Department of Materials and Production, Aalborg University, 9220 Aalborg, Denmark.
Faculty of Electronics and Computer Science, Koszalin University of Technology, 75-453 Koszalin, Poland.
Sensors (Basel). 2020 Jan 16;20(2):515. doi: 10.3390/s20020515.
Fleet mission planning for Unmanned Aerial Vehicles (UAVs) is the process of creating flight plans for a specific set of objectives and typically over a time period. Due to the increasing focus on the usage of large UAVs, a key challenge is to conduct mission planning addressing changing weather conditions, collision avoidance, and energy constraints specific to these types of UAVs. This paper presents a declarative approach for solving the complex mission planning resistant to weather uncertainty. The approach has been tested on several examples, analyzing how customer satisfaction is influenced by different values of the mission parameters, such as the fleet size, travel distance, wind direction, and wind speed. Computational experiments show the results that allow assessing alternative strategies of UAV mission planning.
无人机（UAV）的机群任务规划是为特定目标集并通常在一段时间内创建飞行计划的过程。由于越来越关注大型无人机的使用，一个关键挑战是进行任务规划，以应对这些类型的无人机特有的天气变化、避免碰撞和能量限制等问题。本文提出了一种针对复杂任务规划的声明式方法，以抵抗天气不确定性。该方法已经在多个示例上进行了测试，分析了客户满意度如何受到任务参数（如机群规模、行驶距离、风向和风速）不同值的影响。计算实验展示了允许评估无人机任务规划替代策略的结果。
