Unmanned Aerial Vehicle Speed Research Articles

Flight Time Minimization of UAV for Data Collection Over Wireless Sensor Networks

In this paper, we consider a scenario where an unmanned aerial vehicle (UAV) collects data from a set of sensors on a straight line. The UAV can either cruise or hover while communicating with the sensors. The objective is to minimize the UAV's total flight time from a starting point to a destination while allowing each sensor to successfully upload a certain amount of data using a given amount of energy. The whole trajectory is divided into non-overlapping data collection intervals, in each of which one sensor is served by the UAV. The data collection intervals, the UAV's speed and the sensors' transmit powers are jointly optimized. The formulated flight time minimization problem is difficult to solve. We first show that when only one sensor is present, the sensor's transmit power follows a water-filling policy and the UAV's speed can be found efficiently by bisection search. Then, we show that for the general case with multiple sensors, the flight time minimization problem can be equivalently reformulated as a dynamic programming (DP) problem. The subproblem involved in each stage of the DP reduces to handle the case with only one sensor node. Numerical results present insightful behaviors of the UAV and the sensors. Specifically, it is observed that the UAV's optimal speed is proportional to the given energy of the sensors and the inter-sensor distance, but inversely proportional to the data upload requirement.

Computation Rate Maximization in UAV-Enabled Wireless-Powered Mobile-Edge Computing Systems

Mobile edge computing (MEC) and wireless power transfer (WPT) are two promising techniques to enhance the computation capability and to prolong the operational time of low-power wireless devices that are ubiquitous in Internet of Things. However, the computation performance and the harvested energy are significantly impacted by the severe propagation loss. In order to address this issue, an unmanned aerial vehicle (UAV)-enabled MEC wireless powered system is studied in this paper. The computation rate maximization problems in a UAV-enabled MEC wireless powered system are investigated under both partial and binary computation offloading modes, subject to the energy harvesting causal constraint and the UAV's speed constraint. These problems are non-convex and challenging to solve. A two-stage algorithm and a three-stage alternative algorithm are respectively proposed for solving the formulated problems. The closed-form expressions for the optimal central processing unit frequencies, user offloading time, and user transmit power are derived. The optimal selection scheme on whether users choose to locally compute or offload computation tasks is proposed for the binary computation offloading mode. Simulation results show that our proposed resource allocation schemes outperforms other benchmark schemes. The results also demonstrate that the proposed schemes converge fast and have low computational complexity.

Dual-UAV-Enabled Secure Communications: Joint Trajectory Design and User Scheduling

In this paper, we investigate a novel unmanned aerial vehicle (UAV)-enabled secure communication system. Two UAVs are applied in this system where one UAV moves around to communicate with multiple users on the ground using orthogonal time-division multiple access while the other UAV in the area jams the eavesdroppers on the ground to protect communications of the desired users. Specifically, we maximize the minimum worst-case secrecy rate among the users within each period by jointly adjusting UAV trajectories and user scheduling under the maximum UAV speed constraints, the UAV return constraints, the UAV collision avoidance constraints, and the discrete binary constraints on user scheduling variables. Since the resulting optimization problem is very difficult to solve due to its highly nonlinear objective function and nonconvex constraints, we first equivalently transform it into a more tractable problem. In particular, the binary constraints are equivalently converted to a number of equality constraints. Then, we develop a novel joint optimization algorithm to handle the converted problem. In order to further improve the secrecy rate performance, we also extend the developed algorithm to the case with multiple jamming UAVs. The simulation results show that the proposed joint optimization algorithm achieves significantly better performance than the conventional algorithms.

Capacity Characterization of UAV-Enabled Two-User Broadcast Channel

Unmanned aerial vehicles (UAVs) have recently gained growing popularity in wireless communications owing to their many advantages such as swift and cost-effective deployment, line-of-sight (LoS) aerial-to-ground link, and controllable mobility in three-dimensional (3D) space. Although prior works have exploited the UAV’s mobility to enhance the wireless communication performance under different setups, the fundamental capacity limits of UAV-enabled/aided multiuser communication systems have not yet been characterized. To fill this gap, we consider, in this paper, a UAV-enabled two-user broadcast channel (BC), where a UAV flying at a constant altitude is deployed to send independent information to two users at different fixed locations on the ground. We aim to characterize the capacity region of this new type of BC over a given UAV flight duration, by jointly optimizing the UAV’s trajectory and transmit power/rate allocations over time, subject to the UAV’s maximum speed and maximum transmit power constraints. First, to draw essential insights, we consider two special cases with asymptotically large/low UAV flight duration/speed, respectively. For the former case, it is shown that a simple hover-fly-hover (HFH) UAV trajectory with time division multiple access (TDMA)-based orthogonal multiuser transmission is capacity-achieving; while in the latter case, the UAV should hover at a fixed location that is nearer to the user with larger achievable rate and in general superposition coding (SC)-based non-orthogonal transmission with interference cancellation at the receiver of the nearer user is required. Next, we consider the general case with finite UAV speed and flight duration. We show that the optimal UAV trajectory should follow a general HFH structure, i.e., the UAV successively hovers at a pair of optimal initial and final locations above the line segment connecting the two users each with a certain amount of time and flies unidirectionally between them at the maximum speed, and SC is generally needed. Furthermore, when TDMA-based transmission is considered for low-complexity implementation, we show that the optimal UAV trajectory still follows an HFH structure, but the hovering locations can only be those above the two users. Extensive simulation results are provided to verify our analysis, which also reveal useful guidelines to the practical design of UAV trajectory and communication jointly.

Trajectory Design for Completion Time Minimization in UAV-Enabled Multicasting

This paper studies an unmanned aerial vehicle (UAV)-enabled multicasting system, where a UAV is dispatched to disseminate a common file to a set of ground terminals (GTs). We aim to design the UAV trajectory to minimize its mission completion time, while ensuring that each GT successfully recovers the file with a desired high probability. The formulated problem is nonconvex and difficult to be solved in its original form. Therefore, we first derive an effective lower bound for the success file recovery probability of each GT. The problem is then reformulated in a more tractable form, where the UAV trajectory only needs to be designed to ensure the minimum connection time constraint with each GT, during which their distance is below a certain threshold. We show that without loss of optimality, the UAV trajectory consists of connected line segments only, which can be obtained by determining the optimal set of waypoints as well as the UAV speed along the path connecting the waypoints. We propose efficient schemes for the waypoint design based on a novel concept of virtual base station placement and by applying convex optimization. Furthermore, for fixed waypoints, the optimal UAV speed is efficiently obtained by solving a linear programming problem. Numerical results show that the proposed UAV-enabled multicasting with optimized trajectory design achieves significant performance gains over other benchmark schemes.

Constrained coverage path planning: evolutionary and classical approaches

SUMMARYThe constrained coverage path planning addressed in this paper refers to finding an optimal path traversed by a unmanned aerial vehicle (UAV) to maximize its coverage on a designated area, considering the time limit and the feasibility of the path. The UAV starts from its current position to assess the condition of a new entry to the area. Nevertheless, the UAV needs to comply with the coverage task, simultaneously and therefore, it is likely that the optimal policy would not be the shortest path in such a condition, since a wider area can be covered through a longer path. From the other side, along with a longer path, the UAV may not reach to the target in due time. In addition, the speed of UAV is assumed to be constant and as a result, a feasible path needs to be smooth enough to support this assumption. The problem is modeled as an Epsilon-constraint optimization in which a coverage function has to be maximized, considering the constraints on the length and the smoothness of the path. For this purpose, a new genetic path planning algorithm with adaptive operator selection is proposed to solve such a complicated constrained optimization problem. The proposed approach has been compared to some classical approaches like, a modified version of the Artificial Potential Field and a modified version of Dijkstra's algorithm (a graph-based approach). All the methods are implemented and tested in different scenarios and their performances are evaluated via the simulation results.

Location-Aided Delay Tolerant Routing Protocol in UAV Networks for Post-Disaster Operation

Wireless communication is essential for search and rescue operations in the aftermath of natural disasters. In post-disaster scenarios, unmanned aerial vehicles (UAVs) can be used to capture image and video data from the disaster area and transfer the data to a ground station, owing to their rapid mobility. However, packet forwarding is a challenge because of unstable links and intermittent connectivity in highly dynamic UAV networks. In this paper, we propose a location-aided delay tolerant routing (LADTR) protocol for UAV networks for use in post-disaster operations, which exploits location-aided forwarding combined with a store–carry–forward (SCF) technique. Ferrying UAVs are introduced to enable an efficient SCF, and this is the first attempt at introducing and using ferrying UAVs for routing in UAV networks, to the best of our knowledge. Ferrying UAVs improve the availability of connection paths between searching UAVs and the ground station, thus reducing end-to-end delays and increasing the packet delivery ratio. Future UAV locations are estimated based on the location and speed of UAVs equipped with a global positioning system. The forwarding UAV node predicts the position of the destination UAV node and then decides where to forward. The proposed LADTR ensures that the contact rate between UAV nodes remains high, which enables a high packet delivery ratio, and ensures single-copy data forwarding to avoid replication of each message. Our performance study shows that the proposed LADTR outperforms the four typical routing protocols reported in the literature in terms of packet delivery ratio, average delay, and routing overhead.

Measurement of Pesticide Drift from Unmanned Aerial Vehicle Application to a Vineyard

Abstract. Unmanned aerial vehicles (UAVs) are now being used to perform commercial pesticide applications in California, but little information is available regarding the amount of pesticide drift resulting from these applications. The physical dimensions and operating speed of UAVs differ substantially from those of manned aircraft and fall outside the validated range of spray dispersion models. This study measured spray drift from a 0.84 ha aerial pesticide application of imidacloprid performed with a Yamaha R-Max II UAV over a Napa Valley vineyard. Downwind deposition samples, in-swath deposition samples, and downwind air samples were collected up to 48 m downwind of the application field. In-swath deposition samples measured approximately 57% of the target rate, while downwind drift deposition decreased from approximately 0.4% at 7.5 m downwind to 0.03% at 48 m downwind. All air samples were below the method detection limit. A drift deposition curve fitted to measured ground deposition using a log-log second-degree polynomial function yielded an R2 value of 0.985. An estimated 0.28% to 0.54% of applied material was lost as drift out to 50 m downwind of the field edge based on ground deposition measurements, 82% of which deposited within the first 7.5 m downwind. Uncertainty in mass accountancy and deposition measurements is discussed, with sources of error including obstructions in the downwind measurement area, low collection efficiency of the sampling media, a high coefficient of variation of spray deposition in the treatment field, and possible photodegradation of the tracer material. Keywords: Aerial application, AGDISP, Pesticide deposition, Pesticide drift, Remotely piloted aircraft, UAV, Unmanned aerial vehicle, Vineyard.

Channel estimation for high speed unmanned aerial vehicle with STBC in MIMO radio links

This paper proposes a channel estimation method based on Kalman filter and adaptive estimation with space-time block code (STBC) and multiple antenna systems [multiple input single output (MISO) and multiple input multiple output (MIMO)\ for high speed UAVs. Simulations have been done in time-varying Rayleigh faded channels for BPSK and QPSK. The proposed technique shows a very good error for severely faded channels. Application of Alamouti STBCs with diversity based on multiple antennas provides improved performance in faded wireless channels. Alamouti transmit diversity scheme, however, relies on the availability of accurate channel state information (CSI) for unmanned aerial vehicles (UAVs). Simulation results show that our proposed method achieves accurate estimation for the SNR and Doppler shift in a wide range of velocities and SNRs.

Channel estimation for high speed unmanned aerial vehicle with STBC in MIMO radio links

This paper proposes a channel estimation method based on Kalman filter and adaptive estimation with space-time block code (STBC) and multiple antenna systems [multiple input single output (MISO) and multiple input multiple output (MIMO) for high speed UAVs. Simulations have been done in time-varying Rayleigh faded channels for BPSK and QPSK. The proposed technique shows a very good error for severely faded channels. Application of Alamouti STBCs with diversity based on multiple antennas provides improved performance in faded wireless channels. Alamouti transmit diversity scheme, however, relies on the availability of accurate channel state information (CSI) for unmanned aerial vehicles (UAVs). Simulation results show that our proposed method achieves accurate estimation for the SNR and Doppler shift in a wide range of velocities and SNRs.

Ground Target Tracking Guidance Law for Fixed-Wing Unmanned Aerial Vehicle: A Search and Capture Approach

One important problem for unmanned aerial vehicles (UAVs) in mission applications is to track ground targets automatically. A major concern is how to keep the tracking process stable and efficient while the motion of the ground targets changes rapidly. In this brief, a new guidance strategy for the ground target “Search and Capture” based on a virtual target is proposed. First, a virtual trajectory, which is composed of straight lines and arcs, is generated based on the motion of the target. The straight lines are used to capture, while the arcs are used to search, and switch between straight line and arc when some condition is met; second, we design a new guidance law based on line-of-sight (LOS) which makes a UAV to track the virtual target automatically. This new method solves the following three problems simultaneously: (1) The UAV always keeps a constant speed to track the target with changing velocity, (2) the generated trajectory meets the flight constraints of the UAV, and (3) the speed range of the ground target can be from the stationary to almost the maximum cruising speed of the UAV. Simulation results show that the proposed guidance strategy can achieve stable tracking for various motions of the ground target.

Energy-Efficient UAV Communication With Trajectory Optimization

Wireless communication with unmanned aerial vehicles (UAVs) is a promising technology for future communication systems. In this paper, we study energy-efficient UAV communication with a ground terminal via optimizing the UAV's trajectory, a new design paradigm that jointly considers both the communication throughput and the UAV's energy consumption. To this end, we first derive a theoretical model on the propulsion energy consumption of fixed-wing UAVs as a function of the UAV's flying speed, direction and acceleration, based on which the energy efficiency of UAV communication is defined. Then, for the case of unconstrained trajectory optimization, we show that both the rate-maximization and energy-minimization designs lead to vanishing energy efficiency and thus are energy-inefficient in general. Next, we introduce a practical circular UAV trajectory, under which the UAV's flight radius and speed are optimized to maximize the energy efficiency for communication. Furthermore, an efficient design is proposed for maximizing the UAV's energy efficiency with general constraints on its trajectory, including its initial/final locations and velocities, as well as maximum speed and acceleration. Numerical results show that the proposed designs achieve significantly higher energy efficiency for UAV communication as compared with other benchmark schemes.

Channel estimation, equalisation, and evaluation for high‐mobility airborne hyperspectral data transmission

In the past few years, unmanned aerial vehicles (UAVs) have become a primary airborne platform for hyperspectral imager for studies on precision agriculture, defence, and the environment. The ‘push-broom’ type of hyperspectral sensors require moving vehicle, and transmission and analysis of hyperspectral data by means of a UAV's high-mobility channel is challenging. While high bandwidth of hyperspectral imaging justify using orthogonal frequency division multiplexing (OFDM) for data transmission, the high speed of UAVs imposes intercarrier interference (ICI) on the transmitted OFDM signal because of the Doppler shift. This study proposes a technique for channel estimation and equalisation in order to compensate the ICI. This technique uses a complete channel matrix estimation in the frequency domain in contrast to conventional methods that only use diagonal elements when recovering the data. In order to evaluate the received data using this technique, a classification framework was designed that took into consideration both spectral and spatial information. In order to verify the robustness of the proposed model, the system was analysed using a Pavia Center hyperspectral dataset, and evaluated against speeds of 50 and 500 m/s. By using this method, improvement in both data transmission and the analysis was achieved.

EGL: Efficient Geometry-based Localization in Wireless Sensor Networks

Localisation of sensor nodes deployed randomly is one of the most challenging issues in wireless sensor networks. Position information of nodes helps in effective coverage, routing, data collection, target tracking, and event detection. This paper describes a localisation scheme where an unmanned aerial vehicle (UAV) equipped with a global positioning system (GPS) unit scans the sensing field while periodically broadcasting its current position. The sensor nodes estimate three boundary points on its communication circle and then, using simple geometry rules, estimate their location. The scheme neither requires extra hardware nor any interaction for ordinary sensor nodes with their neighbours. Several enhancements, including detection and correction of boundary points errors, are explained. This scheme is accurate and efficient enough to tolerate obstacles in the sensing field. Simulation results show that localisation accuracy can be adjusted by properly selecting UAV speed and beacon interval along with the UAV scan pattern.

Cooperative Tracking a Moving Target Using Multiple Fixed-wing UAVs

A cooperative tracking scheme is presented in this paper for multiple fixed-wing unmanned aerial vehicles (UAVs) to track an uncooperative, moving target. It is comprised of a target loitering algorithm and a formation flight algorithm. The loitering algorithm enables a constant speed UAV to circle around a moving target, whose speed is allowed to vary up to the UAV's speed. The formation algorithm enables cooperative tracking using multiple UAVs by keeping them flying in a circular formation with equal inter-vehicle angular separation. Under this formation algorithm, the formation center can be controlled independently to perform target loitering, and the admissible range of the target's speed would not be affected for given UAVs. The performance of the proposed tracking system is verified in numerical simulations.

Brain Computer Interface system based on indoor semi-autonomous navigation and motor imagery for Unmanned Aerial Vehicle control

This paper proposes a non-invasive Electroencephalogram (EEG)-based Brain Computer Interface (BCI) system to achieve the easy-to-use and stable control of a low speed Unmanned Aerial Vehicle (UAV) for indoor target searching. The BCI system for UAV control consists of two main subsystems responsible for decision and semi-autonomous navigation. The decision subsystem is established based on the analysis of motor imagery (MI) EEG. The improved cross-correlation method (CC) is used to accomplish the MI feature extraction and the logistic regression method (LR) is employed to complete the MI feature classification and decision. The average classification accuracy rate of the BCI system reaches to 94.36%. The semi-autonomous navigation subsystem is utilized to avoid obstacles automatically for UAV and provide feasible directions for decision subsystem. The actual indoor target searching experiment is carried out to verify the performance of this BCI system. The experiment validates the feasibility and effectiveness of this BCI system for low speed UAV control by using MI and semi-autonomous navigation.

A generic approach for photogrammetric survey using a six-rotor unmanned aerial vehicle

This paper discusses a rapid production of slope mapping using multi-rotor unmanned aerial vehicle (UAV). The objective of this study is to determine the accuracy of the photogrammetric results based on novel method of multi-rotor UAV images as well as to analyze the slope error distribution that are obtained from the UAV images. This study only concentrates on multi-rotor UAV which also known as Hexacopter. An operator can control the speed of multi-rotor UAV during flight mission. Several ground control points and checkpoints were established using Real Time Kinematic Global Positioning System (RTK- GPS) at the slope area. Ground control points were used in exterior orientation during image processing in sequence to transform image coordinates into local coordinate system. Checkpoints were established at the slope area for accuracy assessment. A digital camera, Sony NEX-5N was used for image acquisition of slope area from UAV platforms. The digital camera was mounted vertically at the bottom of UAV and captured the images at an altitude. All acquired images went through photogrammetric processing including interior orientation, exterior orientation and bundle adjustment using photogrammetric software. Photogrammetric results such as digital elevation model, and digital orthophoto including slope map were assessed. UAV is able to acquire data within short period of time with low budget compared to the previous methods such as satellite images and airborne laser scanner. Analysis on slope analysis and error distribution analysis are discussed in this paper to determine the quality of slope map in the area of interest. In summary, multi-rotor UAV is suited in slope mapping studies.

Deconfliction Algorithms for a Pair of Constant Speed Unmanned Aerial Vehicles

This paper provides an approach for the deconfliction problem for a pair of constant altitude, constant speed unmanned aerial vehicles (UAVs) modeled as unicycles in the presence of static constraints. We use a combination of navigation and swirling functions to direct the unicycle vehicles along the planned trajectories while avoiding intervehicle collisions. The main feature of our contribution is proposing means of designing a deconfliction algorithm for unicycle vehicles that more closely capture the dynamics of constant speed UAVs as opposed to double integrator models. Specifically, we consider the issue of UAV turn-rate constraints and proceed to explore the selection of key algorithmic parameters in order to minimize undesirable trajectories and overshoots induced by the avoidance algorithm. The avoidance and convergence analysis of the proposed algorithm is then performed for two cooperative UAVs and simulation results are provided to support the viability of the proposed framework for more general mission scenarios.

A comparison of image inspection modes for a visual search and rescue task

Visual inspection and search are important tasks in many fields, including quality control, security surveillance and medical diagnosis. We investigate whether it is better to visually inspect a moving image as opposed to a series of equivalent static images using the challenging problem of locating individuals lost in a wilderness. Wilderness search and rescue may be approached with a systematic aerial search assisted by an unmanned aerial vehicle (UAV) whose camera relays the terrain below for human inspection. We investigated two presentation modes of simulated UAV video feeds. The first mimics the live video from the downward facing camera. In the second, ‘serial visual presentation’ (SVP) mode, a static image remains in view until replaced by a new image at a rate equivalent to the live video mode. We established a statistically significant improvement in the number of detected targets in the SVP mode when compared to the Moving mode. However, these improvements were accompanied by an increase in the number of incorrectly identified targets in SVP mode. UAV speed has a significant effect on target identification in both modes, presumably due to the extra time available for viewing at lower speeds. We found no significant interaction between speed and presentation mode.