UAVs are increasingly being employed to carry out surveillance, parcel delivery, communication-support and other specific tasks. Their equipment and mission plan are carefully selected to minimize the carried load an overall resource consumption. Typically, several single task UAVs are dispatched to perform different missions. In certain cases, (part of) the geographical area of operation may be common to these single task missions (such as those supporting post-disaster recovery) and it may be more efficient to have multiple tasks carried out as part of a single UAV mission using common or even additional specialized equipment. In this paper, we propose and investigate a joint planning of multitask missions leveraging a fleet of UAVs equipped with a standard set of accessories enabling heterogeneous tasks. To this end, an optimization problem is formulated yielding the optimal joint planning and deriving the resulting quality of the delivered tasks. In addition, a heuristic solution is developed for large-scale environments to cope with the increased complexity of the optimization framework. The developed joint planning of multitask missions is applied to a specific post-disaster recovery scenario of a flooding in the San Francisco area. The results show the effectiveness of the proposed solutions and the potential savings in the number of UAVs needed to carry out all the tasks with the required level of quality.
Beside military and security operations, the usage of UAVs is envisioned in a plethora of civil applications, ranging from agriculture to environmental monitoring and disaster management (see @cite_19 for a thorough taxonomy and survey). In the following, we focus on the three types of tasks encompassed in the scenario under study.
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Unmanned aerial vehicles (UAVs), or aerial drones, are an emerging technology with significant market potential. UAVs may lead to substantial cost savings in, for instance, monitoring of difficult-to-access infrastructure, spraying fields and performing surveillance in precision agriculture, as well as in deliveries of packages. In some applications, like disaster management, transport of medical supplies, or environmental monitoring, aerial drones may even help save lives. In this article, we provide a literature survey on optimization approaches to civil applications of UAVs. Our goal is to provide a fast point of entry into the topic for interested researchers and operations planning specialists. We describe the most promising aerial drone applications and outline characteristics of aerial drones relevant to operations planning. In this review of more than 200 articles, we provide insights into widespread and emerging modeling approaches. We conclude by suggesting promising directions for future research. Unmanned aerial vehicles (UAVs) were initially developed for military monitoring and surveillance tasks but found several interesting applications in the civilian domain. A promising application technology is to use drone small cells (DSCs) to expand wireless communication coverage on demand. Rapid deployment along with limited operating costs are key factors that boost the development of DSCs for both military and civilian utilizations. DSCs are rapidly deployable to provide connectivity for temporary users (e.g. attendees of festivals, sporting events, or seminars), or over disaster areas to replace damaged communication infrastructure. UAVs are battery-powered, which makes energy consumption optimization a critical issue for acceptable performance, high availability, and an economically viable DCS deployment. In this article we focus on the scheduling of beaconing periods as an efficient means of energy consumption optimization. The conducted study provides a sub-modular game perspective of the problem and investigates its structural properties. We also provide a learning algorithm that ensures convergence of the considered UAV network with its unique Nash equilibrium operating point. Finally, we conduct extensive numerical investigations to assist our claims about the energy efficiency of the strategic beaconing policy (at Nash equilibrium). Distributed Processing Systems are the ones that include multiple devices (which could be of many types, such as PC computers, mobile devices etc.) that have computational and communication capabilities. Their computational power is jointly used for collaborative processing of variety of tasks – and this processing is realized in distributed manner. UAV - Unmanned Aerial Vehicles (also called drones) gain significant attention over recent years. They have been employed to realize multiple tasks such as surveillance or environmental monitoring. First implementations were based on single UAV, later the potential of multiple UAVs collaborating in a team was noticed. Many applications were implemented in distributed manner, using multiple collaborative UAVs and the distributed processing systems principles. In this paper we survey the applications implemented over cooperative teams of UAVs that operate as distributed processing systems. Wireless sensor networks emerged as a key technology for prolonged, unsupervised monitoring in a wide spectrum of applications, from biological and environmental to civil and military. The sensor networks should operate autonomously for a long period of time under stringent resource and energy constraints. Energy conservation and power-awareness have become a focus of a number of research efforts, as sensor network nodes must operate on batteries or use energy extracted from the environment, such as solar energy or vibrations. Runtime power measurements and characterization of real existing systems are crucial for studies that target power optimizations, including techniques for dynamic adaptation based on the current energy status. This paper introduces an environment for unobtrusive real-time power monitoring that could be used for a number of wireless sensor platforms. We describe our methodology for calibration and validation of the environment and give empirical data for the Telos wireless sensor platform when it runs a subset of representative applications. Unmanned Aerial Vehicles (UAVs) are used in numerous military and civil application areas, and they have gained prominence in the research community. A UAV has to operate in a complex environment and checks the control points in the mission area by satisfying different constraints of the assigned task. Therefore, the path planning problem is one of the important areas in UAV researches. If the number of control points increases in the Unmanned Aerial System (UAS), finding a feasible solution in this large search space takes up a great deal of time. Nowadays low-cost UAVs are available, and this enables the use of multi-UAV systems to perform different tasks more efficiently and quickly. This usage increases the complexity of effective path planning and task allocation problem. This paper presents a flyable path planning for multi-UAV systems by using a Genetic Algorithm (GA) in a known environment at a constant altitude. A feasible path is firstly calculated by GAs, and then this path is smoothed by using Bezier curves. Experimental results indicate that the proposed approach produces effective and feasible paths for each UAV in a multi-UAV system. System is implemented in Java with a GUI for presenting results. The paper also draws future works that can be done on this topic. There are numerous applications of unmanned aerial vehicles (UAVs) in the management of civil infrastructure assets. A few examples include routine bridge inspections, disaster management, power line surveillance and traffic surveying. As UAV applications become widespread, increased levels of autonomy and independent decision-making are necessary to improve the safety, efficiency, and accuracy of the devices. This paper details the procedure and parameters used for the training of convolutional neural networks (CNNs) on a set of aerial images for efficient and automated object recognition. Potential application areas in the transportation field are also highlighted. The accuracy and reliability of CNNs depend on the network’s training and the selection of operational parameters. This paper details the CNN training procedure and parameter selection. The object recognition results show that by selecting a proper set of parameters, a CNN can detect and classify objects with a high level of accuracy (97.5 ) and computational efficiency. Furthermore, using a convolutional neural network implemented in the “YOLO” (“You Only Look Once”) platform, objects can be tracked, detected (“seen”), and classified (“comprehended”) from video feeds supplied by UAVs in real-time. Abstract The aquatic unmanned aerial vehicle (AquaUAV), a kind of vehicle that can operate both in the air and the water, has been regarded as a new breakthrough to broaden the application scenario of UAV. Wide application prospects in military and civil field are more than bright, therefore many institutions have focused on the development of such a vehicle. However, due to the significant difference of the physical properties between the air and the water, it is rather difficult to design a fully-featured AquaUAV. Until now, majority of partially-featured AquaUAVs have been developed and used to verify the feasibility of an aquatic–aerial vehicle. In the present work, we classify the current partially-featured AquaUAV into three categories from the scope of the whole UAV field, i.e., the seaplane UAV, the submarine-launched UAV, and the submersible UAV. Then the recent advancements and common characteristics of the three kinds of AquaUAVs are reviewed in detail respectively. Then the applications of bionics in the design of AquaUAV, the transition mode between the air and the water, the morphing wing structure for air–water adaptation, and the power source and the propulsion type are summarized and discussed. The tradeoff analyses for different transition methods between the air and the water are presented. Furthermore, it indicates that applying the bionics into the design and development of the AquaUAV will be essential and significant. Finally, the significant technical challenges for the AquaUAV to change from a conception to a practical prototype are indicated. Unmanned aerial vehicles (UAVs) are well-suited to a wide range of mission scenarios, such as search and rescue, border patrol, and military surveillance. The complex and distributed nature of these missions often requires teams of UAVs to work together. Furthermore, overall mission performance can be strongly influenced by vehicle failures or degradations, so an autonomous mission system must account for the possibility of these anomalies if it is to maximize performance. This paper presents a general health management methodology for designing mission systems that can anticipate the negative effects of various types of anomalies on the future mission state and choose actions that mitigate those effects. The formulation is then specialized to the problem of providing persistent surveillance coverage using a group of UAVs, where uncertain fuel usage dynamics and strong interdependence effects between vehicles must be considered. Finally, the paper presents results showing that the health-aware persistent surveillance planner based on this formulation exhibits excellent performance in both simulated and real flight test experiments. In recent years, Unmanned Aerial Vehicles (UAVs) have been used in many military and civil application areas, due to their increased endurance, performance, portability, and their larger payload-carrying, computing and communication capabilities. Because of UAVs' complex operation areas and complicated constraints related to the assigned task, they have to fly on a path, which is calculated online and or offline to satisfy these constraints and to check some control points in the operation theatre. If the number of control points and constraints increases, finding a feasible solution takes up too much time in this large operation area. In this case, the use of multi-UAVs decreases operation completion time; however, this usage increases the complexity of finding a feasible path problem. This problem is typically NP-hard and genetic algorithms have been successfully utilized for solving it in the last few decades. This paper presents how a flyable trajectory can be constructed for multi-UAV systems by using a Genetic Algorithm (GA) in a known environment and at a constant altitude. A GA is implemented parallel in a multi-core environment to increase the performance of the system. First, a feasible path is calculated by using a parallel GA, and then the path is smoothed by using Bezier curves to convert it flyable. Preliminary results show that the proposed method provides an effective and feasible path for each UAV in an Unmanned Aerial System with multi-UAVs. The proposed system is realized in Java with a GUI for showing results. This paper also outlines future work that can be conducted on the multi-UAV path planning. The significant advances of hardware manufacturing technology and the development of efficient software algorithms make technically and economically feasible a network composed of numerous, small, low-cost sensors using wireless communications, that is, a wireless sensor network. WSNs have attracted intensive interest from both academia and industry due to their wide application in civil and military scenarios. In hostile scenarios, it is very important to protect WSNs from malicious attacks. Due to various resource limitations and the salient features of a wireless sensor network, the security design for such networks is significantly challenging. In this article, we present a comprehensive survey of WSN security issues that were investigated by researchers in recent years and that shed light on future directions for WSN security. Wireless sensor networks (WSNs) comprising of tiny, power-constrained nodes are gaining popularity due to their potential for use in a wide variety of environments like monitoring of environmental attributes, intrusion detection, and various military and civilian applications. While the sensing objectives of these environments are unique and application-dependent, a common performance criteria for wireless sensor networks is prolonging network lifetime while satisfying coverage and connectivity in the deployment region. Security is another important performance parameter in wireless sensor networks, where adverse and remote environments pose various kinds of threats to reliable network operation. In this paper, we look at the problems of security and energy efficiency and different formulations of these problems based on the approach of game theory. The potential applicability of WSNs to intruder detection environments also lends itself to game-theoretic formulation of these environments, where pursuit-evasion games provide a relevant framework to model detection, tracking and surveillance applications. The suitability of using game theory to study security and energy efficiency problems and pursuit-evasion scenarios using WSNs stems from the nature of strategic interactions between nodes. Approaches from game theory can be used to optimize node-level as well as network-wide performance by exploiting the distributed decision-making capabilities of WSNs. The use of game theory has proliferated, with a wide range of applications in wireless sensor networking. In the wake of this proliferation, we survey the use of game-theoretic approaches to formulate problems related to security and energy efficiency in wireless sensor networks. Small Unmanned Aerial Vehicles (UAVs) have been proposed for use in a variety of areas including hazard analysis, disaster monitoring, agricultural mapping, and so on. Currently, the development of flight control systems (FCS) for small UAVs is complicated, time-consuming and error-prone. To address these challenges, we present a reconfigurable flight control system architecture (RFCSA) for small UAVs. RFCSA allows rapidly integrating hardware modules and verifying control laws. It utilizes a modular-based framework, in which implementation details of a typical function are packaged into a function module. In addition, each function module in RFCSA is stand-alone with its own processors, memories, power conversion and communication interfaces. In this way, the system designer could be able to focus on the system implementation, rather than pay much attention to the design details of low-level functions. Moreover, an event-driven Service-Oriented Architectures (SOA) is proposed to minimize the coupling between modules and improve the performance of inter-module interactions. The paper is organized as follows: Firstly, the challenges and requirements for small UAVs' flight control systems are discussed. Secondly, the hardware architecture based on modular concept is developed. Thirdly, event driven SOA is proposed, and the mechanisms for modules to share information and coordinate activities are introduced. Finally, the conclusion and future work is pointed out. Wireless sensor networks are widely adopted in many location-sensitive applications including disaster management, environmental monitoring, military applications where the precise estimation of each node position is inevitably important when the absolute positions of a relatively small portion as anchor nodes of the underlying network were predetermined. Intrinsically, localization is an unconstrained optimization problem based on various distance path measures. Most of the existing localization methods focus on using different heuristic-based or mathematical techniques to increase the precision in position estimation. However, there were recent studies showing that nature-inspired algorithms like the ant-based or genetic algorithms can effectively solve many complex optimization problems. In this paper, we propose to adapt an evolutionary approach, namely a micro-genetic algorithm, as a post-optimizer into some existing localization methods such as the Ad-hoc Positioning System (APS) to further improve the accuracy of their position estimation. Obviously, our proposed MGA is highly adaptable and easily integrated into other localization methods. Furthermore, the remarkable improvements attained by our proposed MGA on both isotropic and anisotropic topologies of our simulation tests prompt for several interesting directions for further investigation. With the development of new technologies, these last years have witnessed the emergence of a new paradigm: the Internet of Things (IoT) and of the physical world. We are now able to communicate and interact with our surrounding environment through the use of multiple tiny sensors, RFID technologies or small wireless robots. This allows a set of new applications and usages to be envisioned ranging from logistic and traceability purposes to emergency and rescue operations going through the monitoring of volcanos or forest fires. However, all this comes with several technical and scientific issues like how to ensure the reliability of wireless communications in disturbed environments, how to manage efficiently the low resources (energy, memory, etc.) or how to set a safe and sustainable (both hardware and software) platform maintenance. All these issues are addressed by researchers all around the world but solutions designed for IoT need to face real experimentations to be validated. To ease such experimentations for IoT, several experimental testbeds have been deployed offering diverse and heterogeneous services and tools. In this article, we study the different requirements and features such facilities should offer. We survey the different experimental facilities currently available for the community, describe their characteristics. In particular, we detail the different hardware used for sensor networks and robot platforms and the scope of services the different facilities offer with a specific focus on testbeds which enable experimentations with mobility. We expect this survey assist a potential user to easily choose the one to use regarding his own needs. Finally, we identify existing gaps and difficulties and investigate new directions for such facilities. The complete modeling and simulation of an unmanned vehicle with combined aerial and underwater ca- pabilities, called Hybrid Unmanned Aerial Underwater Vehicle (HUAUV), is presented in this paper. The best architecture for this kind of vehicle was evaluated based on the adaptation of typical platforms for aerial and underwater vehicles, to allow the navigation in both environments. The model selected was based on a quadrotor-like aerial platform, adapted to dive and move underwater. Kinematic and dynamic models are presented here, and the parameters for a small dimension prototype was estimated and simulated. Finally, controllers were used and validated in realistic simulation, including air and water navigation, and the environment transition problem. To the best of our knowledge, it is the first vehicle that is able to navigate in both environment without mechanical adaptation during the medium transitions. I. INTRODUCTION Nowadays, unmanned autonomous vehicles have been the focus of many development efforts, with a large range of applications. The amount of resources applied has improved their capabilities, especially in the military field. Remotely operated or autonomous Unmanned Aerial Vehicles (UAVs), for example, were used in recent military operations around the world (1). But they were also used in non-military activities, like agriculture (2) and surveillance (3). Another important robotic platform are the Unmanned Underwater Vehicles (UUVs), whose the most known are the Remotely Operated Vehicles (ROVs). This kind of vehicles can also be applied in several commercial field operations (4), e.g. oil and gas extraction in ultra deep waters (5). Both kind of vehicles are well adapted to work in their own environment (air and water, respectively), but some situations may require a single vehicle capable of working in both environment. Such requirement commonly appears when is necessary to perform maintenance on partially or fully submersing structures, as ship hull or risers. A typical approach includes using auxiliary vessels to transport ROVs that will make the inspection of offshore target regions. This problem is harder in partially submersed structures. In such situations, where the usage of auxiliary ships is difficult and expensive, underwater robots equipped with wheels or tracks are recommended. Abstract Small drones are being utilized in monitoring, transport, safety and disaster management, and other domains. Envisioning that drones form autonomous networks incorporated into the air traffic, we describe a high-level architecture for the design of a collaborative aerial system consisting of drones with on-board sensors and embedded processing, coordination, and networking capabilities. We implement a multi-drone system consisting of quadcopters and demonstrate its potential in disaster assistance, search and rescue, and aerial monitoring. Furthermore, we illustrate design challenges and present potential solutions based on the lessons learned so far. Search and rescue operations can greatly benefit from the use of autonomous UAVs to survey the environment and collect evidence about the position of a missing person. To minimize the time to find the victim, some fundamental parameters need to be accounted for in the design of the search algorithms: 1) quality of sensory data collected by the UAVs, 2) UAVs energy limitations, 3) environmental hazards (e.g. winds, trees), 4) level of information exchange coordination between UAVs. In this paper, we discuss how these parameters can affect the search task and present some of the research avenues we have been exploring. We then study the performance of different search algorithms when the time to find the victim is the optimization criterion. This paper presents a methodology for classifying the literature of the Vehicle Routing Problem (VRP). VRP as a field of study and practice is defined quite broadly. It is considered to encompass all of the managerial, physical, geographical, and informational considerations as well as the theoretic disciplines impacting this ever emerging-field. Over its lifespan the VRP literature has become quite disjointed and disparate. Keeping track of its development has become difficult because its subject matter transcends several academic disciplines and professions that range from algorithm design to traffic management. Consequently, this paper defines VRP's domain in its entirety, accomplishes an all-encompassing taxonomy for the VRP literature, and delineates all of VRP's facets in a parsimonious and discriminating manner. Sample articles chosen for their disparity are classified to illustrate the descriptive power and parsimony of the taxonomy. Moreover, all previously published VRP taxonomies are shown to be relatively myopic; that is, they are subsumed by what is herein presented. Because the VRP literature encompasses esoteric and highly theoretical articles at one extremum and descriptions of actual applications at the other, the article sampling includes the entire range of the VRP literature. In recent years, the autonomous mobile robot has found diverse applications such as home health care system, surveillance system in civil and military applications and exhibition robot. For surveillance tasks such as moving target pursuit or following and patrol in a region using mobile robot, this paper presents a fuzzy Q-learning, as an intelligent control for cost-based navigation, for autonomous learning of suitable behaviors without the supervision or external human command. The Q-learning is used to select the appropriate rule of interval type-2 fuzzy rule base. The initial testing of the intelligent control is demonstrated by simulation as well as experiment of a simple wall-following based patrolling task of autonomous mobile robot. The modern battlefield scenario presents a number of challenges that highlights the importance of data gathering and information flow as crucial to the success of a military operation. In this context, network-centric warfare systems play an important role to bridge the gap between data and information sources and consumers. These systems are generally composed of different types of networks both deployed on the field and on back-end facilities. A paramount problem faced when conceiving the interconnection between the networked devices is how to provide robust and dependable connections to the devices on the field. This work tackles this issue proposing the use of the self-organizing paradigm to design efficient UAV relay networks to support military operations. As a proof of concept, realistic simulation experiments are performed providing clear indications of the claimed benefits of the self-organizing paradigm applied to these military systems.