Unmanned Aerial Vehicles (UAVs) have become increasingly important in assisting 5G and beyond 5G (B5G) mobile networks. Indeed, UAVs have all the potentials to both satisfy the ever-increasing mobile data demands of such mobile networks and provide ubiquitous connectivity to different kinds of wireless devices. However, the UAV assistance paradigm faces a set of crucial issues and challenges. For example, the network management of current UAV-assisted systems is time consuming, complicated, and carried out manually, thus causing a multitude of interoperability issues. To efficiently address all these issues, Software-Defined Network (SDN) and Network Function Virtualization (NFV) are two promising technologies to efficiently manage and improve the UAV assistance for the next generation of mobile networks. In the literature, no clear guidelines are describing the different use cases of SDN and NFV in the context of UAV assistance to terrestrial networks, including mobile networks. Motivated by this fact, in this survey, we guide the reader through a comprehensive discussion of the main characteristics of SDN and NFV technologies. Moreover, we provide a thorough analysis of the different classifications, use cases, and challenges related to UAV-assisted systems. We then discuss SDN/NFV-enabled UAV-assisted systems, along with several case studies and issues, such as the involvement of UAVs in cellular communications, monitoring, and routing, to name a few. We furthermore present a set of open research challenges, high-level insights, and future research directions related to UAV-assisted systems.

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Softwarization of UAV Networks: A Survey of Applications and Future Trends

Current access infrastructures are characterized by heterogeneity, low latency, high throughput, and high computational capability, enabling massive concurrent connections and various services. Unfortunately, this design does not pay significant attention to mobile services in underserved areas. In this context, the use of aerial radio access networks (ARANs) is a promising strategy to complement existing terrestrial communication systems. Involving airborne components such as unmanned aerial vehicles, drones, and satellites, ARANs can quickly establish a flexible access infrastructure on demand. ARANs are expected to support the development of seamless mobile communication systems toward a comprehensive sixth-generation (6G) global access infrastructure. This paper provides an overview of recent studies regarding ARANs in the literature. First, we investigate related work to identify areas for further exploration in terms of recent knowledge advancements and analyses. Second, we define the scope and methodology of this study. Then, we describe ARAN architecture and its fundamental features for the development of 6G networks. In particular, we analyze the system model from several perspectives, including transmission propagation, energy consumption, communication latency, and network mobility. Furthermore, we introduce technologies that enable the success of ARAN implementations in terms of energy replenishment, operational management, and data delivery. Subsequently, we discuss application scenarios envisioned for these technologies. Finally, we highlight ongoing research efforts and trends toward 6G ARANs.

Survey on Aerial Radio Access Networks: Toward a Comprehensive 6G Access Infrastructure

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Dynamics Of Atmospheric Flight

The spread of Unmanned Aerial Vehicles (UAVs) in the last decade revolutionized many applications fields. Most investigated research topics focus on increasing autonomy during operational campaigns, environmental monitoring, surveillance, maps, and labeling. To achieve such complex goals, a high-level module is exploited to build semantic knowledge leveraging the outputs of the low-level module that takes data acquired from multiple sensors and extracts information concerning what is sensed. All in all, the detection of the objects is undoubtedly the most important low-level task, and the most employed sensors to accomplish it are by far RGB cameras due to costs, dimensions, and the wide literature on RGB-based object detection. This survey presents recent advancements in 2D object detection for the case of UAVs, focusing on the differences, strategies, and trade-offs between the generic problem of object detection, and the adaptation of such solutions for operations of the UAV. Moreover, a new taxonomy that considers different heights intervals and driven by the methodological approaches introduced by the works in the state of the art instead of hardware, physical and/or technological constraints is proposed.

A Survey of Computer Vision Methods for 2D Object Detection from Unmanned Aerial Vehicles

Real-time data collection and decision making with drones will play an important role in precision livestock and farming. Drones are already being used in precision agriculture. Nevertheless, this is not the case for indoor livestock and farming environments due to several challenges and constraints. These indoor environments are limited in physical space and there is the localization problem, due to GPS unavailability. Therefore, this work aims to give a step toward the usage of drones for indoor farming and livestock management. To investigate on the drone positioning in these workspaces, two visual simultaneous localization and mapping (VSLAM)—LSD-SLAM and ORB-SLAM—algorithms were compared using a monocular camera onboard a small drone. Several experiments were carried out in a greenhouse and a dairy farm barn with the absolute trajectory and the relative pose error being analyzed. It was found that the approach that suits best these workspaces is ORB-SLAM. This algorithm was tested by performing waypoint navigation and generating maps from the clustered areas. It was shown that aerial VSLAM could be achieved within these workspaces and that plant and cattle monitoring could benefit from using affordable and off-the-shelf drone technology.

Visual slam for indoor livestock and farming using a small drone with a monocular camera : A feasibility study

In the last decade, different indoor flight navigation systems for small Unmanned Aerial Vehicles (UAVs) have been investigated, with a special focus on different configurations and on sensor technologies. The main idea of this paper is to propose a distributed Guidance Navigation and Control (GNC) system architecture, based on Robotic Operation System (ROS) for light weight UAV autonomous indoor flight. The proposed framework is shown to be more robust and flexible than common configurations. A flight controller and companion computer running ROS for control and navigation are also included in the section. Both hardware and software diagrams are given to show the complete architecture. Further works will be based on the experimental validation of the proposed configuration by indoor flight tests.

A novel distributed architecture for UAV indoor navigation

The large diffusion of Unmanned Aircraft Systems (UAS) requires a suitable strategy to design safe flight missions. In this paper, we propose a novel path planning strategy to compute optimum risk path for UAS over populated areas.The proposed strategy is based on a variant of the RRT* (Rapidly-exploring Random Tree "Star") algorithm, performing a risk assessment during the path planning phase. Like other RRT-based algorithms, the proposed path planning explores the state space by constructing a graph. Each time a new node is added to the graph, the algorithm estimates the risk level involved by the new node, evaluating the flight direction and velocity of the UAS placed in the analyzed node.The risk level quantifies the risk of flying over a specific location and it is defined using a probabilistic risk assessment approach taking into account the drone parameters and environmental characteristics.Then, the proposed algorithm computes an asymptotically optimal path by minimizing the overall risk and flight time.Simulation results in realistic environments corroborate the proposed approach proving how the proposed risk-based path planning is able to compute an effective and safe path in urban areas.

A Risk-based Path Planning Strategy to Compute Optimum Risk Path for Unmanned Aircraft Systems over Populated Areas

Despite many commercial applications make extensive use of Unmanned Aircraft Systems (UAS), there is still lack of published data about their performance under unconventional weather conditions. In the last years, multirotors and fixed wing vehicles, commonly referred to as drones, have been studied in wind environments so that stability and controllability have been improved. However, other important weather variables have impact on UAS performance and they should be properly investigated for a deeper understanding of such vehicles. The primary objective of our study is the preliminary characterization of a propeller in a climate-controlled chamber. Mechanical and electrical data have been measured while testing the propeller at low pressure and cold temperatures. Test results point out that thrust and electric power are strongly affected by air density. A comparison between the experimental data and the results of the Blade Element Theory is carried out to assess the theory capability to estimate thrust in unconventional environments. The overlap between experimental data and theory computation is appropriate despite geometrical uncertainties and corroborate the need of a reliable aerodynamic database. Propeller performance data under unconventional atmospheres will be leveraged to improve UAS design, propulsion system modelling as well as provide guidelines to certify operations in extreme environments.

An Experimental Analysis on Propeller Performance in a Climate-controlled Facility

Unmanned Aircraft Systems (UAS) have become prevalent for a wide spectrum of civilian applications. Support tools and technologies for UAS-based monitoring of smart cities and communities are under development, where Unmanned Aerial Vehicles (UAVs) are the main means of implementation. UAVs provide the eye-in-the-sky alternative to ground-based monitoring, contributing to safety, early anomaly detection and possibly prediction, and improving everyday quality of life with little disruption of, and interference with, humans. This paper presents a simulated real-world environment and accurate model of the city of Turin (Italy) and implements it in the Gazebo software physics simulator, with the aim of monitoring the city. The environment allows for piloting and navigating UAVs with on-board cameras and other onboard sensors through a Ground Control Station (GCS), or through manual direct piloting. Simulated scenarios illustrate monitoring over the perimeter of the urban area, autonomous flight, partially autonomous flight and manual piloting of the UAV. It is expected that obtained results will pave the way to developing complex simulated world environments where candidate scenarios will be developed and executed before real-world testing.

Urban Monitoring of Smart Communities Using UAS

The use of Unmanned Aerial Vehicles (UAVs) is now common, but although they have been for various applications, there are still a lot of challenges that need to be overcome. One key issue is related to standardizing the use of these vehicles in urban environments and guaranteeing a minimum risk level for the population. To rise to these challenges, autonomous strategies that optimize and coordinate vehicles in cooperative missions and avoid human operators should be developed. The novelty of this paper is the development of an autonomous urban mission coordinator, which is responsible for the high-level logistics of a fleet of heterogeneous vehicles. A multi-variable weighted algorithm based on a tree optimization method is also proposed.

Matteo Scanavino

Papers

A novel distributed architecture for UAV indoor navigation

A Risk-based Path Planning Strategy to Compute Optimum Risk Path for Unmanned Aircraft Systems over Populated Areas

An Experimental Analysis on Propeller Performance in a Climate-controlled Facility

Urban Monitoring of Smart Communities Using UAS

A Mission Coordinator Approach for a Fleet of UAVs in Urban Scenarios