An Architecture for the seamless integration of UAV-based wildfire monitoring missions

Current Unmanned Aerial Vehicles (UAVs) technology offers feasible technical solutions for airframes, flight control, communications, and base stations. In addition, the evolution of technology is miniaturizing most sensors used in airborne applications. Hence, sensors like weather radars, SAR, multi spectral line-scan devices, etc. in addition to visual and thermal cameras are being used as payload on board UAVs. As a result (UAVs) are slowly becoming efficient platforms that can be applied in scientific/commercial remote sensing applications. UAVs may offer interesting benefits in terms of cost, flexibility, endurance, etc. Even remote sensing in dangerous situations due to extreme climatic conditions (wind, cold, heat) are now seen as possible because the human factor on board the airborne platform is no longer present. However, the complexity of developing a full UAV-system tailored for remote sensing is currently limiting its practical application. Currently, only large organizations like NASA or NOAA have enough resources and infrastructure to develop such applications. Even though the rapid evolution of UAV technology the generalized development of remote sensing applications is still limited by the absence of systems that support the development of the actual UAV sensing mission. Remote sensing engineers face the development of specific systems to control their desired flight-profile, sensor activation/confi guration along the flight, data storage and eventually its transmission to the ground control. All these elements may delay and increase the risk and cost of the project. This work introduces a flexible and reusable architecture designed to facilitate the development of UAV-based remote sensing applications. Applications are developed following a service/subscription based software architecture. Each computation module may support multiple applications. Each application could create and subscribe to available services. Services could be discovered and consumed in a dynamic way like web services in the Internet domain. Applications could interchange information transparently from network topology, application implementation and actual data payload. This flexibility is organized into an user-parameterizable UAV service abstraction layer (USAL). The USAL defines a collection of pre-defined services and their interrelations as a basic starting point for further development by users. Functionalities like enhanced flight-plans, mission control, data storage, communications management, etc. are offered. Additional services can be included according to requirements but all existing services and inter-service communication infrastructure can be exploited and tailored to specific needs. This approach reduces development times and risks, but at the same time gives the user higher levels of flexibility and permits the development of more ambitious applications. As application scenario, we are developing a UAV system devoted to the detection, control and analysis of wildland forest fires in the Mediterranean area. The design of the proposed UAV system is composed of five main components. Each component will work collaboratively to constitute a platform of high added value.Peer Reviewe

Building a safe and socially acceptable concept of operation for drones flying at the very low level

This work has been prepared by all CORUS partners: DLR, DFS, DSNA, ENAV, EUROCONTROL, HEMAV, NATS, Unifly and UPC. The list of authors would be too long to include them all, but we would like to acknowledge them now. This work has been partially funded by the SESAR Joint Undertaking, a body of the European Commission, under grant H2020 RIA-763551.As part of the Single European Sky ATM Research (SESAR) a number of projects related to the drones flying at the very low level (VLL) have been funded. At its core, the CORUS project is developing the concept of operations (ConOps) of these drones1. At the same time, the last European ATM Masterplan has proposed the “Roadmap for the safe integration of drones into all classes of airspace”2. This document proposes the set of services for the unmanned air traffic management, named as Uspace, together with a calendar for their deployment (from U1 to U4). The CORUS project integrates these services as pillars of the ConOps, with the objective of supporting the new businesses and jobs, improving the safety of the drones, and dealing with their public acceptance.Postprint (published version

U-space concept of operations: A key enabler for opening airspace to emerging low-altitude operations

Opening the sky to new classes of airspace user is a political and economic imperative for the European Union. Drone industries have a significant potential for economical growth according to the latest estimations. To enable this growth safely and efficiently, the CORUS project has developed a concept of operations for drones flying in Europe in very low-level airspace, which they have to share that space with manned aviation, and quite soon with urban air mobility aircraft as well. U-space services and the development of smart, automated, interoperable, and sustainable traffic management solutions are presented as the key enabler for achieving this high level of integration. In this paper, we present the U-space concept of operations (ConOps), produced around three new types of airspace volume, called X, Y, and Z, and the relevant U-space services that will need to be supplied in each of these. The paper also describes the reference high-level U-space architecture using the European air traffic management architecture methodology. Finally, the paper proposes the basis for the aircraft separation standards applicable by each volume, to be used by the conflict detection and resolution services of U-space.This work has been partially funded by the SESAR Joint Undertaking, a body of the European Commission, under grant H2020 RIA-763551 and by the Ministry of Economy and Enterprise of Spain under contract TRA2016-77012-R.Peer ReviewedPostprint (published version

Global IT strategic plan for universities in Spain

Many reports show up every year to measure the development and social use of the Information Technologies (IT) in given territorial areas [1, 2]. They exhibit key aspects of reality using a set of indicators. On consolidated reports one can find more qualitative information through the indicator evolution over years. The knowledge society, which Europe drew in Lisbon, leans on a modern higher education system with innovative methods and resources. Universities, that were pioneer in introducing computation and Internet for research, have been walking fast adopting IT also for student instruction, management and government. In Spain, this evolution was sometimes lacked of reflection and evaluation. For this reason, the IT Working Group of the Spanish Association of University Rectors (CRUE in Spanish set of initials) drove in 2004 the confection of an inquest in order to achieve a global assessment of IT in universities [3]. The results showed that the Spanish Universities, in general, adopt a compromised aim with the introduction and use of IT, but frequently it is more reactive than proactive, more improvised than planned. In this paper we explain the work developed inside the IT Working Group of the CRUE, with members from different universities and different knowledge areas that have been working in the next approach of the IT inquest. We resolved to introduce an IT Strategic Plan, shared by all universities in Spain. The aim is to have a flexible but strong tool to guide the IT department on the politic priorities.Peer Reviewe

A new technique based on mini-UAS for estimating water and bottom radiance contributions in optically shallow waters

The mapping of nearshore bathymetry based on spaceborne radiometers is commonly used for QC ocean colour products in littoral waters. However, the accuracy of these estimates is relatively poor with respect to those derived from Lidar systems due in part to the large uncertainties of bottom depth retrievals caused by changes on bottom reflectivity. Here, we present a method based on mini unmanned aerial vehicles (UAS) images for discriminating bottom-reflected and water radiance components by taking advantage of shadows created by different structures sitting on the bottom boundary. Aerial surveys were done with a drone Draganfly X4P during October 1 2013 in optically shallow waters of the Saint Lawrence Estuary, and during low tide. Colour images with a spatial resolution of 3 mm were obtained with an Olympus EPM-1 camera at 10 m height. Preliminary results showed an increase of the relative difference between bright and dark pixels (dP) toward the red wavelengths of the camera's receiver. This is suggesting that dP values can be potentially used as a quantitative proxy of bottom reflectivity after removing artefacts related to Fresnel reflection and bottom adjacency effects.Peer ReviewedPostprint (published version

Performance measures of the SESAR southwest functional airspace block

To face the challenges of the increasing air traffic demand the ICAO proposed the Performance Based Approach (PBA) as the methodology to apply for the modernization of the Air Traffic Management (ATM). Improvements for enhancing the en route air traffic efficiency include more direct route options and flexible airspace structures. In Europe airspace structures are fragmented by State boundaries avoiding cross-border sector configurations. Functional Airspace Blocks (FAB) are operational instruments of SESAR to facilitate the implementation of the Essential Operational Changes. In the Southwest FAB the plan to introduce Free Route Airspace (FRA) across States is the main change foreseen. The Southwest FAB comprises Portuguese and Spanish airspaces and with the FRA there will be no longer discrete crossing points. The relevance of SW FAB is due to its geographical situation, being one of the most important interconnection nodes for the American transatlantic flights and the European northern-southern corridor. In the paper we provide some measures of the expected benefits of introducing the FRA in Southwest FAB. The aim of the measures is to be useful for the performance analysis of the Southwest FAB development and the FRA already started in May 2014.Peer ReviewedPostprint (author's final draft

Improving real-time drone detection for counter-drone systems

The number of unmanned aerial vehicles (UAVs, also known as drones) has increased dramatically in the airspace worldwide for tasks such as surveillance, reconnaissance, shipping and delivery. However, a small number of them, acting maliciously, can raise many security risks. Recent Artificial Intelligence (AI) capabilities for object detection can be very useful for the identification and classification of drones flying in the airspace and, in particular, are a good solution against malicious drones. A number of counter-drone solutions are being developed, but the cost of drone detection ground systems can also be very high, depending on the number of sensors deployed and powerful fusion algorithms. We propose a low-cost counter-drone solution composed uniquely by a guard-drone that should be able to detect, locate and eliminate any malicious drone. In this paper, a state-of-the-art object detection algorithm is used to train the system to detect drones. Three existing object detection models are improved by transfer learning and tested for real-time drone detection. Training is done with a new dataset of drone images, constructed automatically from a very realistic flight simulator. While flying, the guard-drone captures random images of the area, while at the same time, a malicious drone is flying too. The drone images are auto-labelled using the location and attitude information available in the simulator for both drones. The world coordinates for the malicious drone position must then be projected into image pixel coordinates. The training and test results show a minimum accuracy improvement of 22% with respect to state-of-the-art object detection models, representing promising results that enable a step towards the construction of a fully autonomous counter-drone system.Peer ReviewedPostprint (published version

Countering a drone in a 3D space: Analyzing deep reinforcement learning methods

Unmanned aerial vehicles (UAV), also known as drones have been used for a variety of reasons and the commercial drone market growth is expected to reach remarkable levels in the near future. However, some drone users can mistakenly or intentionally fly into flight paths at major airports, flying too close to commercial aircraft or invading people’s privacy. In order to prevent these unwanted events, counter-drone technology is needed to eliminate threats from drones and hopefully they can be integrated into the skies safely. There are various counter-drone methods available in the industry. However, a counter-drone system supported by an artificial intelligence (AI) method can be an efficient way to fight against drones instead of human intervention. In this paper, a deep reinforcement learning (DRL) method has been proposed to counter a drone in a 3D space by using another drone. In a 2D space it is already shown that the deep reinforcement learning method is an effective way to counter a drone. However, countering a drone in a 3D space with another drone is a very challenging task considering the time required to train and avoid obstacles at the same time. A Deep Q-Network (DQN) algorithm with dueling network architecture and prioritized experience replay is presented to catch another drone in the environment provided by an Airsim simulator. The models have been trained and tested with different scenarios to analyze the learning progress of the drone. Experiences from previous training are also transferred before starting a new training by pre-processing the previous experiences and eliminating those considered as bad experiences. The results show that the best models are obtained with transfer learning and the drone learning progress has been increased dramatically. Additionally, an algorithm which combines imitation learning and reinforcement learning is implemented to catch the target drone. In this algorithm, called deep q-learning from demonstrations (DQfD), expert demonstrations data and self-generated data by the agent are sampled and the agent continues learning without overwriting the demonstration data. The main advantage of this algorithm is to accelerate the learning process even if there is a small amount of demonstration data.This work was funded partially by the AGAUR under grant 2020PANDE00141, the Ministry of Science and Innovation of Spain under grant PID2020-116377RB-C21 and the SESAR Joint Undertaking (JU) project CORUS-XUAM, under grant SESAR-VLD2 101017682. The JU receives support from the European Union’s Horizon 2020 research and innovation program and SESAR JU members other than the Union.Peer ReviewedPostprint (published version

Counter a drone and the performance analysis of deep reinforcement learning method and human pilot

Artificial Intelligence (AI) has been used in different research areas in aerospace to create an intelligent system. Especially, an unmanned aerial vehicle (UAV), known as a drone, can be controlled by AI methods such as deep reinforcement learning (DRL) in different purposes. Drones with DRL become more intelligent and eventually they can be fully autonomous. In this paper, DRL method supported by real time object detection model is proposed to detect and catch a drone. Additionally, the results are analyzed by comparing the time to catch the target drone in seconds between DRL method, human pilot and an algorithm which directs the drone towards the target position without using any AI method or navigation and guidance method. The main idea is to catch a drone in an environment as fast as possible without crashing any obstacles inside the environment. In DRL method, the agent is a quadcopter drone and it is rewarded in each time step by the environment provided by Airsim flight simulator. Drone is trained to catch the target drone by using DRL model which is based on deep Q-Network algorithm. After training, the tests have been made by the agent drone with DRL model and human pilots to catch stationary and non-stationary target drone. The training and test results show that the agent drone learns to catch target drone which can be a stationary and a non-stationary. In addition. the agent avoids crashing any obstacles in the environment with a minimum success rate of 94%. Also, DRL model performance is compared with the human pilot performances and the agent with DRL model shows better time to catch the target drone. Human pilots struggle to control the drone by using remote controller when catching the target in simulation. However, the agent with DRL model is rarely missing the target when trying to catch the target.This work was funded partially by the AGAUR under grant 2020PANDE00141, the Ministry of Science and Innovation of Spain under grant PID2020-116377RB-C21 and the SESAR Joint Undertaking (JU) project CORUS-XUAM, under grant SESAR-VLD2 101017682. The JU receives support from the European Union’s Horizon 2020 research and innovation programme and the SESAR JU members other than the Union. Special thanks to Dr. Pablo Royo Chic for his assistance to our paper by piloting a drone to catch the target drone.Postprint (published version

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