UAV degradation identification for pilot notification using machine learning techniques

Unmanned Aerial Vehicles are currently investigated as an important sub-domain of robotics, a fast growing and truly multidisciplinary research field. UAVs are increasingly deployed in real-world settings for missions in dangerous environments or in environments which are challenging to access. Combined with autonomous flying capabilities, many new possibilities, but also challenges, open up. To overcome the challenge of early identification of degradation, machine learning based on flight features is a promising direction. Existing approaches build classifiers that consider their features to be correlated. This prevents a fine-grained detection of degradation for the different hardware components. This work presents an approach where the data is considered uncorrelated and, using machine learning techniques, allows the precise identification of UAV’s damages

A Briefing on Metrics and Risks for Autonomous Decision-Making in Aerospace Applications

Significant technology advances will enable future aerospace systems to safely and reliably make decisions autonomously, or without human interaction. The decision-making may result in actions that enable an aircraft or spacecraft in an off-nominal state or with slightly degraded components to achieve mission performance and safety goals while reducing or avoiding damage to the aircraft or spacecraft. Some key technology enablers for autonomous decision-making include: a continuous state awareness through the maturation of the prognostics health management field, novel sensor development, and the considerable gains made in computation power and data processing bandwidth versus system size. Sophisticated algorithms and physics based models coupled with these technological advances allow reliable assessment of a system, subsystem, or components. Decisions that balance mission objectives and constraints with remaining useful life predictions can be made autonomously to maintain safety requirements, optimal performance, and ensure mission objectives. This autonomous approach to decision-making will come with new risks and benefits, some of which will be examined in this paper. To start, an account of previous work to categorize or quantify autonomy in aerospace systems will be presented. In addition, a survey of perceived risks in autonomous decision-making in the context of piloted aircraft and remotely piloted or completely autonomous unmanned autonomous systems (UAS) will be presented based on interviews that were conducted with individuals from industry, academia, and government

USMC VERTICAL TAKEOFF AND LANDING AIRCRAFT: HUMAN–MACHINE TEAMING FOR CONTROLLING UNMANNED AERIAL SYSTEMS

The United States Marine Corps (USMC) is investing in aviation technologies through its Vertical Takeoff and Landing (VTOL) aircraft program that will enhance mission superiority and warfare dominance against both conventional and asymmetric threats. One of the USMC program initiatives is to launch unmanned aerial systems (UAS) from future human-piloted VTOL aircraft for collaborative hybrid (manned and unmanned) missions. This hybrid VTOL-UAS capability will support USMC intelligence, surveillance, and reconnaissance (ISR), electronic warfare (EW), communications relay, and kinetic strike air to ground missions. This capstone project studied the complex human-machine interactions involved in the future hybrid VTOL-UAS capability through model-based systems engineering analysis, coactive design interdependence analysis, and modeling and simulation experimentation. The capstone focused on a strike coordination and reconnaissance (SCAR) mission involving a manned VTOL platform, a VTOL-launched UAS, and a ground control station (GCS). The project produced system requirements, a system architecture, a conceptual design, and insights into the human-machine teaming aspects of this future VTOL capability.Major, United States ArmyMajor, United States ArmyMajor, United States ArmyMajor, United States ArmyMajor, United States ArmyApproved for public release. Distribution is unlimited

Early Forest FIre Detection using UAV and Computer Vision

Τα τελευταία χρόνια, ένα σημαντικό περιβαλλοντικό πρόβλημα με τεράστιες οικονομικές συνέπειες που απασχολεί τις περισσότερες ευρωπαϊκές χώρες είναι οι δασικές πυρκα- γιές. Οι κλασικές τεχνικές αναγνώρισης και αντιμετώπισης είναι ανεπαρκείς με μεγάλες απώλειες να σημειώνονται κάθε χρόνο εξαιτίας τους. Τα τελευταία χρόνια έχουν γίνει πολ- λές προτάσεις στον τομέα της πυρανίχνευσης, με τις περισσότερες από αυτές να είναι πολύ δαπανηρές και τεχνολογικά προηγμένες, με αποτέλεσμα απαιτούν αρκετά μεγάλες τεχνολογικές υποδομές για τη συντήρησή τους. Η λύση που προτείνουμε σε αυτή την έρευνα αφορά την αναγνώριση πυρκαγιάς με χρήση μη επανδρωμένου αεροσκάφους σε συνδυασμό με μηχανική όραση. Πιο συγκεκριμένα, έχουμε εκπαιδεύσει ένα μοντέλο αναγνώρισης αντικειμένων (Yolov5) μέσω ενός προσαρ- μοσμένου συνόλου δεδομένων (εικόνων) πυρκαγιάς. Στη συνέχεια, το drone μας κατά την πτήση χρησιμοποιεί αυτό το συγκεκριμένο μοντέλο για τον εντοπισμό δασικών πυρκα- γιών. Στη συνέχεια, τα δεδομένα που συλλέγουμε από το drone αποστέλλονται μέσω του υπολογιστή σε έξυπνες συσκευές και μέσα από μια εφαρμογή που θα έχουν εγκαταστή- σει οι πυροσβεστικές αρχές στα κινητά τους τηλέφωνα θα μπορούν να δουν αμέσως τον τόπο, την ώρα, την ημερομηνία αλλά και τη φωτογραφία της πυρκαγιάς , προκειμένου να παρέμβουν άμεσα για την ελέγξουν αποτελεσματικά.In recent years, a major environmental problem with huge economic consequences that concern most European countries are forest fires. Classical identification and treatment techniques are found to be insufficient with large losses occurring each year due to them. There have been many proposals in the field of fire detection, with most of them being very costly and technologically advanced, requiring large technological infrastructure to maintain them. The solution we propose in this research concerns fire identification using UAVs combined with computer vision. More specifically, we have trained an object recognition model (Yolov5) through a custom fire dataset (images). The data collected by the drone are sent through the computer to smart devices and through an application that the fire authorities will have installed on their mobile phones, they can immediately see the place, the time, the date and also the photo of the fire, in order to intervene immediately and control it effectively

NtoM: a concept of operations for pilots of multiple remotely piloted aircraft

The concept of operations proposed here pursues the feasibility, from a human factors perspective, of having a single pilot/aircrew controlling several remotely piloted aircraft systems at once in non-segregated airspace. To meet such feasibility, this multitasking must be safe and not interfere with the job of the air traffic controllers due to delays or errors associated with parallel piloting. To that end, a set of measures at several levels is suggested, which includes workload prediction and balance, pilot activity monitoring, and a special emphasis on interface usability and the pilot’s situational awareness. The concept relies greatly on the exploitation of the potential of Controller-Pilot Data Link Communications, anticipating future widespread implementation and full use. Experiments comparing the performance of the same pseudo-pilots before and after the implementation of part of the measures showed a decrease in the number of errors, oversights and subjective stress.Peer ReviewedPostprint (published version

EXPEDITIONARY LOGISTICS: A LOW-COST, DEPLOYABLE, UNMANNED AERIAL SYSTEM FOR AIRFIELD DAMAGE ASSESSMENT

Airfield Damage Repair (ADR) is among the most important expeditionary activities for our military. The goal of ADR is to restore a damaged airfield to operational status as quickly as possible. Before the process of ADR can begin, however, the damage to the airfield needs to be assessed. As a result, Airfield Damage Assessment (ADA) has received considerable attention. Often in a damaged airfield, there is an expectation of unexploded ordnance, which makes ADA a slow, difficult, and dangerous process. For this reason, it is best to make ADA completely unmanned and automated. Additionally, ADA needs to be executed as quickly as possible so that ADR can begin and the airfield restored to a usable condition. Among other modalities, tower-based monitoring and remote sensor systems are often used for ADA. There is now an opportunity to investigate the use of commercial-off-the-shelf, low-cost, automated sensor systems for automatic damage detection. By developing a combination of ground-based and Unmanned Aerial Vehicle sensor systems, we demonstrate the completion of ADA in a safe, efficient, and cost-effective manner.http://archive.org/details/expeditionarylog1094561346Outstanding ThesisLieutenant, United States NavyApproved for public release; distribution is unlimited

Requirements for digitized aircraft spotting (Ouija) board for use on U.S. Navy aircraft carriers

This thesis will evaluate system and process elements to initiate requirements modeling necessary for the next generation Digitized Aircraft Spotting (Ouija) Board for use on U.S. Navy aircraft carriers to track and plan aircraft movement. The research will examine and evaluate the feasibility and suitability of transforming the existing two-dimensional static board to an electronic, dynamic display that will enhance situational awareness by using sensors and system information from various sources to display a comprehensive operational picture of the current flight and hangar decks aboard aircraft carriers. The authors will evaluate the current processes and make recommendations on elements the new system would display. These elements include what information is displayed, which external systems feed information to the display, and how intelligent agents could be used to transform the static display to a powerful decision support tool. Optimally, the Aircraft Handler will use this system to effectively manage the Flight and Hangar decks to support the projection of air power from U.S. aircraft carriers.http://archive.org/details/requirementsford109454447Lieutenant Commander, United States NavyLieutenant Commander, United States Navy ReserveApproved for public release; distribution is unlimited

Autonomous Vehicles

This edited volume, Autonomous Vehicles, is a collection of reviewed and relevant research chapters, offering a comprehensive overview of recent developments in the field of vehicle autonomy. The book comprises nine chapters authored by various researchers and edited by an expert active in the field of study. All chapters are complete in itself but united under a common research study topic. This publication aims to provide a thorough overview of the latest research efforts by international authors, open new possible research paths for further novel developments, and to inspire the younger generations into pursuing relevant academic studies and professional careers within the autonomous vehicle field