Design of Ad Hoc Wireless Mesh Networks Formed by Unmanned Aerial Vehicles with Advanced Mechanical Automation

Ad hoc wireless mesh networks formed by unmanned aerial vehicles (UAVs) equipped with wireless transceivers (access points (APs)) are increasingly being touted as being able to provide a flexible "on-the-fly" communications infrastructure that can collect and transmit sensor data from sensors in remote, wilderness, or disaster-hit areas. Recent advances in the mechanical automation of UAVs have resulted in separable APs and replaceable batteries that can be carried by UAVs and placed at arbitrary locations in the field. These advanced mechanized UAV mesh networks pose interesting questions in terms of the design of the network architecture and the optimal UAV scheduling algorithms. This paper studies a range of network architectures that depend on the mechanized automation (AP separation and battery replacement) capabilities of UAVs and proposes heuristic UAV scheduling algorithms for each network architecture, which are benchmarked against optimal designs.Comment: 12 page

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

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

Belief Space Scheduling

This thesis develops the belief space scheduling framework for scheduling under uncertainty in Stochastic Collection and Replenishment (SCAR) scenarios. SCAR scenarios involve the transportation of a resource such as fuel to agents operating in the field. Key characteristics of this scenario are persistent operation of the agents, and consideration of uncertainty. Belief space scheduling performs optimisation on probability distributions describing the state of the system. It consists of three major components---estimation of the current system state given uncertain sensor readings, prediction of the future state given a schedule of tasks, and optimisation of the schedule of the replenishing agents. The state estimation problem is complicated by a number of constraints that act on the state. A novel extension of the truncated Kalman Filter is developed for soft constraints that have uncertainty described by a Gaussian distribution. This is shown to outperform existing estimation methods, striking a balance between the high uncertainty of methods that ignore the constraints and the overconfidence of methods that ignore the uncertainty of the constraints. To predict the future state of the system, a novel analytical, continuous-time framework is proposed. This framework uses multiple Gaussian approximations to propagate the probability distributions describing the system state into the future. It is compared with a Monte Carlo framework and is shown to provide similar discrimination performance while computing, in most cases, orders of magnitude faster. Finally, several branch and bound tree search methods are developed for the optimisation problem. These methods focus optimisation efforts on earlier tasks within a model predictive control-like framework. Combined with the estimation and prediction methods, these are shown to outperform existing approaches

An Integrated Platform to Increase the Range/Endurance of Unmanned Helicopters

Class I (kg) autonomous helicopters are becoming increasingly popular for a wide range of non-military applications such as, surveillance, reconnaissance, traffic monitoring, emergency response, agricultural spraying, and many other eye in the sky missions. However, an efficient landing/takeoff platform with refueling/recharging capabilities has not yet been developed to increase the endurance and decrease the cost for Class I helicopters. This dissertation presents a three-prong approach for increasing the range and endurance of Class I autonomous helicopters, which will then spur demand by non-military organizations wanting to take advantage of such capabilities and, therefore, drop their price. The proposed Intelligent Self-Leveling and Nodal Docking System (ISLANDS) is developed as a mobile refueling/recharging station, which is one part of a three-pronged approach. ISLANDS is an electro-mechanical system that provides a safe landing surface for helicopters on gradients of up to 60%. ISLANDS operates off the grid and, therefore, must provide its own energy sources for the refueling/recharging tasks it performs. A method for determining ISLANDS\u27 energy needs for refueling/recharging of gas and/or electric helicopters for an arbitrary number of days is provided as the second part of the three-pronged approach. The final step for increasing autonomous helicopter endurance is a method for determining placement of ISLANDS nodes in the area to be serviced ensuring that the helicopters can achieve their mission goal. In this dissertation all aspects of the three-pronged approach are presented and explained in detail, providing experimental results that validate the proposed methods to solve each of the three problems. A case study using Commercially Off The Shelf (COTS) components that shows how all the parts of the proposed three-pronged solution work together for increasing the endurance of Class I helicopters is provided as a conclusion to the dissertation

A Novel Communications Protocol Using Geographic Routing for Swarming UAVs Performing a Search Mission

This research develops the UAV Search Mission Protocol (USMP) for swarming UAVs and determines the protocol\u27s effect on search mission performance. It is hypothesized that geographically routing USMP messages improves search performance by providing geography-dependent data to locations where it impacts search decisions. It is also proposed that the swarm can use data collected by the geographic routing protocol to accurately determine UAV locations and avoid sending explicit location updates. The hypothesis is tested by developing several USMP designs that are combined with the Greedy Perimeter Stateless Routing (GPSR) protocol and a search mission swarm logic into a single network simulation. The test designs use various transmission power levels, sensor types and swarm sizes. The simulation collects performance metrics for each scenario, including measures of distance traveled, UAV direction changes, number of searches and search concentration. USMP significantly improves mission performance over scenarios without inter-UAV communication. However, protocol designs that simply broadcast messages improve search performance by 83% in total searches and 20% in distance traveled compared to geographic routing candidates. Additionally, sending explicit location updates generates 3%-6% better performance per metric versus harvesting GPSR\u27s location information

Formulation of control strategies for requirement definition of multi-agent surveillance systems

In a multi-agent system (MAS), the overall performance is greatly influenced by both the design and the control of the agents. The physical design determines the agent capabilities, and the control strategies drive the agents to pursue their objectives using the available capabilities. The objective of this thesis is to incorporate control strategies in the early conceptual design of an MAS. As such, this thesis proposes a methodology that mainly explores the interdependency between the design variables of the agents and the control strategies used by the agents. The output of the proposed methodology, i.e. the interdependency between the design variables and the control strategies, can be utilized in the requirement analysis as well as in the later design stages to optimize the overall system through some higher fidelity analyses. In this thesis, the proposed methodology is applied to a persistent multi-UAV surveillance problem, whose objective is to increase the situational awareness of a base that receives some instantaneous monitoring information from a group of UAVs. Each UAV has a limited energy capacity and a limited communication range. Accordingly, the connectivity of the communication network becomes essential for the information flow from the UAVs to the base. In long-run missions, the UAVs need to return to the base for refueling with certain frequencies depending on their endurance. Whenever a UAV leaves the surveillance area, the remaining UAVs may need relocation to mitigate the impact of its absence. In the control part of this thesis, a set of energy-aware control strategies are developed for efficient multi-UAV surveillance operations. To this end, this thesis first proposes a decentralized strategy to recover the connectivity of the communication network. Second, it presents two return policies for UAVs to achieve energy-aware persistent surveillance. In the design part of this thesis, a design space exploration is performed to investigate the overall performance by varying a set of design variables and the candidate control strategies. Overall, it is shown that a control strategy used by an MAS affects the influence of the design variables on the mission performance. Furthermore, the proposed methodology identifies the preferable pairs of design variables and control strategies through low fidelity analysis in the early design stages.Ph.D