Kiam Jane Jean, Besada-Portas Eva, Schulte Axel
Institute of Flight Systems, Bundeswehr University Munich, 85579 Neubiberg, Germany.
Department of Computer Architecture and Automation, Universidad Complutense de Madrid, 28040 Madrid, Spain.
Sensors (Basel). 2021 Feb 26;21(5):1630. doi: 10.3390/s21051630.
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.
无人驾驶飞行器（UAV）在测绘和监测地面活动方面越来越受到青睐，部分原因是其成本效益高，且有轻便的高分辨率成像传感器。太阳能高空伪卫星（HAPS）的最新进展拓宽了此类多架无人机在大范围地面区域的平流层下部进行长续航遥感的未来应用。然而，为了提高任务成功率和安全性，在任务规划期间必须考虑风对平台动力学的影响以及云层覆盖对图像质量的影响。因此，本文提出了一种新的规划器，该规划器考虑天气条件，确定多架HAPS任务的时间分层分解。该规划器由多目标进化算法（MOEA）支持，该算法根据精心定义的不同目标确定可行的高级计划的最佳帕累托前沿，以考虑环境时变条件带来的不确定性。同时，通过在MOEA中集成约束处理技术来确保计划的可行性。利用历史天气数据和实际任务设置，我们分析了该规划器在不同场景下的性能，并得出结论，它能够在不同条件下确定总体良好的解决方案。
