Mission Planning Application Software for Solar Powered UAVs

The growing demand for unmanned aerial vehicles (UAV) for dedicated civilian use over the last decade has attracted the attention of investigators and engineers all over the world. It is important to note that the non-necessity of manual piloting is ideally suited to the operation of dirty, dangerous, dull (long autonomy) or large scale missions (use of swarms of UAVs) [1], however it demands a greater level of attention to the development of technologies that allow and ease the planning, operation and management of such vehicles. A lot of improvement has been made in the development of solar-powered UAVs, which promise a low-energy cost, silent and clean operation. However, despite solar energy being free and abundant, among many the present cost, complexity, solar energy capture systems’ efficiency, electric storage and traction efficiency, as well as the consequent requirement for large-size vehicles, greatly restricts the extensive use of these UAVs [2], besides the added difficulties from the absence of a human pilot. Nevertheless, the present work covers the development of a graphical user interface (GUI) associated to the improvement of a mission planning software created by past work, allying flexibility and quickness to the planning efficiency of solar UAV operations. Beyond facilitating the input of necessary data to the optimization of a pre-set route, this interface allows to export the optimized route to the open-source ground control station (GCS) program “MissionPlanner” (MP) [3]. In addition, as part of an exhaustive testing process, the final ensembled software was run several times, proving its capabilities and limitations in a real operational situation.A crescente procura por veículos aéreos não tripulados (UAV) para uso civil na última década tem atraído a atenção de investigadores e engenheiros um pouco por todo o mundo. É importante realçar que a sua desnecessidade de pilotagem manual é idealmente adequada à realização de missões “sujas”, perigosas, monótonas (longa autonomia) ou de grande escala (uso de “enxames” de UAVs) [1], contudo exige uma maior atenção ao desenvolvimento de tecnologias que permitam e facilitem o planeamento, operação e gestão destes veículos. Bastantes avanços têm sido feitos em UAVs movidos a energia solar, que prometem uma operação de baixo custo energético, silenciosa e limpa. Contudo, por mais que a energia solar seja livre e abundante, o presente custo, complexidade, eficiência dos sistemas de captação solar, do armazenamento e da tração usando energia elétrica, bem como a consequente necessidade de veículos de grande tamanho, restringe muito a aplicação extensiva destes veículos [2], para além das dificuldades acrescidas pela ausência de um piloto humano. Não obstante, esta dissertação abrange o desenvolvimento de um interface gráfico de utilizador (GUI) associado ao aperfeiçoamento de um software de planeamento de missões criado a partir de projetos passados, aliando a flexibilidade e rapidez à eficiência de planeamento da operação de UAVs solares. Para além de facilitar a introdução de dados necessários à otimização de uma rota predefinida, este interface permite exportar a rota otimizada para o programa open-source de estação de controlo de solo (GCS) “MissionPlanner” (MP) [3]. Para além disso, o software conjunto final foi também executado como parte de um teste exaustivo, provando as suas capacidades e limitações numa situação real de operação

Sustainable Wireless Services with UAV Swarms Tailored to Renewable Energy Sources

Unmanned Aerial Vehicle (UAV) swarms are often required in off-grid scenarios, such as disaster-struck, war-torn or rural areas, where the UAVs have no access to the power grid and instead rely on renewable energy. Considering a main battery fed from two renewable sources, wind and solar, we scale such a system based on the financial budget, environmental characteristics, and seasonal variations. Interestingly, the source of energy is correlated with the energy expenditure of the UAVs, since strong winds cause UAV hovering to become increasingly energy-hungry. The aim is to maximize the cost efficiency of coverage at a particular location, which is a combinatorial optimization problem for dimensioning of the multivariate energy generation system under non-convex criteria. We have devised a customized algorithm by lowering the processing complexity and reducing the solution space through sampling. Evaluation is done with condensed real-world data on wind, solar energy, and traffic load per unit area, driven by vendor-provided prices. The implementation was tested in four locations, with varying wind or solar intensity. The best results were achieved in locations with mild wind presence and strong solar irradiation, while locations with strong winds and low solar intensity require higher Capital Expenditure (CAPEX) allocation.Comment: To be published in Transactions on Smart Gri