Power Management Strategy by Enhancing the Mission Profile Configuration of Solar-Powered Aircraft

Solar energy offers solar-powered unmanned aerial vehicle (UAV) the possibility of unlimited endurance. Some researchers have developed techniques to achieve perpetual flight by maximizing the power from the sun and by flying in accordance with its azimuth angles. However, flying in a path that follows the sun consumes more energy to sustain level flight. This study optimizes the overall power ratio by adopting the mission profile configuration of optimal solar energy exploitation. Extensive simulation is conducted to optimize and restructure the mission profile phases of UAV and to determine the optimal phase definition of the start, ascent, and descent periods, thereby maximizing the energy from the sun. In addition, a vertical cylindrical flight trajectory instead of maximizing the solar inclination angle has been adopted. This approach improves the net power ratio by 30.84% compared with other techniques. As a result, the battery weight may be massively reduced by 75.23%. In conclusion, the proposed mission profile configuration with the optimal power ratio of the trajectory of the path planning effectively prolongs UAV operation

Hierarchical mission planning with a GA-optimizer for unmanned high altitude pseudo-satellites

Unmanned Aerial Vehicles (UAVs) are gaining preference for mapping and monitoring ground activities, partially due to the cost efficiency and availability of lightweight high-resolution imaging sensors. Recent advances in solar-powered High Altitude Pseudo-Satellites (HAPSs) widen the future use of multiple UAVs of this sort for long-endurance remote sensing, from the lower stratosphere of vast ground areas. However, to increase mission success and safety, the effect of the wind on the platform dynamics and of the cloud coverage on the quality of the images must be considered during mission planning. For this reason, this article presents a new planner that, considering the weather conditions, determines the temporal hierarchical decomposition of the tasks of several HAPSs. This planner is supported by a Multiple Objective Evolutionary Algorithm (MOEA) that determines the best Pareto front of feasible high-level plans according to different objectives carefully defined to consider the uncertainties imposed by the time-varying conditions of the environment. Meanwhile, the feasibility of the plans is assured by integrating constraints handling techniques in the MOEA. Leveraging historical weather data and realistic mission settings, we analyze the performance of the planner for different scenarios and conclude that it is capable of determining overall good solutions under different conditions

Satellite and UAV Platforms, Remote Sensing for Geographic Information Systems

The present book contains ten articles illustrating the different possible uses of UAVs and satellite remotely sensed data integration in Geographical Information Systems to model and predict changes in both the natural and the human environment. It illustrates the powerful instruments given by modern geo-statistical methods, modeling, and visualization techniques. These methods are applied to Arctic, tropical and mid-latitude environments, agriculture, forest, wetlands, and aquatic environments, as well as further engineering-related problems. The present Special Issue gives a balanced view of the present state of the field of geoinformatics