# Colored Petri Net Modelling and Evaluation of Drone Inspection Methods for Distribution Networks
## 2 Background  
### Related Works  
In the conceptual design phase, decision-makers are faced with the need to find a compromise solution among hundreds of options [8]. At this stage, it is critical to determine the required characteristics of the aircraft, as changes in the later stages of development will increase rapidly. Several methods exist in systems engineering, e.g., the article [9] describes the Interactive Reconfigurable Matrix of Alternatives (IRMA) method based on morphological analysis. The application of this decision-making concept for long-range aircraft has also been demonstrated. Other methods exist: the Genetic Algorithm [10], Sensitivity Profiling [11], Probabilistic Design Methods [12], variations of Multiple-Criteria Decision Making (MCDM) methods and others. The number of combinations of technical parameters is considerably reduced by using the mentioned methods. Further choices are made based on the goals of aircraft development using simulation methods. The task of modelling the monitoring of a linear object by drones is very similar to classical tasks in transport logistics. Many mathematical models and software tools were created to implement logistics solutions for transport companies (Oracle transportation management, 1C TRM logistics, Log4 Pro, AMB cloud, etc.). However, all of them do not take into account the features of UAVs and cannot be used to model unmanned aviation without significant modifications [13]. For this reason, a distinct type of route-planning task has been assigned specifically to drones. The task of route planning has been considered by commercial companies as part of the development of UAV software. Typically, these products use coordinates to specify the trajectory that one or a group of drones will follow or specify the area or size of an object to be inspected. Almost every major company has its own solution. For example, one of the industry leaders DJI, offers DJI GS PRO software [14] for route planning. Other companies that focus on UAV software also offer solutions. SPH Engineering has developed the UgCS tool [15] for UAV flight mission planning and control. Similar solutions are developed by the British company Dronecloud [16] and the American company Measure [17]. The subsidiary of the laboratory of computer vision of the Federal Polytechnic School of Lausanne Pix4D [18] has developed a set of software products for unmanned aerial vehicles, including route planning. The scientific community is constantly proposing new solutions for modelling drone flight missions. The authors of [19] suggest software for a ground control centre. They use Google Maps and geometrics to build a route and take into account the range of the UAV. The authors of the paper [20] use a greedy algorithm based on tagging. A platform for the automated monitoring of environmental parameters using UAVs in agriculture has been tested over the long term and has shown an increase in efficiency compared to other methods [21]. There are also solutions for controlling a group (swarm) of quadcopters [22], including for monitoring large areas [23] or for delivery tasks [24]. Some solutions allow different types of drones to be used in the planning of a single mission [25]. There are also special missions that require more detailed planning and control due to the complexity of the task, for example, the need to change the trajectory of the quadcopter during flight [26] or the peculiarities of military missions [27]. Several review articles [28; 29] are published in which the authors make in-depth analyses of existing route-planning algorithms and compare the algorithms and protocols based on performance criteria. All of the solutions described above are of high practical relevance, and some of them are widespread in the professional environment and offer broad functionality.