperativos podem reduzir, em média, 57% do tempo de busca (comparando com uma busca de dois VANTs não cooperativos no mesmo cenário), mantendo a probabilidade média de detecção dos objetos próxima de 100% e sobrevoando apenas 30% do espaço de busca. / There are an increasing number of researches into UAV (Unmanned Aerial Vehicle) in the literature. These robots are quite suitable to dull, dirty and dangerous missions. Thus, an important application of these vehicles is the search operations involving multiple UAVs in which there is risk of collisions among aircrafts and the flight time is limited by the maximum time of pilot working hours. However, despite the huge potential use of the UAVs, cooperative search operations with this kind of flying robots are not yet occurring. This research topic is a new and multidisciplinary area of study in its beginning and there are several issues that can be studied, such as centralized versus decentralized control, path planning for cooperative flights, agent reasoning for UAV tactical planning, safety assessments, reliability in automatic target reconnaissance by cameras, agent coordination mechanisms applied to UAV cooperation and the application itself. Different path planning algorithms were studied aiming to attain the most suitable to these kinds of operations, and the conclusions are presented. In addition, official documents of Search and Rescue operations are also studied in order to know the best practices already established for this kind of operations, and, finally, an overview of the coordination multi-agent theory is presented and evaluated to achieve the UAV coordination. This work proposes a model that combines path planning algorithms, search patterns and multi-agent coordination techniques to obtain a cooperative UAV model. The great goal for cooperative UAV is to achieve such performance that the performance of the group overcomes the sum of the individual performances isolatedly. Then, aiming to analyze the average percentage of objects detection, and the average search time, a simulator was developed and thousands of simulations were run. It was observed that, using the proposed model, two cooperative UAVs can perform a search operation 57% faster than two non cooperative UAVs, keeping the average probability of objects detection approaching at 100% and flying only 30% of the search space.

Finding of collision free path is central in creation of mobile, autonomous robot. Goal of this paper is to show the most important algorithms implementing such solutions. It also describes application that is being created to allow students experiment with these methods. For this purpose it uses library that was introduced by Jakub Rusnák in 2017, which means this is a continuation and possibly extension of his work.

<div><div><div><p>Globally, there are more than 727 million people aged 65 years and older in the world, and the elderly population is expected to grow more than double in the next three decades. Families search for affordable and quality care for their senior loved ones will have an effect on the care-giving profession. A personal robot assistant could help with daily tasks such as carrying things for them and keeping track of their routines, relieving the burdens of human caregivers. Performing mentioned tasks usually requires the robot to autonomously navi- gate. An autonomous navigation robot should collect the knowledge of its surroundings by mapping the environment, find its position in the map and calculate trajectories by avoiding obstacles. Furthermore, to assign specific tasks which are in various locations, robot has to categorize the rooms in addition to memorizing the respective coordinates. In this research, methods have been developed to achieve autonomous navigation and room categorization of a mobile robot within indoor environments. A Simultaneously Localization and Map- ping (SLAM) algorithm has been used to build the map and localize the robot. Gmapping, a method of SLAM, was applied by utilizing an odometry and a 2D Light Detection and Ranging (LiDAR) sensor. The trajectory to achieve the goal position by an optimal path is provided by path planning algorithms, which is divided into two parts namely, global and local planners. Global path planning has been produced by DIJKSTRA and local path planning by Dynamic Window Approach (DWA). While exploring new environments with Gmapping and trajectory planning algorithms, rooms in the generated map were classified by a powerful deep learning algorithm called Convolutional Neural Network (CNN). Once the environment is explored, the robots localization in the 2D space is done by Adaptive Monte Carlo Localization (AMCL). To utilize and test the methods above, Gazebo software by The Robotic Operating System (ROS) was used and simulations were performed prior to real life experiments. After the trouble-shooting and feedback acquired from simulations, the robot was able to perform above tasks and later tested in various indoor environments. The environment was mapped successfully by Gmapping and the robot was located within the map by AMCL. Compared to the theoretical maximum efficient path, the robot was able to plan the trajectory with acceptable deviation. In addition, the room names were classified with minimum of 85% accuracy by CNN algorithm. Autonomous navigation results show that the robot can assist elderly people in their home environment by successfully exploring, categorizing and navigating between the rooms.</p></div></div></div>