Swarming Reconnaissance Using Unmanned Aerial Vehicles in a Parallel Discrete Event Simulation

Current military affairs indicate that future military warfare requires safer, more accurate, and more fault-tolerant weapons systems. Unmanned Aerial Vehicles (UAV) are one answer to this military requirement. Technology in the UAV arena is moving toward smaller and more capable systems and is becoming available at a fraction of the cost. Exploiting the advances in these miniaturized flying vehicles is the aim of this research. How are the UAVs employed for the future military? The concept of operations for a micro-UAV system is adopted from nature from the appearance of flocking birds, movement of a school of fish, and swarming bees among others. All of these natural phenomena have a common thread: a global action resulting from many small individual actions. This emergent behavior is the aggregate result of many simple interactions occurring within the flock, school, or swarm. In a similar manner, a more robust weapon system uses emergent behavior resulting in no weakest link because the system itself is made up of simple interactions by hundreds or thousands of homogeneous UAVs. The global system in this research is referred to as a swarm. Losing one or a few individual unmanned vehicles would not dramatically impact the swarms ability to complete the mission or cause harm to any human operator. Swarming reconnaissance is the emergent behavior of swarms to perform a reconnaissance operation. An in-depth look at the design of a reconnaissance swarming mission is studied. A taxonomy of passive reconnaissance applications is developed to address feasibility. Evaluation of algorithms for swarm movement, communication, sensor input/analysis, targeting, and network topology result in priorities of each model\u27s desired features. After a thorough selection process of available implementations, a subset of those models are integrated and built upon resulting in a simulation that explores the innovations of swarming UAVs

UAS in the Airspace: A Review on Integration, Simulation, Optimization, and Open Challenges

Air transportation is essential for society, and it is increasing gradually due to its importance. To improve the airspace operation, new technologies are under development, such as Unmanned Aircraft Systems (UAS). In fact, in the past few years, there has been a growth in UAS numbers in segregated airspace. However, there is an interest in integrating these aircraft into the National Airspace System (NAS). The UAS is vital to different industries due to its advantages brought to the airspace (e.g., efficiency). Conversely, the relationship between UAS and Air Traffic Control (ATC) needs to be well-defined due to the impacts on ATC capacity these aircraft may present. Throughout the years, this impact may be lower than it is nowadays because the current lack of familiarity in this relationship contributes to higher workload levels. Thereupon, the primary goal of this research is to present a comprehensive review of the advancements in the integration of UAS in the National Airspace System (NAS) from different perspectives. We consider the challenges regarding simulation, final approach, and optimization of problems related to the interoperability of such systems in the airspace. Finally, we identify several open challenges in the field based on the existing state-of-the-art proposals

Motion Planning of UAV Swarm: Recent Challenges and Approaches

The unmanned aerial vehicle (UAV) swarm is gaining massive interest for researchers as it has huge significance over a single UAV. Many studies focus only on a few challenges of this complex multidisciplinary group. Most of them have certain limitations. This paper aims to recognize and arrange relevant research for evaluating motion planning techniques and models for a swarm from the viewpoint of control, path planning, architecture, communication, monitoring and tracking, and safety issues. Then, a state-of-the-art understanding of the UAV swarm and an overview of swarm intelligence (SI) are provided in this research. Multiple challenges are considered, and some approaches are presented. Findings show that swarm intelligence is leading in this era and is the most significant approach for UAV swarm that offers distinct contributions in different environments. This integration of studies will serve as a basis for knowledge concerning swarm, create guidelines for motion planning issues, and strengthens support for existing methods. Moreover, this paper possesses the capacity to engender new strategies that can serve as the grounds for future work

Data Gathering and Dissemination over Flying Ad-hoc Networks in Smart Environments

The advent of the Internet of Things (IoT) has laid the foundations for new possibilities in our life. The ability to communicate with any electronic device has become a fascinating opportunity. Particularly interesting are UAVs (Unmanned Airborne Vehicles), autonomous or remotely controlled flying devices able to operate in many contexts thanks to their mobility, sensors, and communication capabilities. Recently, the use of UAVs has become an important asset in many critical and common scenarios; thereby, various research groups have started to consider UAVs’ potentiality applied on smart environments. UAVs can communicate with each other forming a Flying Ad-hoc Network (FANET), in order to provide complex services that requires the coordination of several UAVs; yet, this also generates challenging communication issues. This dissertation starts from this standpoint, firstly focusing on networking issues and potential solutions already present in the state-of-the-art. To this aim, the peculiar issues of routing in mobile, 3D shaped ad-hoc networks have been investigated through a set of simulations to compare different ad-hoc routing protocols and understand their limits. From this knowledge, our work takes into consideration the differences between classic MANETs and FANETs, highlighting the specific communication performance of UAVs and their specific mobility models. Based on these assumptions, we propose refinements and improvements of routing protocols, as well as their linkage with actual UAV-based applications to support smart services. Particular consideration is devoted to Delay/Disruption Tolerant Networks (DTNs), characterized by long packet delays and intermittent connectivity, a critical aspect when UAVs are involved. The goal is to leverage on context-aware strategies in order to design more efficient routing solutions. The outcome of this work includes the design and analysis of new routing protocols supporting efficient UAVs’ communication with heterogeneous smart objects in smart environments. Finally, we discuss about how the presence of UAV swarms may affect the perception of population, providing a critical analysis of how the consideration of these aspects could change a FANET communication infrastructure

Resource allocation optimization problems in the public sector

This dissertation consists of three distinct, although conceptually related, public sector topics: the Transportation Security Agency (TSA), U.S. Customs and Border Patrol (CBP), and the Georgia Trauma Care Network Commission (GTCNC). The topics are unified in their mathematical modeling and mixed-integer programming solution strategies. In Chapter 2, we discuss strategies for solving large-scale integer programs to include column generation and the known heuristic of particle swarm optimization (PSO). In order to solve problems with an exponential number of decision variables, we employ Dantzig-Wolfe decomposition to take advantage of the special subproblem structures encountered in resource allocation problems. In each of the resource allocation problems presented, we concentrate on selecting an optimal portfolio of improvement measures. In most cases, the number of potential portfolios of investment is too large to be expressed explicitly or stored on a computer. We use column generation to effectively solve these problems to optimality, but are hindered by the solution time and large CPU requirement. We explore utilizing multi-swarm particle swarm optimization to solve the decomposition heuristically. We also explore integrating multi-swarm PSO into the column generation framework to solve the pricing problem for entering columns of negative reduced cost. In Chapter 3, we present a TSA problem to allocate security measures across all federally funded airports nationwide. This project establishes a quantitative construct for enterprise risk assessment and optimal resource allocation to achieve the best aviation security. We first analyze and model the various aviation transportation risks and establish their interdependencies. The mixed-integer program determines how best to invest any additional security measures for the best overall risk protection and return on investment. Our analysis involves cascading and inter-dependency modeling of the multi-tier risk taxonomy and overlaying security measurements. The model selects optimal security measure allocations for each airport with the objectives to minimize the probability of false clears, maximize the probability of threat detection, and maximize the risk posture (ability to mitigate risks) in aviation security. The risk assessment and optimal resource allocation construct are generalizable and are applied to the CBP problem. In Chapter 4, we optimize security measure investments to achieve the most cost-effective deterrence and detection capabilities for the CBP. A large-scale resource allocation integer program was successfully modeled that rapidly returns good Pareto optimal results. The model incorporates the utility of each measure, the probability of success, along with multiple objectives. To the best of our knowledge, our work presents the first mathematical model that optimizes security strategies for the CBP and is the first to introduce a utility factor to emphasize deterrence and detection impact. The model accommodates different resources, constraints, and various types of objectives. In Chapter 5, we analyze the emergency trauma network problem first by simulation. The simulation offers a framework of resource allocation for trauma systems and possible ways to evaluate the impact of the investments on the overall performance of the trauma system. The simulation works as an effective proof of concept to demonstrate that improvements to patient well-being can be measured and that alternative solutions can be analyzed. We then explore three different formulations to model the Emergency Trauma Network as a mixed-integer programming model. The first model is a Multi-Region, Multi-Depot, Multi-Trip Vehicle Routing Problem with Time Windows. This is a known expansion of the vehicle routing problem that has been extended to model the Georgia trauma network. We then adapt an Ambulance Routing Problem (ARP) to the previously mentioned VRP. There are no known ARPs of this magnitude/extension of a VRP. One of the primary differences is many ARPs are constructed for disaster scenarios versus day-to-day emergency trauma operations. The new ARP also implements more constraints based on trauma level limitations for patients and hospitals. Lastly, the Resource Allocation ARP is constructed to reflect the investment decisions presented in the simulation.Ph.D

Mobile Robots

The objective of this book is to cover advances of mobile robotics and related technologies applied for multi robot systems' design and development. Design of control system is a complex issue, requiring the application of information technologies to link the robots into a single network. Human robot interface becomes a demanding task, especially when we try to use sophisticated methods for brain signal processing. Generated electrophysiological signals can be used to command different devices, such as cars, wheelchair or even video games. A number of developments in navigation and path planning, including parallel programming, can be observed. Cooperative path planning, formation control of multi robotic agents, communication and distance measurement between agents are shown. Training of the mobile robot operators is very difficult task also because of several factors related to different task execution. The presented improvement is related to environment model generation based on autonomous mobile robot observations

DRONE DELIVERY OF CBNRECy – DEW WEAPONS Emerging Threats of Mini-Weapons of Mass Destruction and Disruption (WMDD)

Drone Delivery of CBNRECy – DEW Weapons: Emerging Threats of Mini-Weapons of Mass Destruction and Disruption (WMDD) is our sixth textbook in a series covering the world of UASs and UUVs. Our textbook takes on a whole new purview for UAS / CUAS/ UUV (drones) – how they can be used to deploy Weapons of Mass Destruction and Deception against CBRNE and civilian targets of opportunity. We are concerned with the future use of these inexpensive devices and their availability to maleficent actors. Our work suggests that UASs in air and underwater UUVs will be the future of military and civilian terrorist operations. UAS / UUVs can deliver a huge punch for a low investment and minimize human casualties.https://newprairiepress.org/ebooks/1046/thumbnail.jp

Upravljanje putanjama vazduhoplova u kontroli letenja na pre-taktičkom i taktičkom nivou

Global air traffic demand is continuously increasing, and it is predicted to be tripled by 2050. The need for increasing air traffic capacity motivates a shift of ATM towards Trajectory Based Operations (TBOs). This implies the possibility to design efficient congestion-free aircraft trajectories more in advance (pre-tactical, strategic level) reducing controller’s workload on tactical level. As consequence, controllers will be able to manage more flights. Current flow management practices in air traffic management (ATM) system shows that under the present system settings there are only timid demand management actions taken prior to the day of operation such as: slot allocation and strategic flow rerouting. But the choice of air route for a particular flight is seen as a commercial decision to be taken by airlines, given air traffic control constraints. This thesis investigates the potential of robust trajectory planning (considered as an additional demand management action) at pre-tactical level as a mean to alleviate the en-route congestion in airspace. Robust trajectory planning (RTP) involves generation of congestion-free trajectories with minimum operating cost taking into account uncertainty of trajectory prediction and unforeseen event. Although planned cost could be higher than of conventional models, adding robustness to schedules might reduce cost of disruptions and hopefully lead to reductions in operating cost. The most of existing trajectory planning models consider finding of conflict-free trajectories without taking into account uncertainty of trajectory prediction. It is shown in the thesis that in the case of traffic disturbances, it is better to have a robust solution otherwise newly generated congestion problems would be hard and costly to solve. This thesis introduces a novel approach for route generation (3D trajectory) based on homotopic feature of continuous functions. It is shown that this approach is capable of generating a large number of route shapes with a reasonable number of decision variables. Those shapes are then coupled with time dimension in order to create trajectories (4D)...Globalna potražnja za vazdušnim saobraćajem u stalnom je porastu i prognozira se da će broj letova biti utrostručen do 2050 godine. Potreba za povećanjem kapaciteta sistema vazdušnog saobraćaja motivisala je promene u sistemu upravljanja saobraćajnim tokovima u kome će u budućnosti centralnu ulogu imati putanje vazduhoplova tzv. “trajectory-based” koncept. Takav sistem omogućiće planiranje putanja vazduhoplova koje ne stvaraju zagušenja u sistemu na pre-taktičkom nivou i time smanjiti radno opterećenje kontrolora na taktičkom nivou. Kao posledica, kontrolor će moći da upravlja više letova nego u današnjem sistemu. Današnja praksa upravljanja saobraćajnim tokovima pokazuje da se mali broj upravljačkih akcija primenjuje pre dana obavljanja letova npr.: alokacija slotova poletanja i strateško upravljanje saobraćajnim tokovima. Međutim izbor putanje kojom će se odviti let posmatra se kao komercijalna odluka aviokompanije (uz poštovanje postavljenih ograničenja od strane kontrole letenja) i stoga je ostavljen na izbor avio-kompaniji. Većina, do danas razvijenih, modela upravljanja putanjama vazduhoplova ima za cilj generisanje bez-konfliktnih putanja, ne uzimajući u obzir neizvesnost u poziciji vazduhoplova. U ovoj doktorskoj disertaciji ispitivano je planiranje robustnih putanja vazduhoplova (RTP) na pre-taktičkom nivou kao sredstvo ublažavanja zagušenja u vazdušnom prostoru . Robustno upravljanje putanjama vazduhoplova podrazumeva izbor putanja vazduhoplova sa minimalnim operativnim troškovima koje ne izazivaju zagušenja u vazdušnom prostoru u uslovima neizvesnosti buduđe pozicije vazduhoplova i nepredviđenih događaja. Iako predviđeni (planirani) operativni troškovi robustnih putanja mogu u startu biti veći od operativnih troškova bez-konfliktnih putanja, robusnost može uticati na smanjenje troškove poremećaja putanja jer ne zahteva dodatnu promenu putanja vazduhplova radi izbegavanja konfliktnih situacija na taktičkom nivou. To na kraju može dovesti i do smanjenja stvarnih operativnih troškova. U tezi je pokazano, da je u slučaju poremećaja saobraćaja bolje imati robustno rešenje (putanje), jer novo-nastali problem zagušenosti vazdušnog prostora je teško i skupo rešiti..