Energy-efficient resource management for continuous scenario fulfillment by UAV fleets

Unbemannte Luftfahrzeuge (unmanned aerial vehicles, UAV) sind autonom fliegende und flexibel einsetzbare mobile Roboter, welche durch ihre große Flexibilität und Erweiterbarkeit viele Möglichkeiten bieten. Insbesondere im Bereich der Katastrophenbewältigung erlangen sie immer stärkere Bedeutung, da die Aufgaben zur Aufklärung im Gebiet und zur Erschaffung einer Kommunikationsinfrastruktur ungebunden und schnell durch sie bewältigt werden können. Der Forschungsschwerpunkt dieser Arbeit liegt in der Herausforderung der Ressourcenverwaltung in einem solchen Szenario. Während die Priorität des UAV Einsatzes klar darin besteht die Katastrophenbekämpfung unterbrechungsfrei zu unterstützten, muss ebenso auf die Verwaltung limitierter Ressourcen, wie elektrischer Energie, eingegangen werden. Wir präsentieren ein entsprechendes Systemdesign einer Ressourcenverwaltung und Strategien zur Verbesserung der Leistung und damit zur Erhöhung der Energieeffizienz des Gesamtsystems. Die Implementierung und gründliche Untersuchung eines solchen komplexen Systems von Teilsystemen ist verbunden mit hohen finanziellen Kosten, großem Test-Risiko und einer langen Entwicklungsdauer. Aus diesem Grund setzt diese Arbeit auf abstrakte ausführbare Modelle der Umgebung, des Verwaltungssystems und der UAVs. Die Verwendung dieser Modelle in einer Massensimulation mit beliebiger Komplexität und Konfiguration ermöglicht die schnelle und kostengünstige Verifikation der Funktionstüchtigkeit und die Bewertung verschiedener Verwaltungsstrategien. Im Vergleich zu der präsentierten trivialen Lösung ist die entwickelte verbesserte Lösung in der Lage den zeitlichen Anteil einzelner UAVs im Missionseinsatz zu erhöhen und die insgesamt nötige Menge an UAVs für die dauerhafte Abdeckung aller Aufgaben zu reduzieren. Die Schritte zur Optimierung reduzierten im analysierten Beispiel den Gesamtenergiebedarf aller UAVs um nahezu 20 Prozent.Unmanned aerial vehicles (UAV) are autonomous and flexible robotic systems with a remarkable degree of freedom and extendibility. They are especially valuable in the context of disaster scenarios, where arising use cases for reconnaissance and mobile communication infrastructure creation have to be addressed rapidly and unbound from restrictions in the operation field. The research focus of this thesis lies in the challenge of resource management during such an application. While the priority of the UAV utilization lies on uninterrupted task execution, concern for limited resources, like electrical energy, and resultant maintenance processes has to be dealt with on a lower management layer. We present a resource management system design and multiple competing strategies to improve its performance and overall energy efficiency. The implementation and thorough evaluation of such a complex system of systems is linked to high costs, great operational risks, and a long development time. For that reason, we developed executable models representing the environment, the resource management system, and the UAV. Through mass simulation of these models in various scenario constellations and configurations, we are able to verify the applicability of our proposed resource management system and to evaluate and optimize various aspects of its processes. In comparison to a presented trivial solution, we are able to increase the UAV flight utilization efficiency and decrease the needed amount of provided UAVs in the scenario. Our optimization efforts reduce the overall energy demand of UAVs in the analyzed example scenario by almost 20 percent

Autonomisen multikopteriparven hallinta etsintä- ja pelastustehtävissä

This thesis presents the requirements and implementation of a Ground Control Station (GCS) application for controlling a fleet of multicopters to perform a Search And Rescue (SAR) mission. The requirements are put together by analysing existing drone types, SAR practices, and available GCS applications. Multicopters are found to be the most feasible drone to use for the SAR use case because of their maneuverability, despite not having the best endurance. Several existing area coverage methods are presented and their usefulness is analyzed for SAR scenarios where different amounts of prior knowledge is available. It is stated that most search patterns can be used with a fleet of drones, by creating drone formations and by dividing the target area into sub-areas. It is noted that most currently available GCS applications are focused on controlling a single drone for either industrial or hobby use. A proof of concept prototype is developed on top of an open source GCS and tested in field tests. Based on all the previous learnings from the protype and research, a new GCS is designed and developed. The development on optimizing communications between the GCS and the autopilot leads to a filed patent application. The new software is tested with three multicopters in a water rescue scenario and several user interface improvements are made as a result of the learnings. The development of a GCS for controlling a drone fleet for search and rescue is proven feasible.Työssä esitetään multikopteriparven hallintaan käytettävän Ground Control Station (GCS) ohjelmiston vaatimukset ja toteutus Search And Rescue (SAR) etsintä- ja pelastustehtävien suorittamiseksi. Vaatimukset kootaan yhteen analysoimalla saatavilla olevia droonityyppejä, SAR pelastuskäytäntöjä, sekä GCS ohjelmistoja. Multikopterit osoittautuvat liikkuvuutensa ansiosta pelastustehtäviin sopivimmaksi vaihtoehdoksi, vaikka niiden saavutettavissa oleva lentoaika ei ole parhaimmasta päästä. Erilaisia etsintämetodeja esitetään alueiden kattamiseksi ja niiden hyödyllisyyttä analysoidaan SAR tilanteissa, joissa ennakkotietoa on saatavilla vaihtelevasti. Osoitetaan, että useimpia etsintäalgoritmeja voidaan hyödyntää drooniparvella, muodostamalla lentomuodostelmia, sekä jakamalla kohdealue pienempiin osa-alueisiin. Huomataan, että suurin osa tällä hetkellä saatavilla olevista GCS ohjelmistoista on suunnattu teollisuuden tai harrastelijoiden käyttöön, pääasiassa yksittäisen droonin hallintaan. Prototyyppi kehitetään avoimen lähdekoodin GCS ohjelmiston pohjalta ja testataan kenttätesteissä. Tästä saadun tiedon avulla suunnitellaan ja kehitetään uusi GCS ohjelmisto. Kehitystyö viestinnän optimoinniksi autopilotin ja GCS ohjelmiston välillä johtaa patenttihakemukseen. Uusi ohjelmisto testataan kolmella multikopterilla vesipelastustilanteessa ja sen seurauksena käyttöliittymään tehdään useita parannuksia. GCS ohjelmiston luominen drooniparven hallintaan etsintä- ja pelastustehtävissä todetaan mahdolliseksi

Dependability Analysis Methodology for FPGA-Based UAV Communication Protocols using UPPAAL-SMC

UAVs are multifaceted devices that have enormous versatility and flexibility in a plethora of various fields. Year over year, UAVs see a tremendous amount of research invested in it to make them more efficient and autonomous when performing a task. This increase in autonomy requires the UAVs to have a dependable link between them to exchange crucial information like current position and speed. These messages are transmitted to avoid collisions and perform missions efficiently. The communication between UAVs depends on several factors like the used telemetry device, distance between the UAVs, speed of the UAVs, and application environment. Hence, an UAV designer must analyze the reliability of the communication based on the desired application environment and necessary communication components in the UAV. Faults can also propagate in UAV components built using the FPGA technology when they are placed in harsh radiation environments like radiation monitoring. These errors can lead to complications in the operation of an UAV communication component, and hence, FPGAs require techniques like blind scrubbing to mitigate these faults. The availability of the communication component can be impacted when using this mitigation approach. Therefore, investigating the optimal configuration to maintain high and consistent availability is crucial. This thesis presents a methodology to perform high-level dependability analysis for UAV communication protocols using statistical model checking. First, we evaluate the reliability of a point-to-point UAV communication using the UAV-UAV framework. The main objective of this framework is to investigate the link reliability between UAVs based on the specifications of the telemetry device and the availability of the communication components. To accomplish this, we propose models to emulate the behavior of two UAVs in air, the condition of the transmitter and receiver, and the data exchange phase between two UAVs. Then, we analyze the availability of an UAV communication module in a harsh radiation environment using blind scrubbing as a mitigation approach. The peak availability of UAV-UAV and UAV-GCS communication components is investigated through the UAV-UAV and UAV-GCS frameworks. The two frameworks utilize the SEU rate computed from the RTL code of the communication component design. Then, implement crucial features like scrubbing interval and scrub time in the transmitter and receiver modules to find the optimal scrubbing interval when the UAV communications with other UAVs or the GCS. Finally, the effect of these faults and limitations of blind scrubbing is also investigated in our work

Reference Model for Interoperability of Autonomous Systems

This thesis proposes a reference model to describe the components of an Un-manned Air, Ground, Surface, or Underwater System (UxS), and the use of a single Interoperability Building Block to command, control, and get feedback from such vehicles. The importance and advantages of such a reference model, with a standard nomenclature and taxonomy, is shown. We overview the concepts of interoperability and some efforts to achieve common refer-ence models in other areas. We then present an overview of existing un-manned systems, their history, characteristics, classification, and missions. The concept of Interoperability Building Blocks (IBB) is introduced to describe standards, protocols, data models, and frameworks, and a large set of these are analyzed. A new and powerful reference model for UxS, named RAMP, is proposed, that describes the various components that a UxS may have. It is a hierarchical model with four levels, that describes the vehicle components, the datalink, and the ground segment. The reference model is validated by showing how it can be applied in various projects the author worked on. An example is given on how a single standard was capable of controlling a set of heterogeneous UAVs, USVs, and UGVs

Feature Papers of Drones - Volume I

[EN] The present book is divided into two volumes (Volume I: articles 1–23, and Volume II: articles 24–54) which compile the articles and communications submitted to the Topical Collection ”Feature Papers of Drones” during the years 2020 to 2022 describing novel or new cutting-edge designs, developments, and/or applications of unmanned vehicles (drones). Articles 1–8 are devoted to the developments of drone design, where new concepts and modeling strategies as well as effective designs that improve drone stability and autonomy are introduced. Articles 9–16 focus on the communication aspects of drones as effective strategies for smooth deployment and efficient functioning are required. Therefore, several developments that aim to optimize performance and security are presented. In this regard, one of the most directly related topics is drone swarms, not only in terms of communication but also human-swarm interaction and their applications for science missions, surveillance, and disaster rescue operations. To conclude with the volume I related to drone improvements, articles 17–23 discusses the advancements associated with autonomous navigation, obstacle avoidance, and enhanced flight plannin

Optimization and Communication in UAV Networks

UAVs are becoming a reality and attract increasing attention. They can be remotely controlled or completely autonomous and be used alone or as a fleet and in a large set of applications. They are constrained by hardware since they cannot be too heavy and rely on batteries. Their use still raises a large set of exciting new challenges in terms of trajectory optimization and positioning when they are used alone or in cooperation, and communication when they evolve in swarm, to name but a few examples. This book presents some new original contributions regarding UAV or UAV swarm optimization and communication aspects

Automation and Control

Advances in automation and control today cover many areas of technology where human input is minimized. This book discusses numerous types and applications of automation and control. Chapters address topics such as building information modeling (BIM)–based automated code compliance checking (ACCC), control algorithms useful for military operations and video games, rescue competitions using unmanned aerial-ground robots, and stochastic control systems

Aerial base station placement in temporary-event scenarios

Die Anforderungen an den Netzdatenverkehr sind in den letzten Jahren dramatisch gestiegen, was ein großes Interesse an der Entwicklung neuartiger Lösungen zur Erhöhung der Netzkapazität in Mobilfunknetzen erzeugt hat. Besonderes Augenmerk wurde auf das Problem der Kapazitätsverbesserung bei temporären Veranstaltungen gelegt, bei denen das Umfeld im Wesentlichen dynamisch ist. Um der Dynamik der sich verändernden Umgebung gerecht zu werden und die Bodeninfrastruktur durch zusätzliche Kapazität zu unterstützen, wurde der Einsatz von Luftbasisstationen vorgeschlagen. Die Luftbasisstationen können in der Nähe des Nutzers platziert werden und aufgrund der im Vergleich zur Bodeninfrastruktur höheren Lage die Vorteile der Sichtlinienkommunikation nutzen. Dies reduziert den Pfadverlust und ermöglicht eine höhere Kanalkapazität. Das Optimierungsproblem der Maximierung der Netzkapazität durch die richtige Platzierung von Luftbasisstationen bildet einen Schwerpunkt der Arbeit. Es ist notwendig, das Optimierungsproblem rechtzeitig zu lösen, um auf Veränderungen in der dynamischen Funkumgebung zu reagieren. Die optimale Platzierung von Luftbasisstationen stellt jedoch ein NP-schweres Problem dar, wodurch die Lösung nicht trivial ist. Daher besteht ein Bedarf an schnellen und skalierbaren Optimierungsalgorithmen. Als Erstes wird ein neuartiger Hybrid-Algorithmus (Projected Clustering) vorgeschlagen, der mehrere Lösungen auf der Grundlage der schnellen entfernungsbasierten Kapazitätsapproximierung berechnet und sie auf dem genauen SINR-basierten Kapazitätsmodell bewertet. Dabei werden suboptimale Lösungen vermieden. Als Zweites wird ein neuartiges verteiltes, selbstorganisiertes Framework (AIDA) vorgeschlagen, welches nur lokales Wissen verwendet, den Netzwerkmehraufwand verringert und die Anforderungen an die Kommunikation zwischen Luftbasisstationen lockert. Bei der Formulierung des Platzierungsproblems konnte festgestellt werden, dass Unsicherheiten in Bezug auf die Modellierung der Luft-Bodensignalausbreitung bestehen. Da dieser Aspekt im Rahmen der Analyse eine wichtige Rolle spielt, erfolgte eine Validierung moderner Luft-Bodensignalausbreitungsmodelle, indem reale Messungen gesammelt und das genaueste Modell für die Simulationen ausgewählt wurden.As the traffic demands have grown dramatically in recent years, so has the interest in developing novel solutions that increase the network capacity in cellular networks. The problem of capacity improvement is even more complex when applied to a dynamic environment during a disaster or temporary event. The use of aerial base stations has received much attention in the last ten years as the solution to cope with the dynamics of the changing environment and to supplement the ground infrastructure with extra capacity. Due to higher elevations and possibility to place aerial base stations in close proximity to the user, path loss is significantly smaller in comparison to the ground infrastructure, which in turn enables high data capacity. We are studying the optimization problem of maximizing network capacity by proper placement of aerial base stations. To handle the changes in the dynamic radio environment, it is necessary to promptly solve the optimization problem. However, we show that the optimal placement of aerial base stations is the NP-hard problem and its solution is non-trivial, and thus, there is a need for fast and scalable optimization algorithms. This dissertation investigates how to solve the placement problem efficiently and to support the dynamics of temporary events. First, we propose a novel hybrid algorithm (Projected Clustering), which calculates multiple solutions based on the fast distance-based capacity approximation and evaluates them on the accurate SINR-based capacity model, avoiding sub-optimal solutions. Second, we propose a novel distributed, self-organized framework (AIDA), which conducts a decision-making process using only local knowledge, decreasing the network overhead and relaxing the requirements for communication between aerial base stations. During the formulation of the placement problem, we found that there is still considerable uncertainty with regard to air-to-ground propagation modeling. Since this aspect plays an important role in our analysis, we validated state-of-the-art air-to-ground propagation models by collecting real measurements and chose the most accurate model for the simulations