Drone Trajectory Research Articles

Reconfigurable Intelligent Surface Enhanced Secure Aerial-Ground Communication

Reconfigurable intelligent surface (RIS), as a revolutionary technique, appears to ameliorate undesirable propagation environment in a controllable manner, which has the potential to substantially boost security of private information in the future smart radio environment. Much recent attention has been directed to the RIS-enhanced secure communication, among which the terrestrial network scenarios are generally concerned, while the exploration of aerial-ground communication integrated with RIS remains an open issue. Furthermore, in the available research, the ideal assumption of line-of-sight channel for aerial-ground link cannot be exploited in complex urban environment. Inspired by this, we provide in this paper the secrecy rate maximization problem under a complex urban scenario, where the drone's high mobility and RIS's tunable capability are utilized to against the potential eavesdropper. Although the established problem is intractable to tackle, we devise an iterative algorithm combining alternating optimization and successive convex approximation method as the solution, which jointly optimizes the phase shifts, the drone's trajectory along with transmit power for the single-user scenario. The proposed designs are also extended to the multi-user multi-eavesdropper system. Eventually, extensive simulation experiments indicate the remarkable benefits brought by our proposed scheme on secrecy performance and the necessity of optimizing phase shifts.

Next generation aircraft noise-mapping

At CSA, Centre for Sustainable Aviation at KTH Stockholm, several projects have run during the last 4 years. One outcome from this research is the SAFT-program for prediction of aircraft noise contours (noise-mapping) and time-histories in receiving points on ground. SAFT is a versatile and comprehensive tool already including several computational methods such as standard ECACdoc.29 method and more accurate time-stepping simulation-based representations of aircraft sound sources (frequency and direction dependent). The program allows for input of "general aircraft trajectory input" in the sense that either the trajectory data of concern is fitted to the current pre-defined formats or SAFT is easily updated to read a "new" format. Among the pre-defined formats of current version is csv-files prepared from OpenSky Historical database. From these kinds of data thrust and other noise-predictor variables are extracted and applied for noise-mapping. Moreover, SAFT allows for studies of aggregated air-traffic in defined areas as well as of single event flight-trajectories. And for these almost any metric (L, L, L, L, ...) might be extracted together with differences in dB, "Delta-dB", between any two scenarios or individual flights. Could be routing, runway-use, individual flight procedures etc. Anticipated future implementations involve drone trajectories and sound-source representations.

Drone Trajectory Segmentation for Real-Time and Adaptive Time-Of-Flight Prediction

This paper presents a method developed to predict the flight-time employed by a drone to complete a planned path adopting a machine-learning-based approach. A generic path is cut in properly designed corner-shaped standard sub-paths and the flight-time needed to travel along a standard sub-path is predicted employing a properly trained neural network. The final flight-time over the complete path is computed summing the partial results related to the standard sub-paths. Real drone flight-tests were performed in order to realize an adequate database needed to train the adopted neural network as a classifier, employing the Bayesian regularization backpropagation algorithm as training function. For the network, the relative angle between two sides of a corner and the wind condition are the inputs, while the flight-time over the corner is the output parameter. Then, generic paths were designed and performed to test the method. The total flight-time as resulting from the drone telemetry was compared with the flight-time predicted by the developed method based on machine learning techniques. At the end of the paper, the proposed method was demonstrated as effective in predicting possible collisions among drones flying intersecting paths, as a possible application to support the development of unmanned traffic management procedures.

Autonomous Flight Trajectory Control System for Drones in Smart City Traffic Management

With the exponential growth of numerous drone operations ranging from infrastructure monitoring to even package delivery services, the integration of UAS in the smart city transportation systems is an actual task that requires radically new, sustainable (safe, secure, with minimum environmental impact and life cycle cost) solutions. The primary objective of this proposed option is the definition of routes as desired and commanded trajectories and their autonomous execution. The airspace structure and fixed routes are given in the global GPS reference system with supporting GIS mapping. The concept application requires a series of further studies and solutions as drone trajectory (or corridor) following by an autonomous trajectory tracking control system, coupled with autonomous conflict detection, resolution, safe drone following, and formation flight options. The second part of the paper introduces such possible models and shows some results of their verification tests. Drones will be connected with the agency, designed trajectories to support them with factual information on trajectories and corridors. While the agency will use trajectory elements to design fixed or desired trajectories, drones may use the conventional GPS, infrared, acoustic, and visual sensors for positioning and advanced navigation. The accuracy can be improved by unique markers integrated into the infrastructure.

DNCS: New UAV navigation with considering the no-fly zone and efficient selection of the charging station

Due to Drones’ limited battery capacity, quick battery-charging machines (QCMs) are required to ensure long-distance flying. A QCM is chosen for a drone using the shortest path algorithm considering the waiting time at a QCM, charging time, End-to-End (E2E) flight time and other similar time and distance-based criteria for the drone services. However, these methods lack reckoning the varying costs of charging by multiple service providers. Furthermore, such algorithms commonly do not consider the avoidance of no-drone cross-over zones. It is supposed that drone navigation and charging stations (DNCS) are to clearly identify drone trajectories, so both the overall traveled distance and drones charging costs are minimized simultaneously (without crossing over the no-drone zones). The results of simulation reveal the proposed algorithm’s remarkable cost-effectiveness and its avoidance from the risk of crossing over no-fly zone compared to the current planning methods accounting for only the shortest-path- based drone trajectory.

Sensors and Communication Simulation for Unmanned Traffic Management.

Unmanned traffic management (UTM) systems will become a key enabler to the future drone market ecosystem, enabling the safe concurrent operation of both manned and unmanned aircrafts. Currently, these systems are usually tested by performing real scenarios that are costly, limited, hardly scalable, and poorly repeatable. As a solution, in this paper we propose an agent-based simulation platform, implemented through a micro service architecture, which may simulate UTM information sources, such as flight plans, telemetry messages, or tracks from a surveillance network. The final objective of this simulator is to use these information streams to perform a system-level evaluation of UTM systems both in the pre-flight and in-flight stages. The proposed platform, with a focus on simulation of communications and sensors, allows to model UTM actors’ behaviors and their interactions. In addition, it also considers the manual definition of events to simulate unexpected behaviors/events (contingencies), such as communications failures or pilots’ actions. In order to validate our architecture, we implemented a simulator that considers the following actors: drones, pilots, ground control stations, surveillance networks, and communications networks. This platform enables the simulation of the drone trajectory and control, the C2 (command and control) link, drone detection by surveillance sensors, and the communication of all agents by means of a mobile communications network. Our results show that it is possible to truthfully recreate complex scenarios using this simulator, mitigating the disadvantages of real testbeds.

Energy-Efficient Drone Trajectory Planning for the Localization of 6G-Enabled IoT Devices

6G will be an enabler for the massive Internet of Things (IoT) in which millions of devices communicate at high data rates and low latencies. One key area among 6G applications is advanced sensing. However, higher speed implies moving to higher frequencies, which generally require more transmission power. In remote sensing, this causes problems, since either we have to increase the number of sensors and lower their communications ranges or increase their ranges and accept faster battery depletion. To cut the cost, even localization modules are not usually included in sensors. However, in many applications, IoT sensors must know their locations. Recent advances in the field of drones have led to promising solutions for localization. In this article, we propose a novel approach called semidynamic mobile anchor guiding (SEDMAG) for drones which aims at energy-conservative trajectory planning and localization of massive IoT devices. In this approach, the drone tracks the shortest path over a connected graph. This path determines the visiting order of devices. But we show that the complexity of this approach is high, thus, a graph reduction approach is proposed. It reduces the complexity and decreases the drones' energy consumption and positioning delay. The drone then follows a weighted search algorithm (WSA) to dynamically visit the devices. Simulation results are used to verify the superiority of the proposed approach.

Performance Characterization of Canonical Mobility Models in Drone Cellular Networks

In this paper, we characterize the performance of several canonical mobility models in a drone cellular network in which drone base stations (DBSs) serve a set of user equipment (UE) on the ground. In particular, we consider the following four mobility models: (i) straight line (SL), (ii) random stop (RS), (iii) random walk (RW), and (iv) random waypoint (RWP), among which the SL mobility model is inspired by the simulation models used by the third generation partnership project (3GPP) for the placement and trajectory of drones, while the other three are well-known canonical models (or their variants) that offer a useful balance between realism and tractability. Assuming the nearest-neighbor association policy, we consider two service models for the UEs: (i) UE independent model (UIM), and (ii) UE dependent model (UDM). While the serving DBS follows the same mobility model as the other DBSs in the UIM, it is assumed to fly towards the UE of interest in the UDM and hover above its location after reaching there. The main contribution of this paper is a unified approach to characterize the point process of DBSs for all the mobility and service models. Using this, we provide exact mathematical expressions for the average received rate and the session rate as seen by the typical UE. Further, using tools from the calculus of variations, we concretely demonstrate that the simple SL mobility model provides a lower bound on the performance of other general mobility models (including the ones in which drones follow curved trajectories) as long as the movement of each drone in these models is independent and identically distributed (i.i.d.). To the best of our knowledge, this is the first work that provides a rigorous analysis of key canonical mobility models for an infinite drone cellular network and establishes useful connections between them.

Drone Searches in Challenging Conditions

The planning of drone trajectories in the search for ground-level objects is considered, in the case of challenging observation conditions. In existing planning algorithms, a priori data regarding the location of the objects are used in the secondary search, but no account is taken of their local visibility (on account of fog or smoke, say). To improve search productivity in challenging observation conditions, the observability may be taken into account in planning the drone trajectory. Heuristic models are used to assess the observability. The working trajectory is selected by calculating the maximum useful search information obtained from various possible trajectories. To eliminate Shannon indeterminacy in the utility of the information regarding successive points, we introduce an additional utility function. The results obtained by simulation of the search process confirm that this approach is more effective than trajectory planning on the basis of the maximum a priori probability that objects are present and on the basis of search entropy estimates.

Impact of drone route geometry on information collection in wireless sensor networks

The recent technological evolution of drones along with the constantly growing maturity of its commercialization, has led to the emergence of novel drone-based applications within the field of wireless sensor networks for information collection purposes. In such settings, especially when deployed in outdoor environments with limited external control, energy consumption and robustness are challenging problems for the system’s operation. In the present paper, a drone-assisted wireless sensor network is studied, the aim being to coordinate the routing of information (among the ground nodes and its propagation to the drone), investigating several drone trajectories or route shapes and examining their impact on information collection (the aim being to minimize transmissions and consequently, energy consumption). The main contribution lies on the proposed algorithms that coordinate the communication between (terrestrial) sensor nodes and the drone that may follow different route shapes. It is shown through simulations using soft random geometric graphs that the number of transmitted messages for each drone route shape depends on the rotational symmetry around the center of each shape. An interesting result is that the higher the order of symmetry, the lower the number of transmitted messages for data collection. Contrary, for those cases that the order of symmetry is the same, even for different route shapes, similar number of messages is transmitted. In addition to the simulation results, an experimental demonstration, using spatial data from grit bin locations, further validates the proposed solution under real-world conditions, demonstrating the applicability of the proposed approach.

An energy-aware drone trajectory planning scheme for terrestrial sensors localization

Employing GPS-equipped drones to act as mobile anchors is a popular solution for terrestrial sensors positioning in a generic environment. Researchers have proposed several approaches, usually to reduce estimated locations error and increase localization coverage, though no efficient solution has been presented for energy conservation of the drone. Drones, such as the commercial quadcopters, have limited power supply and cannot fly long. Any localization algorithm should consider the energy constraints beside the performance indicators. Furthermore, there is no suggested strategy to mitigate the error of Received Signal Strength (RSS)-based distance measurements in the existing solutions. In this paper, we propose a novel scheme to plan the drone trajectory, called the “Weighted Energy-aware Trajectory with Adaptive Radius (WETAR)”. The proposed scheme employs Linear Programming (LP) for trajectory planning in the presence of the sensors with given estimative regions which are acquired in a range-free pre-localization phase. It also specifies candidate waypoints, which are the projection of aerial anchor points on the ground, and assign weights to them based on two criteria: quality of the beacons that the sensors would receive and their coverage ratio. We assume that sensors utilize a range-based localization algorithm on the basis of RSS measurements. Simulation results show that the WETAR as an aerial anchor guiding mechanism, guides the drone effectively and reduces localization time, saves the drone energy, and improves the location error as well as the localization coverage.

An insight on UAV/drone autonomous navigation methods and applications: a review

In the past decade, unmanned aerial vehicles (UAVs) are gaining a lot amount of importance in the field of research and development. These UAVs have applications ranging from surveillance, the cinematography in movies, communication, transportation, agriculture, delivery of goods, and rescue operations in various scenarios. The navigation in drones is always a significant challenge as it depends on multiple aspects of technologies, which include both hardware and software. This survey paper lists the importance of UAVs and their various applications. The paper encompasses both the indoor and outdoor aspects of navigation. It describes various properties for navigating, like drone control architecture, deployed hardware-technologies, implemented algorithmic models, drone trajectory planning, and drone communications technologies requirements. Additionally, we also focus on the role of artificial intelligence paradigms for autonomous drone navigation. Vision-based navigation of UAVs using locality sensors and cameras has also discussed with the safety aspects of a drone's successful operation. This paper presents a review of the essential aspects of drone navigation methods and their applications. In the end, this survey reports the essential requirements of UAVs in civil applications from a navigation point of view.

Navion: A 2-mW Fully Integrated Real-Time Visual-Inertial Odometry Accelerator for Autonomous Navigation of Nano Drones

This paper presents Navion, an energy-efficient accelerator for visual-inertial odometry (VIO) that enables autonomous navigation of miniaturized robots (e.g., nano drones), and virtual reality (VR)/augmented reality (AR) on portable devices. The chip uses inertial measurements and mono/stereo images to estimate the drone's trajectory and a 3-D map of the environment. This estimate is obtained by running a state-of-theart VIO algorithm based on non-linear factor graph optimization, which requires large irregularly structured memories and heterogeneous computation flow. To reduce the energy consumption and footprint, the entire VIO system is fully integrated on-chip to eliminate costly off-chip processing and storage. This paper uses compression and exploits both structured and unstructured sparsity to reduce on-chip memory size by 4.1×. Parallelism is used under tight area constraints to increase throughput by 43%. The chip is fabricated in 65-nm CMOS and can process 752 × 480 stereo images from EuRoC data set in real time at 20 frames per second (fps) consuming only an average power of 2 mW. At its peak performance, Navion can process stereo images at up to 171 fps and inertial measurements at up to 52 kHz, while consuming an average of 24 mW. The chip is configurable to maximize accuracy, throughput, and energy-efficiency tradeoffs and to adapt to different environments. To the best of our knowledge, this is the first fully integrated VIO system in an application-specified integrated circuit (ASIC).

Opportunistic Mobility Utilization in Flying Ad-Hoc Networks: A Dynamic Matching Approach

The effective information interaction is important for drones to complete various tasks of flying ad hoc networks (FANETs). In this letter, we study the selection problem of transmission nodes and modes in dynamic FANETs. Besides classic direct transmission and relay transmission, drones can choose ferrying transmission opportunistically based on the given trajectory of other drones during the topology adjustment of swarms. To help multiple drones adjust the selection strategy with the topology changing, we design a dynamic matching game model, which takes both the current communication environment and resources that the future holds into account. Simulation results show that the proposed algorithm can effectively increase the system-wide throughput in the process of the adjustment.

Virtualized Control Over Fog: Interplay Between Reliability and Latency

This paper introduces an analytical framework to investigate optimal design choices for the placement of virtual controllers along the cloud-to-things continuum. The main application scenarios include low-latency cyber-physical systems in which real-time control actions are required in response to the changes in states of an Internet of Things (IoT) node. In such cases, deploying controller software on a cloud server is often not tolerable due to delay from the network edge to the cloud. Hence, it is desirable to trade reliability with latency by moving controller logic closer to the network edge. Modeling the IoT node as a dynamical system that evolves linearly in time with quadratic penalty for state deviations, recursive expressions for the optimum control policy and the resulting minimum cost value are obtained by taking virtual fog controller reliability and response time latency into account. Our results indicate that latency is more critical than reliability in provisioning virtualized control services over fog endpoints, as it determines the swiftness of the fog control system as well as the timeliness of state measurements. Based on a drone trajectory tracking model, an extensive simulation study is also performed to illustrate the influence of reliability and latency on the control of autonomous vehicles over fog.

Directing Cinematographic Drones

Quadrotor drones equipped with high-quality cameras have rapidly raised as novel, cheap, and stable devices for filmmakers. While professional drone pilots can create aesthetically pleasing videos in short time, the smooth—and cinematographic—control of a camera drone remains challenging for most users, despite recent tools that either automate part of the process or enable the manual design of waypoints to create drone trajectories. This article moves a step further by offering high-level control of cinematographic drones for the specific task of framing dynamic targets. We propose techniques to automatically and interactively plan quadrotor drone motions in dynamic three-dimensional (3D) environments while satisfying both cinematographic and physical quadrotor constraints. We first propose the Drone Toric Space , a dedicated camera parameter space with embedded constraints, and derive some intuitive on-screen viewpoint manipulators. Second, we propose a dedicated path planning technique that ensures both that cinematographic properties can be enforced along the path and that the path is physically feasible by a quadrotor drone. At last, we build on the Drone Toric Space and the specific path planning technique to coordinate the motion of multiple drones around dynamic targets. A number of results demonstrate the interactive and automated capacities of our approaches on different use-cases.

충돌회피를 위한 드론택배 시스템의 통신망 토폴로지 및 성능평가

최근 기업에서는 드론을 이용하여 다양한 상업적인 서비스를 시도하고 있다. 특히, 드론을 이용한 택배서비스가 그 좋은 예라고 할 수 있다. 그러나, 이러한 드론 택배시스템은 사람들이 활보하는 거리 위에서 무거운 물건들을 배송하는 일이기 때문에 서로 충돌로 인한 소포가 떨어지는 등 다양한 사안을 고려해야만 한다. 이러한 문제점을 해결하기 위해 본 논문에서는 드론간 통신을 활용하고자 하며 예상되는 드론의 통신망 토폴로지를 Opnet 시뮬레이터로 구현하고, 해당 통신망의 성능을 시뮬레이션하고 분석하였다. 추가적으로 자유 운동적(random mobility)인 이동경로의 토폴로지도 구현하여 제안한 드론망의 성능과 비교분석하였다. Recently, many companies try to offer various services using drones. Especially, the drone delivery system is a good example. However, the drone delivery service has some problems since the heavy parcels flies over the people walking down streets, so many things must be considered such as dropping mails by collision of drones. To resolve the problem, in this paper, a inter-drone network communication will be used to design the topology and to simulate in the Opnet simulator for the performance evaluation. Additionally, the topology with random mobility of trajectory of drones is also designed and simulated for the result.

Drone-Based Experimental Investigation of Three-Dimensional Flow Structure of a Multi-Megawatt Wind Turbine in Complex Terrain

The aerodynamic characteristics of wakes in complex terrain have a profound impact on the energy yield of wind farms and on the fatigue loads on wind turbines in the wind farm. In order to detail the spatial variations of the wind speed, wind direction, and turbulent kinetic energy (TKE) in the near-wake, comprehensive drone-based measurements at a multi-megawatt (MW) wind turbine that is located in complex terrain have been conducted. A short-time Fourier transform (STFT)-based analysis method is used to derive time-localized TKE along the drone's trajectory. In upstream and in the near-wake, the vertical profiles of wind speed, wind direction, and TKE are detailed. There is an increase in the TKE from upstream to downstream of the wind turbine, and whereas, the characteristic microscale length scales increase with increasing height above the ground upstream of the turbine, in the near-wake the microscale lengths are of constant, smaller magnitude. The first-ever measurements of the pressure field across a multi-MW wind turbines rotor plane and of the tip vortices in the near-wake are also reported. It is shown that the pitch between subsequent tip vortices, which are shed from the wind turbines blades, increases in the near-wake as the wake evolves. These details of the near-wake can have an important effect on the subsequent evolution of the wake and must be incorporated into the three-dimensional (3D) field wake models that are currently under intensive development.