Abstract

This project consists in building a drone traffic generator capable of spawning random operations in a country. This document provides the guidelines to generate the operations taking into account the existing regulation, the forbidden flight zones, the number of operators and the concept of operations. Using the input parameters, the generator creates and assigns a fleet of drones to each operator introduced. Then, the generator spawns the operations and assigns them to drones, so that each operator has a fleet of drones and list of operations to perform. Furthermore, the operations have an assigned flight schedule to follow the typical workday of the country. The generator also allows visualizing the operations along the day by using the program Google Earth. Moreover, the conflicts and busy areas can also be seen with this program, so that the user can see where they occur. On the other hand, the generated operations are stored in two types of file. The first type corresponds to the XML files, which store the whole information relative to the operators, their fleet of drones and the operations performed by each one. The second type of file corresponds to the MavLink files. These files store the flight plans of all the generated operations. By using the simulator ArduPilot and the software Software in the Loop, the operations can be simulated to obtain the logs. These logs can be used to evaluate, for example, the implementation of ADS-B in drones. Finally, the generator allows changing the parametrization of routes in each simulation, as well as the number of operators and the drone models. Hence, the user is able to see the evolution of drone traffic with the generations. Moreover, the generator allows downloading CSV and XML files, where all the information relative to the generated operations, the assigned fleet of drones and the operators is collected.

Abstract

In the context of the ERAINT project, a number of human-in-the-loop simulations were conducted to study the implications of integrating a remote piloted aircraft system (RPAS) into the managed airspace. For the purpose of this study, the RPAS was assumed to be involved in surveillance missions, flying a large-endurance scan pattern. The area of surveillance was selected such that it crossed an active airway for approaches. Furthermore, the simulations also included situations in which the RPAS was involved in an emergency situation such as lost links and engine failures. From previous work, the results obtained from these simulations showed that the air traffic controllers (ATCs) could successfully manage the required separations for airspace safety assurance. Nevertheless, the number of total commands issued increased, in particular the number of requirements for altitude changes, and especially those destined to commercial aircraft. Given an aircraft's flying altitude impact on performance, one question rapidly arose: Is there an increase in flight costs for airlines as a result of the increased number of ATC commands issued to provide the necessary separation with the RPAS? For this purpose two metrics relating to time and fuel are defined such that they are targeted on quantifying the economic impact for commercial air traffic resulting from the presence of a RPAS. Both metrics are computed from the ADS-B traces of all aircraft in the sector and the results of each simulation are compared with those of a baseline simulation, in which the RPAS is not present. To improve on the comparison between each simulation's results we complement this study with a statistical analysis of the available data samples using paired t-test analyses to determine if the observed differences are statistically significant or simply due to random variability. Peer Reviewed