Video summary - Neptus, command and control infrastructure for heterogeneous teams of autonomous vehicles

This video shows a brief overview over Neptus, a command and control infrastructure for heterogeneous teams of autonomous vehicles. Having different,types of vehicles at our laboratory and from our partners, there was an increasing need to create a common infrastructure to all these systems. Additionally, a tool to support the entire mission life cycle (Planning, Execution, Review and Dissemination) was lacking. Neptus was created to provide vehicle independence and seamless inter-systems communications. Currently, Neptus has been already tested with Remotely Operated Vehicles, Autonomous Underwater Vehicles, Unmanned Air Vehicles, Autonomous Surface Vehicles and Wireless Sensor Networks. Some of these systems were operated simultaneously by various operating consoles that were sharing the same communication infrastructure. The received data was being relayed to a web server that allowed for the real-time mission following by using a common web browser

A new electronic control system for unmanned underwater vehicles

In this paper a new electronic control system for unmanned underwater vehicles is presented. This control system is characterized by a distribution in control over two network of type CANBus and Ethernet. This new electronic control system integrates functionalities of AUVs, as the automatic execution of preprogrammed trajectories. The control system also integrates an acoustic positioning system based on USBL. The information of relative positioning is sent through specific software tools towards NEPTUS Software for the command and control of the unmanned vehicle, in this way it is possible to observe the positioning of the vehicle under water.Peer Reviewe

Autonomous vehicles in the response to maritime incidents

The future role of autonomous vehicles in the emergency response to maritime incidents isdiscussed and a framework for their integration into existing response plans is proposed. This is done inthe context of the developments on autonomous vehicle systems from the Underwater Systems andTechnologies Laboratory from Porto University

NEPTUS - a framework to support the mission life cycle

The Neptus distributed command and control framework for operations withvehicles, sensors, and human operators in inter-operated networks is presented. This isdone in the context of applications, technologies, and field tests. There are applicationsfor world representation and modeling, mission planning, simulation, execution controland supervision, and post-mission analysis. This is done in a mixed initiative fashionallowing the intervention by experienced human operators. XML abstract data types andXSLT technologies facilitate vehicle-interoperability and the standardization ofinteractions. A publish/subscribe (P/S) middleware framework for communications in adistributed environment enables the transparent inter-operability of communicationnetworks. A console builder together with the P/S middleware allow the user to configureoperating consoles for different vehicles. Results from field tests validate the overallframework and provide directions for future work

Mixed initiative planning and control of UAV teams for persistent surveillance

Tese de mestrado. Mestrado Integrado em Engenharia Electrotécnica e de Computadores. Faculdade de Engenharia. Universidade do Porto. 201

Visualização de dados para redes de veículos autónomos

Tese de Mestrado Integrado. Engenharia Electrotécnica e de Computadores (Major Automação). Faculdade de Engenharia. Universidade do Porto. 201

Metodologias e ferramentas para o teste e validação de sistemas

Tese de mestrado integrado. Engenharia Informática e Computação. Faculdade de Engenharia. Universidade do Porto. 201

Desenvolvimento de módulos para planeamento e controlo de execução de missões de veículos aéreos não tripulados

Com a evolução da tecnologia, os UAVs (unmanned aerial vehicles) são cada vez mais utilizados, não só em missões de risco para o ser Humano, mas também noutro tipo de missões, como é o caso de missões de inspeção, vigilância, busca e salvamento. Isto devese ao baixo custo das plataformas assim como à sua enorme fiabilidade e facilidade de operação. Esta dissertação surge da necessidade de aumentar a autonomia dos UAVs do projeto PITVANT (Projeto de Investigação e Tecnologia em Veículos Aéreos Não Tripulados), projeto de investigação colaborativa entre a AFA (Academia da Força Aérea) e a FEUP (Faculdade de Engenharia da Universidade do Porto), relativamente ao planeamento de trajetórias entre dois pontos no espaço, evitando os obstáculos que intersetem o caminho. Para executar o planeamento da trajetória mais curta entre dois pontos, foi implementado o algoritmo de pesquisa A*, por ser um algoritmo de pesquisa de soluções ótimas. A área de pesquisa é decomposta em células regulares e o centro das células são os nós de pesquisa do A*. O tamanho de cada célula é dependente da dinâmica de cada aeronave. Para que as aeronaves não colidam com os obstáculos, foi desenvolvido um método numérico baseado em relações trigonométricas para criar uma margem de segurança em torno de cada obstáculo. Estas margens de segurança são configuráveis, sendo o seu valor por defeito igual ao raio mínimo de curvatura da aeronave à velocidade de cruzeiro. De forma a avaliar a sua escalabilidade, o algoritmo foi avaliado com diferentes números de obstáculos. As métricas utilizadas para avaliação do algoritmo foram o tempo de computação do mesmo e o comprimento do trajeto obtido. Foi ainda comparado o desempenho do algoritmo desenvolvido com um algoritmo já implementado, do tipo fast marching.With the evolution of technology, UAVs (unmanned aerial vehicles) are being used, not only on missions with risk to humans, but also on other types of missions, such as, inspection, surveillance, search and rescue. This is due to the low cost of the platforms, and their great ease of operation and reliability. This project arises from the need to increase the autonomy of UAVs inside PITVANT project, regarding the planning of trajectories between two points in space, avoiding obstacles that intersect the way. To plan the shortest path between two points, the A * search algorithm was implemented, because it gives an optimal path. The search area was broken down into regular cells and their centers are the nodes of the A * search algorithm. The size of each cell is dependent on the dynamics of each aircraft. To avoiding aircraft from touching the obstacles, it was developed a numerical method based on trigonometric relationships, to create a safety margin around each obstacle. These safety margins are configurable, by default, these margins have the value of the minimum radius of curvature of the aircraft at cruising speed. In order to evaluate its scalability, the algorithm was evaluated with different numbers of obstacles. The metric used to evaluate the algorithm were the computation time and the length of the path obtained. The performance of the algorithm developed was also compared with an algorithm from fast marching family