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Article

Hierarchical Mission Planning with a GA-Optimizer for Unmanned High Altitude Pseudo-Satellites

1
Institute of Flight Systems, Bundeswehr University Munich, 85579 Neubiberg, Germany
2
Department of Computer Architecture and Automation, Universidad Complutense de Madrid, 28040 Madrid, Spain
*
Author to whom correspondence should be addressed.
Academic Editor: Alfred Colpaert
Sensors 2021, 21(5), 1630; https://doi.org/10.3390/s21051630
Received: 4 January 2021 / Revised: 15 February 2021 / Accepted: 16 February 2021 / Published: 26 February 2021
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. View Full-Text
Keywords: HAPS; UAV; monitoring; constrained multiple objective optimization; temporal hierarchical task planning HAPS; UAV; monitoring; constrained multiple objective optimization; temporal hierarchical task planning
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MDPI and ACS Style

Kiam, J.J.; Besada-Portas, E.; Schulte, A. Hierarchical Mission Planning with a GA-Optimizer for Unmanned High Altitude Pseudo-Satellites. Sensors 2021, 21, 1630. https://doi.org/10.3390/s21051630

AMA Style

Kiam JJ, Besada-Portas E, Schulte A. Hierarchical Mission Planning with a GA-Optimizer for Unmanned High Altitude Pseudo-Satellites. Sensors. 2021; 21(5):1630. https://doi.org/10.3390/s21051630

Chicago/Turabian Style

Kiam, Jane J., Eva Besada-Portas, and Axel Schulte. 2021. "Hierarchical Mission Planning with a GA-Optimizer for Unmanned High Altitude Pseudo-Satellites" Sensors 21, no. 5: 1630. https://doi.org/10.3390/s21051630

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