Unmanned Aerial Vehicle Flight Path Research Articles

Energy Efficient UAV Flight Path Model for Cluster Head Selection in Next-Generation Wireless Sensor Networks.

Wireless sensor networks (WSNs) are one of the fundamental infrastructures for Internet of Things (IoTs) technology. Efficient energy consumption is one of the greatest challenges in WSNs because of its resource-constrained sensor nodes (SNs). Clustering techniques can significantly help resolve this issue and extend the network’s lifespan. In clustering, WSN is divided into various clusters, and a cluster head (CH) is selected in each cluster. The selection of appropriate CHs highly influences the clustering technique, and poor cluster structures lead toward the early death of WSNs. In this paper, we propose an energy-efficient clustering and cluster head selection technique for next-generation wireless sensor networks (NG-WSNs). The proposed clustering approach is based on the midpoint technique, considering residual energy and distance among nodes. It distributes the sensors uniformly creating balanced clusters, and uses multihop communication for distant CHs to the base station (BS). We consider a four-layer hierarchical network composed of SNs, CHs, unmanned aerial vehicle (UAV), and BS. The UAV brings the advantage of flexibility and mobility; it shortens the communication range of sensors, which leads to an extended lifetime. Finally, a simulated annealing algorithm is applied for the optimal trajectory of the UAV according to the ground sensor network. The experimental results show that the proposed approach outperforms with respect to energy efficiency and network lifetime when compared with state-of-the-art techniques from recent literature.

Fully automated region of interest segmentation pipeline for UAV based RGB images

Unmanned Aerial Vehicles (UAVs) have exhibited its potential for efficient and non-invasive crop data acquisition in high throughput crop phenotyping. In general, for analysis of phenotypic traits, there is a need for extracting the region of interest (RoI) from images captured by UAVs. It involves the generation of orthomosaic, which is a complicated and time-intensive process. In this study, a fully automated AI-based pipeline has been proposed for the RoI segmentation from raw RGB images acquired via UAV. The proposed pipeline achieves a near real-time processing speed compared to the other baseline methods. The key feature of the pipeline is the introduction of Sub-Paths , in which the original UAV flight path is divided into several small paths which facilitates parallel processing. The image quality of the extracted RoI has been examined using blind/referenceless image spatial quality evaluator (BRISQUE) and natural image quality evaluator (NIQE). The performance of the proposed pipeline is exemplified with the Leaf Area Index (LAI) estimation on five datasets containing three different crop types and growth stages. Regression analysis has also been performed on the estimated LAI values. Average R 2 , RMSE, and correlation scores of the estimates are observed to be 0.68, 0.033, and 0.83, respectively. • Fully automated end-to-end AI-based pipeline avoids using orthomosaic. • Processing time for fixed field and fixed overlap is same, significantly faster than using orthomosaic. • Image quality of the extracted ROI has been analysed using BRISQUE and NIQE indices. • A plant trait has been estimated autonomously as a use case of the proposed framework.

Multi-carrier based UAV data relaying and user tracking system

In the case of natural disaster or emergency rescue, unmanned aerial vehicle (UAV) can be dispatched to the target region and leveraged as aerial relay to track and serve users owning to flexible maneuverability and fast deployment. In this article, a multi-carrier based UAV data relaying and user tracking system is proposed in which the UAV adopts multi-carrier transmission and builds a communication connection from the reliable infrastructure to the target region in emergency. More specifically, a base station (BS) sends information to the UAV which can allocate sub-carriers to users, and then the UAV forwards information, tracks and serves users to ameliorate system performance. A downlink average achievable sum rate maximization-based joint optimization problem, including the UAV flight path, sub-carrier transmit power and user scheduling, is formulated with the information causality constraint. Since the formulated problem suffers from non-convexity, a three-step iterative algorithm is derived to approximate the optimal solution to the original problem by alternatively optimizing UAV flight path, sub-carrier transmit power and user scheduling. Simulation results show that the proposed algorithm can not only track mobile users dynamically, but also demonstrate the performance superiorities of the proposed scheme prominently.

Hybrid FWPS cooperation algorithm based unmanned aerial vehicle constrained path planning

This paper considers the unmanned aerial vehicle (UAV) global path planning as an optimization problem with multiple constraints and proposes an improved fireworks algorithm (FWA) and particle swarm optimization (PSO) cooperation algorithm to generate an optimal path. The objective function of the UAV flight path is modeled to have the shortest length satisfying strict multiple threat area constraints. The α constrained method using the level comparison strategy is integrated into both FWA and PSO to enhance their superior constraint-handling ability. To increase the population diversity, the whole population is divided to fireworks and particles, which perform search operation in parallel. A new mutation strategy in the fireworks is adopted to avoid falling into the local optimum. Information sharing mechanism between fireworks and particles is established to make the population achieve the excellent global optimization performance. Several numerical simulations are carried out and the results show that our proposed algorithm performs well in obtaining high quality solutions and handling constraints.

A low-cost physical location discovery scheme for large-scale Internet of Things in smart city through joint use of vehicles and UAVs

With the development of Information and Communication Technology (ICT), the construction of the smart city came into being. Compared with the traditional city, a smart city can reduce resource consumption, improve energy efficiency, reduce environmental pollution, reduce traffic congestion, reduce potential safety hazards, improve the quality of life of citizens, etc. In order to collect a large amount of data to provide accurate decision-making recommendations for the management of smart cities, a large-scale Internet of Things (IoT) system needs to be built as the basis. For most applications in smart cities, it is very important to obtain the physical location information of the data during the data collection. However, it is a challenging issue for most sensor devices in the IoT system, because sensor devices are hard to equip positioning equipment as limited by cost. To tackle this, a Low-Cost Physical Locations Discovery (LCPLD) Scheme is proposed in this paper. In LCPLD scheme, mobile vehicles and unmanned aerial vehicles (UAVs) are used for physical location discovery on the wireless sensor networks which are the important component of the IoT system in a smart city. In order to further reduce cost, we propose a task application mechanism to reduce the cost of vehicle broadcasting and the Adaptive UAV Flight Path Planning (AUPPP) algorithm to reduce UAV flight cost. In order to reduce localization error, the Large Error Rejection (LER) algorithm and the UAV Same Position Broadcast Repeat (USPBR) algorithm are proposed in this paper. After simulation experiments based on real vehicle driving data, the experimental results prove the effectiveness of the proposed scheme: Compared with the comparison scheme, the LCPLD scheme proposed has a cost reduction of 16.58% ∼19.88%, an average reduction of 78.80% in positioning error, and an average reduction of 99.88% in the variance of positioning error.

A SLAM Map Restoration Algorithm Based on Submaps and an Undirected Connected Graph

Many visual simultaneous localization and mapping (SLAM) systems have been shown to be accurate and robust, and have real-time performance capabilities on both indoor and ground datasets. However, these methods can be problematic when dealing with aerial frames captured by a camera mounted on an unmanned aerial vehicle (UAV) because the flight height of the UAV can be difficult to control and is easily affected by the environment. For example, the UAV may be shaken or experience a rapid drop in height due to sudden strong wind, which may in turn lead to lost tracking. What is more, when photographing a large area, the UAV flight path is usually planned in advance and the UAV does not generally return to the previously covered areas, so if the tracking fails during the flight, many areas of the map will be missing. To cope with the case of lost tracking, we present a method of reconstructing a complete global map of UAV datasets by sequentially merging the submaps via the corresponding undirected connected graph. Specifically, submaps are repeatedly generated, from the initialization process to the place where the tracking is lost, and a corresponding undirected connected graph is built by considering these submaps as nodes and the common map points within two submaps as edges. The common map points are then determined by the bag-of-words (BoW) method, and the submaps are merged if they are found to be connected with the online map in the undirect connected graph. To demonstrate the performance of the proposed method, we first investigated the performance on a UAV dataset, and the experimental results showed that, in the case of several tracking failures, the integrity of the mapping was significantly better than that of the current mainstream SLAM method. We also tested the proposed method on both ground and indoor datasets, where it again showed a superior performance.

Meteorology-Aware Path Planning for the UAV Based on the Improved Intelligent Water Drops Algorithm

A wide range of applications of the unmanned aerial vehicle (UAV) have been observed in the past few years, and path planning is one of the most critical issues that require to be resolved. UAVs are still prone to meteorological impediments such as thunderstorms, ice accumulation, and severe convective weather for the safety of flights. This paper proposes a meteorology-aware path planning method based on the improved intelligent water drops (IIWD) algorithm. The algorithm consists of both static and dynamic path planning. In the static path planning phase, ice accumulation and the Richardson number are considered when determining the trajectory with low risk. In the dynamic path planning phase, the latest forecast products are adopted to modify the planned path, and the virtual potential force is applied to adjust the flight direction of the UAV when encountered with severe convective weather. The validation and efficiency of the proposed algorithm are verified via simulations in comparison with the ant colony optimization, genetic algorithm, and Q-learning algorithm, where the quality of our algorithm's performance in terms of flight time and risk degree is determined. Meanwhile, the risk degree of the UAV flight path at different altitudes is analyzed. The simulation results show that the average flight speed decreases, and the risk degree increases along with the descending of the flight altitude, respectively, which is found to be in consensus with the theory of meteorology.

Prediction accuracy of reservoir break flood simulation model using finite volume method and UAV

Methods for producing high-resolution digital topographic maps using an unmanned aerial vehicle (UAV), and 3D fluid dynamics simulation to estimate the flooded areas caused by a collapsed reservoir, were proposed in this paper. The UAV flight path for photographing damaged areas was divided into two sections considering the drone flight time and overlapping range of the images in the x- and y-directions. The metadata taken by the drone were transferred into world coordinates by tracking the key features of the photographs of nearby areas using a 3D rotation matrix. The point cloud data with a 3D space were extracted from the registered images, and a digital surface map (DSM) was produced using a point cloud classification geometric mapping technique. To amend the serious elevation errors caused by natural or artificial obstacles, a kriging interpolation method was used to reproduce the DSM. A transient computational simulation that considers both the complex geometric topology and hydrodynamic energy of flowing water was conducted using FLOW-3D software to deal with an renormalization group (RNG) turbulence model. The flooded areas calculated through visual reading using images taken by the UAV were compared with the 3D simulation results for verification. The flooded areas estimated through the simulation were approximately 18.3% larger than those found by visual reading. Turbulent flows were mainly observed in obstacles or curved areas of the stream, and the differences in the water depth could be further increased. However, the villagers confirmed that the flooded areas were much greater than what was seen through the visual reading. Therefore, the combination of UAV surveying and the 3D simulation method based on the RNG turbulence model is recommended to accurately estimate flooded areas, and it will support an administrative policy aimed at minimizing the economic costs of damage caused by future reservoir collapses. Keywords: reservoir failure, flooded area, finite volume method, unmanned aerial vehicle, photogrammetric survey, digital surface model DOI: 10.25165/j.ijabe.20201306.4909 Citation: Jeon J, Choi W. Prediction accuracy of reservoir break flood simulation model using finite volume method and UAV. Int J Agric & Biol Eng, 2020; 13(6): 7–15.

Development of control system for quadrotor unmanned aerial vehicle using LoRa wireless and GPS tracking

In the past decades, there has been a growing interest in unmanned aerial vehicles (UAVs) for educational, research, business, and military purposes. The most critical data for a flight system is the telemetry data from the GPS and wireless transmitter and also from the gyroscope and accelerometer. The objective of this paper is to develop a control system for UAV using long-range wireless communication and GPS. First, Matlab simulation was conducted to obtain an optimum PID gains controller. Then LoRa wireless was evaluated during clear and rainy days. Static and dynamic points measurement was conducted to validate and optimize GPS accuracy. GeoMapping in Matlab and Google GPS GeoPlanner were then used to analyze the traveled UAV flight path.

Autonomous Tracking Using a Swarm of UAVs: A Constrained Multi-Agent Reinforcement Learning Approach

In this paper, we aim to design an autonomous tracking system for a swarm of unmanned aerial vehicles (UAVs) to localize a radio frequency (RF) mobile target. In the system, UAVs equipped with omnidirectional received signal strength (RSS) sensors can cooperatively search the target with a specified tracking accuracy. To achieve fast localization and tracking in the highly dynamic channel environment (e.g., time-varying transmit power and intermittent signal), we formulate a flight decision problem as a constrained Markov decision process (CMDP) with the main objective of avoiding redundant UAV flight path. Then, we propose an enhanced multi-agent reinforcement learning to coordinate multiple UAVs performing real-time target tracking. The core of the proposed scheme is a feedback control system that takes into account the uncertainty of the channel estimate. We prove that the proposed algorithm can converge to the optimal decision. Our simulation results show that the proposed scheme outperforms standard Q-learning and multi-agent Q-learning algorithms in terms of searching time and successful localization probability.

Efficient Multitask Scheduling for Completion Time Minimization in UAV-Assisted Mobile Edge Computing

Mobile edge computing (MEC) can alleviate the computing resource shortage problem of mobile user equipment (UEs). However, due to long communication distance or the obstruction of big obstacles, the direct communication link may not exist between a UE and a MEC node. It thus hinders the task offloading in MEC. Unmanned aerial vehicles (UAVs) have high degree of mobility and can carry lightweight computation and storage modules. This paper presents a UAV-assisted MEC method, in which the UAV can relay the task-input data of a UE to the MEC node and can also utilize the airborne computation and storage resource to shorten the execution time of the offloaded tasks. Considering the strict order dependency among multiple offloaded tasks, this paper optimizes the task scheduling and the UAV flight path in a joint manner. A heuristic algorithm based on particle swarm optimization (PSO) is also developed to find the optimal solution. The simulation results show that the proposed multitask scheduling method can always find the best tradeoff between the UAV’s position and the wireless channel condition. In comparison to the other three baseline scheduling methods, the proposed method can use the minimum execution time to complete all the offloaded tasks.

Emerging approaches to support dynamic mission planning: survey and recommendations for future research

In the Department of Defense, unmanned aerial vehicle (UAV) mission planning is typically in the form of a set of pre-defined waypoints and tasks, and results in optimized plans being implemented prior to the beginning of the mission. These include the order of waypoints, assignment of tasks, and assignment of trajectories. One emerging area that has been recently identified in the literature involves frameworks, simulations, and supporting algorithms for dynamic mission planning, which entail re-planning mid-mission based on new information. These frameworks require algorithmic support for flight path and flight time approximations, which can be computationally complex in nature. This article seeks to identify the leading academic algorithms that could support dynamic mission planning and recommendations for future research for how they could be adopted and used in current applications. A survey of emerging UAV mission planning algorithms and academic UAV flight path algorithms is presented, beginning with a taxonomy of the problem space. Next, areas of future research related to current applications are presented.

Internet of Things Data Collection Using Unmanned Aerial Vehicles in Infrastructure Free Environments

With the immensity of distributed Internet of Things (IoT) devices and the exponential increase in data generated from a variety of IoT-driven smart-world applications, how to effectively provide data driven service supported by IoT has become a critical issue. While the state-of-the-art technologies have been developed and network infrastructures with high capabilities have been designed to deal with the data collection problem, there are still application scenarios, in which network infrastructure is not available or appropriate in large target areas (e.g., farmlands deployed with IoT sensors in operation, providing precise agriculture; emergency responder with IoT sensors, providing public safety service). To address the issue of efficiently collecting data from IoT devices deployed in large areas without pre-deployed network infrastructure, we formalize the problem space in a three-dimensional model that considers task, resource, and methodology. Based on the designed problem space, we propose a novel solution that deploys an unmanned aerial vehicle (UAV), as a critical next generation mobile network, to achieve intermittent IoT device connections and enable data collection based on delay tolerant network (DTN) protocol. The UAV flight path is determined using a Hilbert Curve-based path planning algorithm. Through a series of quantitative experiments, we validate the effectiveness of our approach in a network emulation environment, and confirm its advantages in comparison with several baseline approaches. The results of our research shows the capability of quality and cost control in IoT applications such as smart agriculture, public safety disaster recovery and rescue.

UAV-Aided Jamming for Secure Ground Communication With Unknown Eavesdropper Location

This paper investigates unmanned aerial vehicle (UAV)-aided jamming technique for enabling physical layer keyless security in scenarios where the exact eavesdropper location is unknown. We assume that the unknown eavesdropper location is within an ellipse characterizing the coverage region of the transmitter. By sequentially optimizing the transmit power, the flight path of the UAV and its jamming power, we aim at maximizing the average secrecy rate with arbitrary eavesdropper location. Simulation results demonstrate that the optimal flight path obtains better secrecy rate performance compared to that using direct UAV flight path encasing the transmitter and the legitimate receiver. Most importantly, even with the unknown eavesdropper location, we obtained a secrecy rate that is comparable to a scenario when the eavesdropper's location is known. However, the average secrecy rate with the unknown eavesdropper location varies depending on the proximity of the eavesdropper to the known location of the transmitter. We also observe that due to the UAV-aided jamming, the average secrecy rate stabilizes at some point even though the average received envelope power of the eavesdropper increases. This essentially demonstrates the effectiveness of the proposed scheme.

Secrecy improvement via joint optimization of UAV relay flight path and transmit power

Eavesdropping occurs when an unauthorized receiver secretly intercepts a transmitted signal. In this paper, we investigate the optimum flight path and transmit power for an unmanned aerial vehicle (UAV) relay to improve secrecy performance in the presence of an eavesdropper. We propose an algorithm based on particle swarm optimization (PSO) where the UAV flight path and power are jointly optimized to maximize the secrecy capacity. Simulation results show that the proposed algorithm is capable of achieving a performance that is close to the ideal secrecy performance. Simulation results also highlight the benefits of power optimization.

UAVs joint vehicles as data mules for fast codes dissemination for edge networking in Smart City

With the rapid development of software-defined technologies, emerging multimedia applications are booming, which require real-time communication and computation via devices. Meanwhile, the program codes in multimedia applications need to be updated periodically to accommodate changes in the edge networking environment. Therefore, a huge number of smart sensing devices which are deployed in the infrastructures of smart city can play a bigger role than ever before due to their program codes in multimedia applications can be updated by sensing data. However, how to spread program codes to a large amount of devices which are distributed in smart city in a low-cost and fast way is a challenging issue. To solve the issue, in this paper, an Unmanned Aerial Vehicles (UAVs) joint Vehicles as Data Mules for Fast Codes Dissemination (UVDCD) scheme is proposed to spread codes as a fast and low-cost pattern for edge networking in smart city. In UVDCD scheme, first of all, large amount of vehicles in smart city act as mules for code dissemination. Although the cost of the method is low and can quickly cover most networks, there is a very long trailing phenomenon for this approach, i.e. in the early stage of code dissemination, the code dissemination rate increases rapidly over time and have a high efficiency, but after a short time, the increase of code dissemination rate has become very slowly over time. So, in this situation, the unmanned aerial vehicle is used to spread the program codes of intelligent devices where vehicles are hard to spread, thereby eliminating the trailing phenomenon in code dissemination. In order to achieve a high dissemination efficiency for UAV, first, we cluster the device nodes which have not received codes, then we select the optimized UAV flight trajectory based on the cluster so that the total length of the UAV flight path is the shortest, i.e. low-cost, and the number of devices which can receive codes is the largest. Finally, the validity of UVDCD scheme is confirmed based on real vehicle data, and through experiments, it can effectively overcome the trailing phenomenon in code dissemination after using UAV. The coverage ratio and dissemination speed are higher than those of previous strategies and increase 2.11% and 69.44% respectively.

ANALYSIS OF BUNDLE ADJUSTMENTS AND EPIPOLAR MODEL ACCURACY ACCORDING TO FLIGHT PATH CHARACTERISTICS OF UAV

Abstract. Nowadays, UAVs(Unmanned aerial vehicles) are being used in spatial information construction. To construct spatial information, it is important to select appropriate data. Therefore, in this paper, we analyzed the characteristics of UAV flight paths and their effects on accuracy of bundle adjustments and epipolar models. We analyzed the flight path characteristics in three aspects: body stability, linearity of path and overlap between images. Firstly, in case of the body stability, ideal body stability is defined when images are taken in vertical direction. We calculated the body stability by the photographed angle, which is a difference between the vertical direction and actual photographed direction. Secondly, in case of the linearity of path, we are calculated the residual after fitting ground coordinates of orthogonal UAV positions or image centers by a straight line. Finally, in case of the overlap between images, we selected two closest images along vertical and horizontal direction as a pair and calculated the overlap of the pair on the ground space. We performed bundle adjustments and extracted EOPs(Exterior Orientation Parameters). Using the EOPs, we constructed an orthoimage, a DSM(Digital Surface model), and an epipolar model. By verified orthoimage and DSM, we were able to obtain accuracy of bundle adjustments. We also analyzed y-parallax of epipolar models to obtain its accuracy. Results showed that the body stability show very little affect vertical error of bundle adjustments. In case of linearity of path, there was little effect on the accuracy of bundle adjustment and epipolar models. The overlap between images affected all accuracy of bundle adjustments and epipolar model. The best accuracy was obtained when the overlap was about 70%.

Path Planning of Unmanned Air Vehicles Relay Communication Based on Lowest Power Loss

Unmanned aerial vehicle (UAV) relay communication is an important means to realize long-range wireless communication, where the lowest power loss could prolong the air residence time and increase the duration of the relay communication of UAV. In this paper, we present a UAV relay communication path planning based on the minimum encircling circle algorithm for the UAV working at the lowest power loss. We acquired the minimum radius of UAV relay communication based on for moving platform positions, and then determined the 3D position and flight path of UAV. Compared with the centroid enclosing circle algorithm, our simulation can reduce the signal transmitting power, realize the path planning of UAV relay communication under the lowest power loss.

Q-SQUARE: A Q-learning approach to provide a QoE aware UAV flight path in cellular networks

This paper deals with the adoption of Unmanned Aerial Vehicles (UAVs) as mobile Base Stations providing video streaming services within a cellular macro area. We devise a Q-learning based UAV flight planning algorithm aimed at improving the Quality of Experience (QoE) of video users. Specifically, the proposed algorithm, herein denoted as Q-SQUARE, leverages the well-established Q-learning algorithm by introducing a reward related to a key QoE metric that is the video segment delay. The Q-SQUARE algorithm also accounts for different UAV recharging stations being available in the covered area. The performance analysis, as a function of the number of UAVs and recharging stations, show that Q-SQUARE identifies the UAV flight paths, i.e. specific space-time allocation of the available bandwidth resources, that definitely improve the QoE of the streaming services.

UAV Relay Flight Path Planning in the Presence of Jamming Signal

The flight path of the unmanned aerial vehicle (UAV) relay can significantly affect the performance of the wireless relay communication system. In this paper, we investigate the optimization of the UAV flight path in a wireless relay communication system, when a jamming or interference signal is present. The UAV flight path is optimized to maximize the signal-to-interference-plus-noise ratio (SINR), and the required parameters are estimated. The simulation results show that the proposed method can achieve performance close to an ideal SINR optimization and also highlight the benefits of optimizing the SINR instead of the signal-to-noise ratio (SNR) when a jamming or interference signal is present.