Low Complexity Algorithms for Mission Completion Time Minimization in UAV-Based Emergency Response

This study aims to consider the improvement of technical inspection methods in the context of military conflict. Taking into account the factor of human safety in the survey process, the search for optimal methods and technologies for data collection and analysis is carried out, in particular, the use of modern technologies such as unmanned aerial vehicles (UAVs) and geographic information systems (GIS) is considered. The use of drones allows for quick and efficient surveys of hard-to-reach areas and the collection of high-quality damage data. The integration of UAVs with GIS provides a systematic view of large-scale destruction problems and helps to make informed decisions on how to address the consequences of armed aggression. Despite the advantages of using these technologies, their limitations should be taken into account. The combination of different survey methods allows to obtain the most complete and accurate information about the condition of buildings and structures, which is key in making decisions on the restoration and reconstruction of the affected areas. The study examined the practical use of these technologies within the framework of the Rebuild ua project, which digitized the main destructions in Chernihiv, Sumy, and Kyiv regions. The use of unmanned aerial vehicles in combination with geographic information technologies allows to systematize, analyze, obtain visual information and images with geometric data, which simplifies the process of damage assessment and decision-making during the survey procedure. GIS technologies are also capable of analyzing geographic data, visualizing damage on a map, locating damaged objects and assessing their damage. The integration of GIS technologies and unmanned aerial vehicles with other systems makes it possible to simplify decision-making on the priorities of liquidation and recovery.

URLLC in UAV-enabled multicasting systems: A dual time and energy minimization problem using UAV speed, altitude and beamwidth

: With the recent draw down of the US military after the end of the Cold War, the US Department of Defense (DoD) is placing considerable emphasis on employing a lighter, leaner, and more lethal military force to accomplish the strategic objectives mandated by political leadership. As a force enabler for military forces, Unmanned Aerial Vehicles (UAVs) recently demonstrated their potential during NATO military operations in Kosovo. Their activities were limited to merely gathering enemy intelligence. As a result of advancements in UAVs capabilities, UAVs expanded their operations by directly assisting in combat missions. The resulting logical question that evolved out of the Kosovo operations is, can UAVs be more actively integrated into other military functions to enhance mission accomplishment? More specifically, can UAVs be incorporated into the function of gaining and maintaining control of the air for US forces? To completely answer this question, this research paper will first analyze the current background of UAVs as seen in recent military operations. Secondly, the function of counterair will be examined to identify the deficiencies US forces have in obtaining control of the air. Third, the present and future capabilities that UAVs can bring to the fight will be identified. Fourth, a detailed examination of which UAVs payloads can be incorporated into the function of counterair will be accomplished. Fifth, this paper will show that UAVs can be effectively integrated to enhance US military weapons systems accomplishment of the function of counterair. Finally, this author will briefly discuss one possible plan to integrate UAVs in the function of counterair to overcome the noted deficiencies.

Unmanned aerial vehicles (UAVs) are envisioned to play a key role in intelligent transportation systems to complement the communication infrastructure in future smart cities. UAV-assisted vehicular networking research typically adopts throughput and latency as the main performance metrics. These conventional metrics, however, are not adequate to reflect the freshness of the information, an attribute that has been recently identified as a critical requirement to enable services such as autonomous driving and accident prevention. In this paper, we consider a UAV-assisted single-hop vehicular network, wherein sensors (e.g., LiDARs and cameras) on vehicles generate time sensitive data streams, and UAVs are used to collect and process this data while maintaining a minimum age of information (AoI). We aim to jointly optimize the trajectories of UAVs and find scheduling policies to keep the information fresh under minimum throughput constraints. The formulated optimization problem is shown to be mixed integer non-linear program (MINLP) and generally hard to be solved. Motivated by the success of machine learning (ML) techniques particularly deep learning in solving complex problems with low complexity, we reformulate the trajectories and scheduling policies problem as a Markov decision process (MDP) where the system state space considers the vehicular network dynamics. Then, we develop deep reinforcement learning (DRL) to learn the vehicular environment and its dynamics in order to handle UAVs' trajectory and scheduling policy. In particular, we leverage Deep Deterministic Policy Gradient (DDPG) for learning the trajectories of the deployed UAVs to efficiently minimize the Expected Weighted Sum AoI (EWSA). Simulations results demonstrate the effectiveness of the proposed design and show the deployed UAVs adapt their velocities during the data collection mission in order to minimize the AoI.

Unmanned aerial vehicles (UAVs) have been widely used to collect data from ground nodes (GNs) in various applications because of their flexibility and mobility. Some emergency situations pose strict requirements for UAV’s mission completion time. The current research focuses on the design and optimization of UAV’s flight trajectory to shorten the mission completion time. But the data collection process also greatly affects the mission completion time, especially when the uplink throughput is large. This article emphasizes the data collection process and proposes a V-shaped collection trajectory for the first time to shorten UAV’s collection time and then jointly optimizes the flight and collection trajectory to minimize the mission completion time. We formulate the mission completion time under energy constraints to an optimization problem and decompose it into three subproblems: 1) mission allocation of UAVs; 2) collection time minimization; and 3) joint optimization of collection and flight trajectory. 1) We establish the mission allocation model by setting the scaling factors for the flight and collection mission, and then solve it by utilizing the min–max multiple traveling salesman problem (MTSP) algorithm; 2) for the data collection process, we propose a V-shaped trajectory to minimize the data collection time and prove its validity; and 3) since the flight and collection trajectory of the adjacent GNs are deeply coupled, we model the joint optimization of the flight and the collection time as a mixed-integer nonlinear programming problem, and solve it by the joint optimization algorithm for flight trajectory and collection trajectory (JOFC). The simulation results show that our proposal outperforms the compared schemes.

Integration of Cognitive Radio Technology with unmanned aerial vehicles: Issues, opportunities, and future research challenges

<div>In this dissertation, methods for real-time trajectory generation and autonomous obstacle avoidance for fixed-wing and quad-rotor unmanned aerial vehicles (UAV) are studied. A key challenge for such trajectory generation is the high computation time required to plan a new path to safely maneuver around obstacles instantaneously. Therefore, methods for rapid generation of obstacle avoidance trajectory are explored. The high computation time is a result of the computationally intensive algorithms used to generate trajectories for real-time object avoidance. Recent studies have shown that custom solvers have been developed that are able to solve the problem with a lower computation time however these designs are limited to small sized problems or are proprietary. Additionally, for a swarm problem, which is an area of high interest, as the number of agents increases the problem size increases and in turn creates further computational challenges. A solution to these challenges will allow for UAVs to be used in autonomous missions robust to environmental uncertainties.</div><div><br></div><div>In this study, a trajectory generation problem posed as an optimal control problem is solved using a sequential convex programming approach; a nonlinear programming algorithm, for which custom solver is used. First, a method for feasible trajectory generation for fast-paced obstacle-rich environments is presented for the case of fixed-wing UAVs. Next, a problem of trajectory generation for fixed-wing and quad-rotor UAVs is defined such that starting from an initial state a UAV moves forward along the direction of flight while avoiding obstacles and remaining close to a reference path. The problem is solved within the framework of finite-horizon model predictive control. Finally, the problem of trajectory generation is extended to a swarm of quad-rotors where each UAV in a swarm has a reference path to fly along. Utilizing a centralized approach, a swarm scenario with moving targets is studied in two different cases in an attempt to lower the solution time; the first, solve the entire swarm problem at once, and the second, solve iteratively for a UAV in the swarm while considering trajectories of other UAVs as fixed.</div><div><br></div><div>Results show that a feasible trajectory for a fixed-wing UAV can be obtained within tens of milliseconds. Moreover, the obtained feasible trajectories can be used as initial guesses to the optimal solvers to speed up the solution of optimal trajectories. The methods explored demonstrated the ability for rapid feasible trajectory generation allowing for safe obstacle avoidance, which may be used in the case an optimal trajectory solution is not available. A comparative study between a dynamic and a kinematic model shows that the dynamic model provides better trajectories including aggressive trajectories around obstacles compared to the kinematic counterpart for fixed-wing UAVs, despite having approximately the same computational demands. Whereas, for the case of quad-rotor UAVs, the kinematic model takes almost half the solution time than with a reduced dynamic model, despite having approximately the similar range of values for the cost function. When extended to a swarm, solving the problem for each UAV is four to seven times computationally cheaper than solving the swarm as a whole. With the improved computation time for trajectory generation for a swarm of quad-rotors using centralized approach, the problem is now reasonably scalable, which opens up the possibility to increase the number of agents in a swarm using high-end computing machines for real-time applications. Overall, a custom solver jointly with a sequential convex programming approach solves an optimization problem in a low computation time.</div>

Fixed-wing unmanned aerial vehicles (UAVs) offer significant performance advantages over rotary-wing UAVs in terms of speed, endurance, and efficiency. However, these vehicles have traditionally been severely limited with regards to maneuverability. In this paper, we present a nonlinear control approach for enabling aerobatic fixed-wing UAVs to maneuver in constrained spaces. Our approach utilizes full-state direct trajectory optimization and a minimalistic, but representative, nonlinear aircraft model to plan aggressive fixed-wing trajectories in real-time at 5 Hz across high angles-of-attack. Randomized motion planning is used to avoid local minima and local-linear feedback is used to compensate for model inaccuracies between updates. We demonstrate our method in hardware and show that both local-linear feedback and re-planning are necessary for successful navigation of a complex environment in the presence of model uncertainty.

Unmanned aerial vehicles (UAVs)-based projects in the smart city paradigm are no more a slim chance. Crisis reaction and coordination, open or private infrastructure control, coordination or support to astute transportation systems are a portion of spaces in which UAV may end up key in the city of things. UAVs have pulled in a critical consideration in an assortment of fields, particularly in intelligent transportation systems. UAVs speak of a vital help to the current advancements to control street traffic and screen occurrences. The chapter investigates the integration of UAVs and future communication networks, such as 5G as part of the existing internet of things and cloud platforms. It aims at proposing an optimized UAV placement that assures the aforementioned characteristics. A framework is proposed for optimizing three dimensional placements and the mobility of UAVs. UAVs have pulled in a critical consideration in an assortment of fields, particularly in intelligent transportation systems.

In this paper, a survey on distributed control applications for multi Unmanned Aerial Vehicles (UAVs) systems is proposed. The focus is on consensus-based control, and both rotary-wing and fixed-wing UAVs are considered. On one side, the latest experimental configurations for the implementation of formation flight are analysed and compared for multirotor UAVs. On the other hand, the control frameworks taking into account the mobility of the fixed-wing UAVs performing target tracking are considered. This approach can be helpful to assess and compare the solutions for practical applications of consensus in UAV swarms.

The rise of IoT devices and big data generation pose challenges to Intelligent Transportation Systems (ITS), particularly in remote areas. Unmanned aerial vehicles (UAVs) have shown potential in enhancing computation and communication, but there is a lack of literature on a well-developed model that combines UAVs with a multi-hop, fog-cloud collaborative architecture. This work proposes an optimized multi-hop fog-cloud offloading architecture that uses UAVs to improve job offloading and resource allocation in ITS. The approach combines fog and cloud computing with UAV-enabled multi-hop collaboration to address the limitations of fog computing devices in processing large amounts of data and time-sensitive tasks. The offloading problem is formulated as a mixed integer nonlinear programming (MINLP) problem with constraints on user association, UAV positioning, task offloading, and resource allocation. The proposed multi-hop collaborative algorithm minimizes energy consumption, latency, and optimizes job prioritization across fog and cloud layers.

Energy optimization for full-duplex wireless-powered IoT networks using rotary-wing UAV with multiple antennas

This report introduces 3 post-processing techniques that can be used on images captured from an unmanned aerial vehicle (UAV) to distort the images in such a way as to make all the images appear as though they were taken from a uniform altitude, pitch, roll and yaw and from a rectilinear lens, meaning that when the images are stitched together the resulting map is accurate. The recent increase in demand for UAV technologies in the marketplace has been illustrated by a host of new products from major companies. The work in this report adds to the field of imaging from UAV's — for example, the creation of an aerial map. A current limitation in technology is the distortion of images captured by UAVs. This distortion is caused by turbulence as well as the necessary pitch and roll rotations required to move horizontally. This report introduces techniques which make images captured from fisheye lenses appear rectilinear; make all images appear of uniform scale, regardless of the height at which they were captured; and correct for any skew distortions caused by the pitch, roll and yaw of the UAV. All algorithms were developed in Matlab and tested on actual UAV images. This report focuses on rotary wing UAV's such as quad-copters and hexa-copters, although some of the principles discussed could be applied to fixed-wing aircraft as well.

In the field of autonomous transportation systems, the integration of Unmanned Aerial Vehicles (UAVs) in emergency response scenarios is important for enhancing the operational efficiency and the victims’ positioning. This article presents a novel Positioning, Navigation, and Timing (PNT) framework, named HEROES , which leverages the UAV and integrated sensing and communication technologies to address the challenges in post-disaster environments. Our approach focuses on a comprehensive post-disaster scenario involving multiple victims, first responders, UAVs, and an emergency control center. HEROES enables UAVs to function as anchor nodes and facilitate the precise positioning of the victims while simultaneously collecting critical data from the disaster area. We further introduce a reinforcement learning model based on the Optimistic Q-learning with Upper Confidence Bound algorithm, enabling the victims and first responders to autonomously select the most advantageous UAV connections based on their channel gain, shadowing probability, and positional characteristics. Furthermore, HEROES is based on a satisfaction game-theoretic model to enhance the sensing, communication, and positioning functionalities. Our analysis reveals the existence of various satisfaction equilibria, including minimum efficient satisfaction equilibrium, ensuring that the UAVs meet their quality of service constraints at minimal operational costs. Extensive experimental results validate the scalability and performance of HEROES, demonstrating significant improvements over existing state-of-the-art methods in delivering PNT services during humanitarian emergencies.

Uneven-aged mixed forests have been recognized as important contributors to biodiversity conservation, ecological stability, carbon sequestration, the provisioning of ecosystem services, and sustainable timber production. Recently, numerous studies have demonstrated the applicability of integrating remote sensing datasets with machine learning for forest management purposes, such as forest type classification and the identification of individual trees. However, studies focusing on the integration of unmanned aerial vehicle (UAV) datasets with machine learning for mapping of tree species groups in uneven-aged mixed forests remain limited. Thus, this study explored the feasibility of integrating UAV imagery with semantic segmentation-based machine learning classification algorithms to describe conifer and broadleaf species canopies in uneven-aged mixed forests. The study was conducted in two sub-compartments of the University of Tokyo Hokkaido Forest in northern Japan. We analyzed UAV images using the semantic-segmentation based U-Net and random forest (RF) classification models. The results indicate that the integration of UAV imagery with the U-Net model generated reliable conifer and broadleaf canopy cover classification maps in both sub-compartments, while the RF model often failed to distinguish conifer crowns. Moreover, our findings demonstrate the potential of this method to detect dominant tree species groups in uneven-aged mixed forests.