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

_ Best of Session (UTM-6: sUAS Traffic Management and Deconfliction) Award, 38 DASC 38th IEEE/AIAA Digital Avionics Systems Conference This paper studies strategic conflict resolution for air traffic based on sum coloring. We consider two application scenarios: manned and unmanned air traffic, with similar targets: to improve efficiency of operations and to reduce the costs. For the Unmanned Air Vehicles Traffic Management (UTM) we consider also a payment mechanism which incentivizes the operators to share information necessary to find a socially optimal solution. We quantify the potential savings via a series of experiments, showing that our methods drastically outperform the widely used First-Come-First-Serve (FCFS) strategy. Peer Reviewed Award-winning

Abstract

Unmanned Aircraft Systems (UAS) are currently being used primarily for military applications, but with the evolution of avionics technology, a huge market in civil applications is emerging. However, most civil applications require UAS to access non-segregated airspace. Current use limitations of non-segregated airspace make it extremely difficult to extensively evaluate operational concepts and mission profiles. Using real flights to test the complete UAS mission infrastructure involves costs and risks. Therefore, simulators must be used prior to real flight campaigns to test and validate UAS components and must take into account all the actors involved in the non-segregated airspace. This paper describes the development of a Software-in-the-Loop simulator called ISIS+ to evaluate the automated operations of UAS in non-segregated airspace and to test new software components of the UAS with other avionics components. The ISIS+ simulator provides a realistic simulation environment in which the UAS software components can be developed under real air traffic scenarios while taking into account all the UAS's peculiarities. The UAS is integrated with eDEP, an air traffic simulation environment, in order to explore the UAS behavior, conflicting traffic and air traffic controllers' requests with the airspace actors. The integration is carried out using ADS-B messages and considers the UAS as an active actor in the networked environment to simulate future SESAR and NextGen scenarios. Peer Reviewed