Department of Computer Science and Automation, Technische Universität Ilmenau, 98693 Ilmenau, Germany.
Department of Mechanical Engineering, Technische Universität Ilmenau, 98693 Ilmenau, Germany.
Sensors (Basel). 2022 Apr 29;22(9):3418. doi: 10.3390/s22093418.
The UAV industry is developing rapidly and drones are increasingly used for monitoring industrial facilities. When designing such systems, operating companies have to find a system configuration of multiple drones that is near-optimal in terms of cost while achieving the required monitoring quality. Stochastic influences such as failures and maintenance have to be taken into account. Model-based systems engineering supplies tools and methods to solve such problems. This paper presents a method to model and evaluate such UAV systems with coloured Petri nets. It supports a modular view on typical setup elements and different types of UAVs and is based on UAV application standards. The model can be easily adapted to the most popular flight tasks and allows for estimating the monitoring frequency and determining the most appropriate grouping and configuration of UAVs, monitoring schemes, air time and maintenance periods. An important advantage is the ability to consider drone maintenance processes. Thus, the methodology will be useful in the conceptual design phase of UAVs, in monitoring planning, and in the selection of UAVs for specific monitoring tasks.
无人机行业发展迅速,无人机越来越多地用于监测工业设施。在设计此类系统时,运营公司必须找到一种在成本方面接近最优的多架无人机系统配置,同时达到所需的监测质量。必须考虑到故障和维护等随机影响。基于模型的系统工程提供了解决此类问题的工具和方法。本文提出了一种使用有色 Petri 网对这种无人机系统进行建模和评估的方法。它支持对典型设置元素和不同类型的无人机的模块化视图,并且基于无人机应用标准。该模型可以轻松适应最流行的飞行任务,并能够估计监测频率,确定最合适的无人机分组和配置、监测方案、飞行时间和维护周期。一个重要的优势是能够考虑无人机的维护过程。因此,该方法将在无人机的概念设计阶段、监测规划以及为特定监测任务选择无人机时非常有用。