Manned Aerial Vehicles Research Articles

Fault-tolerant security-efficiency combined authentication scheme for manned-unmanned teaming

Manned-unmanned teaming (MUM-T) is an emerging network system which interconnects manned aerial vehicles (MAVs) with unmanned aerial vehicles (UAVs) to enhance mission effectiveness and reduce workloads. Like other wireless systems, MUM-T is prone to attacks from the open communication channel. Therefore, an authentication scheme is required to establish secure and trusted communication between the MAV and the UAV. However, existing schemes fail to provide adequate security features and necessary efficiency, mostly due to their incomprehensive threat modeling and improper use of authentication techniques. Moreover, traditional centralized architecture of the authentication server leads to the single point of failure (SPOF) problem, which jeopardizes the robustness and scalability of MUM-T. In this paper, we propose a fault-tolerant authentication scheme for MUM-T that will solve these problems. To enhance its security, we construct a new threat model consisting of adversary's capabilities, security features, and security challenges to ensure the security of a scheme under combination attacks. To preserve the highest possible efficiency, we propose the design principle of using lightweight primitives for authentication and applying public key operations in key establishment. To address the SPOF problem, we employ a distributed fault-tolerant mechanism to share registration information within authentication servers and defend against faulty nodes. As is demonstrated by the security proof and performance comparison, our scheme succeeds in improving security and reducing overall costs, which provides a better solution than existing schemes.

Evaluating spray drift from Uncrewed Aerial Spray Systems: A machine learning and variance-based sensitivity analysis of environmental and spray system parameters

Uncrewed Aerial Spray Systems (UASS), commonly called drones, have become an important application technique for plant protection products in Asia and worldwide. As such, environmental variables and spray system parameters influencing spray drift deserve detailed investigations. This study presents the data analysis of 114 UASS drift trials conducted between December 2021 and December 2022 in China. Study design was based on the ISO 22866:2005 protocol for spray drift trials and considered different UASS platforms, nozzles, and release heights, and specifically continuously measured weather conditions. The relative importance of the environmental variables and spray system parameters was evaluated by a random forest (RF) feature importance analysis, a Sobol sensitivity analysis and partial dependence plots. This approach was preferred to linear ranking techniques such as ANOVA (analysis of variance) due to the non-linearity of the system. In addition, partial dependence plots are proposed to visualize the relationship between specific input parameters within the system.Drift deposition curves calculated from the 114 trials show good agreement with previous UASS trials reported in the literature. As reported in previous studies, spray drift following UASS applications is lower than for manned aerial vehicles, greater than for ground spray applications, and similar to drift observed from orchard air blast applications. In addition, 9 trials were conducted on corn fields in order to evaluate the potential effect of crop cover on spray drift. Spray drift was observed to be reduced over the cropped soil, suggesting that plant cover might possibly reduce spray drift. These findings could help supporting drift mitigation policies, stewardship advice and product labelling around the world.

Study of methods for searching and localizing objects in images from aircraft using convolutional neural networks

The use of unmanned and manned aerial vehicles for remote object localization and classification is very common. These methods are used in various systems, ranging from territory surveys to law enforcement. Methods of object localization and classification using neural networks require a detailed study and research of the quality of their work on data that has certain specifics, such as vehicle detection. The use of neural networks to detect certain types of objects using images obtained from aircraft can also help in the study of hard-to-reach locations. Therefore, the main subject of this paper is the localization and classification of objects in images obtained using digital cameras mounted on aircraft. The main focus is on determining the accuracy of object localization and detection using selected types of neural networks, which are the most important indicators of neural network efficiency. The speed of a neural network is also an equally important characteristic as it directly affects its ability to be used in tasks that require fast object localization, such as video surveillance or automated car control systems. The main goal of this study is to study the accuracy of object localization and classification in images obtained with the help of cameras mounted on aircraft, as well as to study the speed of neural networks and determine the effectiveness of their application in real-world conditions. The objectives of this study are to train YOLO v5, SSD, and Faster RCNNs on the VisDrone dataset and to further study them on the vehicle localization dataset. The main goal of this work is to obtain statistics on the performance of neural networks trained on the VisDrone dataset. On the basis of the obtained statistics, conclusions are drawn about the effectiveness of the considered neural networks. The conclusions are drawn by considering the speed of the model, localization (IoU), and classification (Precision, Recall) metrics. Possible directions for further development of the topic under study are presented as conclusions.

Energy efficient gateway based routing with maximized node coverage in a UAV assisted wireless sensor network.

Ad-hoc wireless sensor networks face challenges of optimized node deployment for maximizing coverage and efficiently routing data to control centers in post disaster events. These challenges impact the outcome for extending the lifetime of wireless sensor networks. This study presents a uav assisted reactive zone based EHGR (energy efficient hierarchical gateway routing protocol) that is deployed in a situation where the natural calamity has caused communication and infrastructure damage to a major portion of the sensor network. EHGR is a hybrid multi layer routing protocol for large heterogeneous sensor nodes (smart nodes, basic nodes, user handheld devices etc.) EHGR is tailored to meet two important concerns for a disaster hit wsn ie. optimized deployment and energy efficient routing. The first part of EGHR focuses on maximized coverage during node deployments. Maximized coverage is an important aspect to be considered during the event of disaster since most of the nodes loose coverage and are detached from the wireless sensor network. The first part of EHGR uses state of the art game theory approach to build a model that maximizes the coverage of nodes during the deployment phase from all participating entities i.e. nodes and uavs. Rather than fixing the cluster head as is the case in traditional cluster-based approaches EHGR uses the energy centroid nodes. Energy centroid nodes evolve on the basis of aggregated energy of the zone. This approach is superior to simply electing cluster head nodes on the basis of some probability function. The nodes that fail to achieve any successful outcome from the game theory matching model fail to get any association. These nodes will use multi hop d2d relay approach to reach the energy centroid nodes. Gateway relay nodes used with the game theory approach during the deployment of the uav assisted wsn improves the overall coverage by 25% against traditional leach based hierarchical approaches. Once the optimum deployment phase is completed the routing phase is initiated. Aggregated data is sent by the energy centroid nodes from the ECN nodes to the servicing micro controller enabled un manned aerial vehicles. The routing process places partial burden of zone formation and data transmission to the control center for each phase on the servicing uavs. Energy centroid nodes engage only in the data aggregation process and transmission of data to servicing uav. Servicing-uavs reduce energy dissipated of the entire zone which result in gradual decrease of energy for the zone thus increasing the network lifetime. Node deployment phase and the routing phase of EHGR utilize the computations provide by the mirco controller enabled unmanned aerial vehicles such that the computationally intensive calculations are offloaded to the servicing uav. Experiment results indicate an increase in the first dead node report, half dead node report, and last dead node report. Network lifetime is extended to approximately 1800 rounds which is an increase by ratio of 100% against the traditional leach approach and increase by 50% percent against the latest approaches as highlighted in the literature.

In-Vehicle Speech Recognition for Voice-Driven UAV Control in a Collaborative Environment of MAV and UAV

Most conventional speech recognition systems have mainly concentrated on voice-driven control of personal user devices such as smartphones. Therefore, a speech recognition system used in a special environment needs to be developed in consideration of the environment. In this study, a speech recognition framework for voice-driven control of unmanned aerial vehicles (UAVs) is proposed in a collaborative environment between manned aerial vehicles (MAVs) and UAVs, where multiple MAVs and UAVs fly together, and pilots on board MAVs control multiple UAVs with their voices. Standard speech recognition systems consist of several modules, including front-end, recognition, and post-processing. Among them, this study focuses on recognition and post-processing modules in terms of in-vehicle speech recognition. In order to stably control UAVs via voice, it is necessary to handle the environmental conditions of the UAVs carefully. First, we define control commands that the MAV pilot delivers to UAVs and construct training data. Next, for the recognition module, we investigate an acoustic model suitable for the characteristics of the UAV control commands and the UAV system with hardware resource constraints. Finally, two approaches are proposed for post-processing: grammar network-based syntax analysis and transaction-based semantic analysis. For evaluation, we developed a speech recognition system in a collaborative simulation environment between a MAV and an UAV and successfully verified the validity of each module. As a result of recognition experiments of connected words consisting of two to five words, the recognition rates of hidden Markov model (HMM) and deep neural network (DNN)-based acoustic models were 98.2% and 98.4%, respectively. However, in terms of computational amount, the HMM model was about 100 times more efficient than DNN. In addition, the relative improvement in error rate with the proposed post-processing was about 65%.

Cluster Space Control Method of Manned-Unmanned Aerial Team for Target Search Task

This brief proposed a cluster space control method to help the operator of manned aerial vehicles (MAVs) to coordinate with unmanned aerial vehicles (UAVs) more efficiently. The operator of the MAV could change the states of the whole heterogeneous team only by issuing a single command. To verify its application performance, the method is employed to conduct search tasks under a complex environment with static and dynamic unknown targets. The receding horizon control (RHC) and backstepping control methods are utilized to complete the search task. Simulation results show the feasibility and efficiency of the proposed method and give a strong foundation for man-machine coordination in related fields.

A Dual Aircraft Maneuver Formation Controller for MAV/UAV Based on the Hybrid Intelligent Agent

This paper proposes a hybrid intelligent agent controller (HIAC) for manned aerial vehicles (MAV)/unmanned aerial vehicles (UAV) formation under the leader–follower control strategy. Based on the high-fidelity three-degrees-of-freedom (DOF) dynamic model of UAV, this method decoupled multiple-input-multiple-output (MIMO) systems into multiple single-input-single-output (SISO) systems. Then, it innovatively combined the deep deterministic policy gradient (DDPG) and the double deep Q network (DDQN) to construct a hybrid reinforcement learning-agent model, which was used to generate onboard desired state commands. Finally, we adopted the dynamic inversion control law and the first-order lag filter to improve the actual flight-control process. Under the working conditions of a continuous S-shaped large overload maneuver for the MAV, the simulations verified that the UAV can achieve accurate tracking for the complex trajectory of the MAV. Compared with the traditional linear quadratic regulator (LQR) and DDPG, the HIAC has better control efficiency and precision.

SMART TRANSPORT FOR SMART CITIES: A FUTURISTIC SCENARIO AND A REALISTIC PROJECT

Abstract. Smart transport for smart cities: our article presents an innovative and original idea in the field of urban air transport with a new type of manned drone. This scenario is analysed starting from some initial futuristic ideas up to the reality of the construction of such a vehicle. After a brief presentation of a list of manned aerial vehicles (manned drones) that reflects the efforts that have been realized until now, a number of parameters and specifications related to such aerial urban transport, are also pointed out. In addition, various emerging issues comprising legislation matters, infrastructure for landing and take-off in cities, navigation methods and other possible impacts on the urban transport, have also been presented shortly.

Performance and Feasibility of Drone-Mounted Imaging Spectroscopy for Invasive Aquatic Vegetation Detection

Invasive plants are non-native species that can spread rapidly, leading to detrimental economic, ecological, or environmental impact. In aquatic systems such as the Sacramento-San Joaquin River Delta in California, USA, management agencies use manned aerial vehicles (MAV) imaging spectroscopy missions to map and track annual changes in invasive aquatic plants. Advances in unmanned aerial vehicles (UAV) and sensor miniaturization are enabling higher spatial resolution species mapping, which is promising for early detection of invasions before they spread over larger areas. This study compared maps made from UAV-based imaging spectroscopy with the manned airborne imaging spectroscopy-derived maps that are currently produced for monitoring invasive aquatic plants in the Sacramento-San Joaquin Delta. Concurrent imagery was collected using the MAV mounted HyMap sensor and the UAV mounted Nano-Hyperspec at a wetland study site and classification maps generated using random forest models were compared. Classification accuracies were comparable between the Nano- and HyMap-derived maps, with the Nano-derived map having a slightly higher overall accuracy. Additionally, the higher resolution of the Nano imagery allowed detection of patches of water hyacinth present in the study site that the HyMap could not. However, it would not be feasible to operate the Nano as a replacement to HyMap at scale despite its improved detection capabilities due to the high costs associated with overcoming area coverage limitations. Overall, UAV-based imaging spectroscopy provides comparable or improved capability, and we suggest it could be used to supplement existing monitoring programs by focusing on target areas of high ecologic or economic priority.

Optimized link state routing protocol performance in flying ad-hoc networks for various data rates of Un manned aerial network

In the wireless communication, nowadays Flying adhoc network or UAV networks hold the most important place in the field of recent research topic. The drones or Un manned aerial vehicles which are enabled with camera, special circuit, sensors and advanced communication systems are coordinated among each other under the Network called as Flying adhoc network. FANET plays vital role in both military and civil applications. In spite of all these pros there are some unavoidable challenges and issues bonds around FANET. Since FANET possess the characteristics of dynamic rapid changing mobility, the choice of Routing protocol in FANET also open wide space in the field of Research. In our previous papers the topology based Routing protocols are analysed under different network scenarios and described the best performed one. It has been also found that the type OLSR is the better choice of FANET in terms of delay, Network load, Throughput and retransmission attempts. The OLSR routing protocol mechanism depends on the link state algorithm. It is proactive and table driven in nature. In order to maintain the topology information of the network, it completely supports the periodic exchanges of messages among themselves. OLSR is good in compacting the size of information sent in the messages. It avoids flooding by controlling the retransmissions. To exhibits this, the protocol utilizes multi point relaying techniques. It also provides optimal routes and this is completely suitable for dense and large networks. In this paper the performance of OLSR is investigated and analysed under 4 different network scenarios and different data rates. In a simulation using OPNET, the results show that the Optimized link state routing protocol has greater improved key indicators.

Centralized Interference Coordination of Cluster heads for UAVs Swarms

In this paper, we coordinate the challenging interference problem of UAVs swarms as to maximizing the throughput of networks with Doppler frequency shifts and jamming, a hierarchical framework of group-centric network is considered for manned aerial vehicles operated in time division duplex (TDD) modes. In this case, according to different frequency division duplex (FDD) modes in cluster heads and sub-networks, the approximate methods for cross-tier and co-tier interferences are derived. Furthermore, an interference coordination algorithm is proposed to achieve a better trade-off for the performance of UAVs swarms based on dynamic particle swarm optimization (PSO). Simulation results compared with traditional optimization algorithms, demonstrates the proposed algorithm can improve the overall throughput significantly.

UAVs for Wilderness Search and Rescue: Real-World Considerations and Technology Roadmap for Fixed Wing UAVs

Wilderness search and rescue is predominantly conducted by ground based teams, however its limitations have encouraged the use of alternative approaches. Aerial search and rescue provides complementary capabilities as it has a higher areal coverage rate and can survey challenging terrain that is not easily accessible. Helicopters and other manned aerial vehicles dominate aerial search, but disadvantages, such as the need for highly trained personnel, slow response time, and high capital requirements, limit their use. Unmanned aerial vehicles (UAVs) capture the benefits of helicopters while overcoming many of the associated drawbacks that limit their use for wilderness search and rescue. Here we demonstrate the need for UAVs in wilderness search and rescue. We compare the two main types of UAVs—fixed wing and rotary—along three performance factors, including flight range, image quality, and degree of control over the flight path. Theoretical and practical considerations for fixed wing UAVs are explored. Finally, we present practical technology ideas to improve the utility of both fixed wing and rotary UAVs. This work offers an introduction to aerial search and rescue with UAVs, assesses real-world tradeoffs between fixed wing and rotary UAVs, and presents a roadmap for further technology development to advance this technology into the field. KEYWORDS: UAVs, fixed wing UAVs, rotary UAVs, wilderness search and rescue, aerial search and rescue

Aerial seeding of forests in Russia: A selected literature analysis

The study is intended for international readers or those unable to read Russian. Information search was carried out in Russian-language databases (Russian libraries and ELibrary.ru) and English-language databases. . Currently, there are a large number of sites in the Russian forests that need to be restored. For many of them, reforestation by ground-based seeding or planting is inefficient or not available. Sowing forest from the air in Russia has more than half a century of history. The results of retrospective analysis show that the choice of the reforestation method depends on the site characteristics and the level of negative environmental impact. Generally, the cost of aerial seeding on such sites with unmanned aerial vehicles (UAV) is lower compared to the use of manned aerial vehicles (MAV). This study will aid in the planning of new forestry experiments to study reforestation with the assistance of natural reforestation in Russia. The study will enable correct plant propagation protocols for sustainable forest management. However, many questions remained unresolved: what types of UAV and sowing apparatus to use; how to position the UAV under a forest canopy?

Collision Avoidance and Path Following Control of Unmanned Aerial Vehicle in Hazardous Environment

Recent years have seen a rapidly increasing number of applications using unmanned aerial vehicles (UAVs). In order to greatly enhance the capabilities of UAVs working along with other manned aerial vehicles in more cluttered, hazardous, and sophisticated environments without violating the aviation traffic regulations, this paper proposes a new hybrid collision avoidance method along with a modified path following approach. The proposed hybrid collision avoidance scheme consists of a global path planner and a local collision avoidance mechanism for the purpose of greatly reducing computational efforts raised by global method, while ensuring the safe and satisfactory performance of collision avoidance. The global path planner is designed using rapidly exploring random tree (RRT), and the dynamics of UAV are also taken into consideration to generate a feasible and optimal path. The light-computational local collision avoidance mechanism, which can partially modify the globally planned path in dynamic environment when any hazardous obstacles blocking the desired path, is developed based on an intelligent fuzzy logic approach by integrating a set of decision making strategies and several aviation traffic regulations. Regarding the presented path following methodology, an improvement to the previous cross-track error calculation mechanism is made by employing the extended Kalman filter (EKF) to estimate the cross-track error. Finally, design of height and attitude control system of UAV is also addressed. Extensive simulation and experimental studies on a series of scenarios with both static and dynamic objects are conducted to demonstrate the effectiveness of the proposed hybrid collision avoidance approach and cross-track error prediction based path following method.

Economics of Mapping Using Small Manned and Unmanned Aerial Vehicles

Economics of Mapping Using Small Manned and Unmanned Aerial Vehicles

How accurate are small drones for measuring microscopic traffic parameters?

Small Unmanned Aerial Vehicles (sUAV or drones) have been one of the latest tools for monitoring transportation infrastructure and operations. Their lower cost compared to current fixed location camera systems or Manned Aerial Vehicles (MAV) and their ability to read just their view area depending on the situation they face, make them a promising tool of collecting both macroscopic and microscopic data. However, although drone technology and computer vision techniques are advancing fast, there is little information on how accurate and reliable they are for collecting microscopic traffic data. In this paper, we examine the potential of using sUAV as part of the ITS infrastructure as a way of extracting naturalistic trajectory data from aerial video footage from a low volume four-way intersection and a pedestrian passage. Moreover, the accuracy of speed data collected from a drone compared to data collected from an On-Board Diagnostics II (OBD-II) device is examined. For this, a controlled experiment where the vehicle was driven in various speeds and the drone flew in ranging altitudes was conducted. Results show that accuracy is highly dependent on the stabilization of the video and the geo-reference procedure. Moreover, the capabilities of such systems are examined in traffic applications and the way they can be part of future transportation infrastructure is discussed.

Foreword to the Special Issue on Hyperspectral Remote Sensing and Imaging Spectroscopy

The twenty six papers in this special issue focus on the technologies of hyperspectral remote sensing (HRS)and imaging spectroscopy. HRS has emerged as a powerful tool to understand phenomena at local and global scales by virtue of imaging through a diverse range of platforms, including terrestrial in-situ imaging platforms, unmanned and manned aerial vehicles, and satellite platforms. By virtue of imaging over a wide range of spectral wavelengths, it is possible to characterize object specific properties very accurately. As a result, hyperspectral imaging (also known as imaging spectroscopy) has gained popularity for a wide variety of applications, including environment monitoring, precision agriculture, mineralogy, forestry, urban planning, and defense applications. The increased analysis capability comes at a cost—there are a variety of challenges that must be overcome for robust image analysis of such data, including high dimensionality, limited sample size for training supervised models, noise and atmospheric affects, mixed pixels, etc. The papers in this issue represent some of the recent developments in image analysis algorithms and unique applications of hyperspectral imaging data.

실제지형을 고려한 고정익 무인항공기의 최적 경로계획

This article describes a path planning algorithm for fixed-wing UAVs when a real terrain should be considered. Nowadays, many UAVs are required to perform mission flights near given terrain for surveillance, reconnaissance, and infiltration, as well as flight altitude of many UAVs are relatively lower than typical manned aerial vehicles. Therefore, real terrain should be considered in path planning algorithms of fixed-wing UAVs. In this research, we have extended a spline-RRT* algorithm to three-dimensional planner. The spline-RRT* algorithm is a RRT* based algorithm, and it takes spline method to extend the tree structure over the workspace to generate smooth paths without any post-processing. Direction continuity of the resulting path is guaranteed via this spline technique, and it is essential factor for the paths of fixed-wing UAVs. The proposed algorithm confirm collision check during the tree structure extension, so that generated path is both geometrically and dynamically feasible in addition to direction continuity. To decrease degrees of freedom of a random configuration, we designed a function assigning directions to nodes of the graph. As a result, it increases the execution speed of the algorithm efficiently. In order to investigate the performance of the proposed planning algorithm, several simulations are performed under real terrain environment. Simulation results show that this proposed algorithm can be utilized effectively to path planning applications considering real terrain.

Regulating And Helix Path Tracking For Unmanned Aerial Vehicle (uav) Using Fuzzy Logic Controllers

In recent years, most of researchers attempt to replace manpower with robots because of their ability to do repetitive work and also their accuracy in critical condition. Unmanned Aerial Vehicles (UAVs) are attained more attention for their advantages rather than other kind of manned aerial vehicles (MAVs). Quadrotor is a special kind of UAVs with simple mechanical structure and high maneuverability for excellence. Also, quadrotor is a 6-DOF (six degree of freedom) system with high nonlinearity in terms of dynamic equations equipped by four rotors. Consequently, there is the nonlinear under-actuated system with 6-DOF and four angular speeds as system input. Although the high nonlinearity nature of system dynamic cause some difficultly in controlling process, this can be helpful in some cases which quick response is needed. Therefore, the aim of this study is to plan such controller which is able to provide all maneuvers in all reachable direction (maneuvers in all altitude and attitude). In this study, the model of system is considered as Multiple Input-Multiple Output (MIMO) and three fuzzy logic controllers (FLC) are proposed to make system regulated with constant value and tracked with helix path. Finally, results indicate good performance in both regulation and tracking purpose.

Identification, Modeling and Control of Unmanned Aerial Vehicles

This paper presents a method to control height and speed of fixed wing unmanned aerial vehicles (UAVs) using MATLAB platform. The mathematical dynamic model of Six Degrees of Freedom (6-DOF) aircraft is nonlinear and it is linearized about a flight condition. System is divided into two Multi Input Multi Output (MIMO) sub models. First model controls the longitudinal dynamics and second model is used to control the lateral dynamics. Each MIMO model is converted into ten Single Input Single Output (SISO) systems and then the systems needed for the primary requirement of plant control is selected. Same technique can be also be used to make Manned Aerial Vehicles (MAV) autopilots and to control flight of other aerial vehicles. Multiple loop feedback control technique is used to control a plant which helps to control many outputs of system with one set point value and controller.