Multiple UAV systems: a survey

Nowadays, Unmanned Aerial Vehicles (UAVs) are used in many different applications. Using systems of multiple UAVs is the next obvious step in the process of applying this technology for variety of tasks. There are few research works that cover the applications of these systems and they are all highly specialized. The goal of this survey is to fill this gap by providing a generic review on different applications of multiple UAV systems that have been developed in recent years. We also present a nomenclature and architecture taxonomy for these systems. In the end, a discussion on current trends and challenges is provided.This work was funded by the Ministry of Economy, Industryand Competitiveness of Spain under Grant Nos. TRA2016-77012-R and BES-2017-079798Peer ReviewedPostprint (published version

Multi-Robot workspace division based on compact polygon decomposition

In this work, we tackle the problem of multi-robot convex workspace division. We present an algorithm to split a convex area among several robots into the corresponding number of parts based on the area requirements for each part. The core idea of the algorithm is a sequence of divisions into pairs with the lowest possible perimeters. In this way, the compactness of the partitions obtained is maximized. The performance of the algorithm, as well as the quality of the obtained parts, are analyzed in comparison with two different algorithms. The presented approach yields better results in all metrics compared to other algorithms.This work was supported by the Ministerio de Economía, Industria y Competitividad, and Gobierno de España under Award BES-2017-079798 and Award TRA2016-77012-R.Peer ReviewedPostprint (published version

Dispatcher3 – Machine learning for efficient flight planning: approach and challenges for data-driven prototypes in air transport

Machine learning techniques to support decisionmaking processes are in trend. These are particularly relevant in the context of flight management where large datasets of planned and realised operations are available. Current operations experience discrepancies between planned and executed flight plan, these might be due to external factors (e.g. weather, congestion) and might lead to sub-optimal decisions (e.g. recovering delay (burning extra fuel) when no holding is expected at arrival and therefore it was no needed). Dispatcher3 produces a set of machine learning models to support flight crew pre-departure, with estimations on expected holding at arrival, runway in use and fuel usage, and the airline’s duty manager on pre-tactical actions, with models trained with a larger look ahead time for ATFM and reactionary delay estimations. This paper describes the prototype architecture and approach of Dispatcher3 with particular focus on the challenges faced by this type of data-driven machine learning models in the field of air transport ranging: from technical aspects such as data leakage to operational requirements such as the consideration and estimation of uncertainty. These considerations should be relevant for projects which try to use machine learning in the field of aviation in general.This work is performed as part of Dispatcher3 innovation action which has received funding from the Clean Sky 2 Joint Undertaking (JU) under grant agreements No 886461. The Topic Manager is Thales AVS France SAS. The JU receives support from the European Union’s Horizon 2020 research and innovation programme and the Clean Sky 2 JU members other than the Union. The opinions expressed herein reflect the authors’ views only. Under no circumstances shall the Clean Sky 2 Joint Undertaking be responsible for any use that may be made of the information contained herein.Postprint (published version

Flight planning in multi-unmanned aerial vehicle systems: Nonconvex polygon area decomposition and trajectory assignment

Nowadays, it is quite common to have one unmanned aerial vehicle (UAV) working on a task but having a team of UAVs is still rare. One of the problems that prevent us from using teams of UAVs more frequently is flight planning. In this work, we present the first open-source solution ( https://pypi.org/project/pode/ ) for splitting any complex area into multiple parts. The area of interest can be convex or nonconvex and can include any number of no-flight zones. Four solutions, based on the algorithm of Hert and Lumelsky, are tested with the aim of improving the compactness of the partitions. We also show how the shape of the partitions influences flight performance in a real case scenario.This work was supported by Ministerio de Economía, Industria y Competitividad, and Gobierno de España under grants/award numbers BES-2017-079798 and TRA2016-77012-R.Peer ReviewedPostprint (published version

Dispatcher3 - D5.2: Verification and validation report

The deliverable provides the results from the verification and validation activities within Dispatcher3 project. The document reviews the internal and external validation activities that were carried out during the course of Dispatcher3 project according to the plan defined in D5.1. Dispatcher3 is organised in three layers: data acquisition and preparation, predictive layer, with machine learning models, and prospective models, with the integration of the individual machine learning models in an interactive Advice Generator and an estimator of rotation/reactionary delay. This deliverable presents the verification and validation activities performed on these three components. For the data acquisition and preparation layer the data-pipelines, including the transformation verification and validation activities are described. In the predictive layer both the models developed for the first release and their evolution for the final prototype are described and presented. Finally, for the prospective layer, the interactive interface with its functional requirements is presented and verified, while the reactionary delay model is described, and different scenarios evaluated for its validation. The deliverable also describes the different internal and external activities and meetings, workshops and dedicated online site visits that have been performed during the duration of the project. Finally, the document assesses the verification of the high-level system-wide requirements identified at the beginning of the project in D1.1 – Technical resources and problem definition, and the research questions identified in the Verification and validation plan (D5.1).This deliverable is part of a project that has received funding from the Clean Sky Joint Undertaking under grant agreement No 886461 under European Union’s Horizon 2020 research and innovation programme.Peer ReviewedPostprint (published version