Aerial Vehicle System Research Articles

Coordinated transportation of tethered multi-rotor UAVs based on differential graphical games

This paper investigates the stability control of the tethered multi-rotor unmanned aerial vehicles (T-MRUAVs) system in cargo delivery, where precise payload trajectory control is deemed unnecessary, with a focus on enhancing controller simplicity and robustness. To achieve this objective, it is imperative to employ an optimal formation tracking control, ensuring not only the symmetrical positioning of UAVs concerning the payload but also the achievement of distributed optimal solutions. Therefore, a formation tracking control strategy based on differential graphical games is designed to ensure optimized performance metrics and minimized suspended load swing. The designed control framework includes an outer loop and an inner loop control, which respectively achieve formation tracking and attitude stability. In addition to maintaining equilibrium states, each UAV is required to track the desired trajectory. Consequently, the formation tracking control consists of a gravity compensation term at the equilibrium point and a formation tracking control term based on differential graphical games. Then, a PID control strategy is displayed for the inner loop control, and the altitude control adopts feedback linearization. Finally, numerical simulations have validated the effectiveness of the designed algorithm.

Aerial Surveillance Leveraging Delaunay Triangulation and Multiple-UAV Imaging Systems

In aerial surveillance systems, achieving optimal object detection precision is of paramount importance for effective monitoring and reconnaissance. This article presents a novel approach to enhance object detection accuracy through the integration of Delaunay triangulation with multi-unmanned aerial vehicle (UAV) systems. The methodology involves positioning multiple UAVs at pre-specified locations using the Delaunay triangulation algorithm with performance of O (n log n). This is compared with the conventional single UAV approach at a near distance. Our findings reveal that the collaborative efforts of multiple UAVs, guided by Delaunay triangulation, significantly improves object detection accuracy, especially when compared to a single UAV operating in close proximity. This research employs advanced image processing techniques to identify objects in the area under surveillance. Results indicate a substantial enhancement in the collective surveillance capabilities of the multi-UAV system, demonstrating its efficacy in unconstrained scenarios. This research not only contributes to the optimization of aerial surveillance operations but also underscores the potential of spatially informed UAV networks for applications demanding heightened object detection accuracy. The integration of Delaunay triangulation with multi-UAV systems emerges as a promising strategy for advancing the capabilities of aerial surveillance in scenarios ranging from security and emergency response to environmental monitoring.

Comparative analysis of unmanned aerial vehicle and conventional spray systems for the maize fall armyworm Spodoptera frugiperda (J.E. Smith) (Lepidoptera; Noctuidae) management

Comparative analysis of unmanned aerial vehicle and conventional spray systems for the maize fall armyworm Spodoptera frugiperda (J.E. Smith) (Lepidoptera; Noctuidae) management

Adaptive prescribed performance based on recursive nonsingular terminal sliding mode control for quad-rotor systems under uncertainty and disturbance: Real-time validation

In this paper, at the aim of the fast trajectory-following control of the Unmanned Aerial Vehicle (UAV) systems subject to uncertainty and disturbance, the adaptive prescribed performance control based recursive nonsingular terminal sliding mode control is suggested. Afterward, for the fast trajectory-following control of the uncertain and perturbed quad-rotor system, a recursive nonsingular terminal sliding mode strategy is recommended. Since the better transient and steady-state response of the sliding mode surface is of the utmost importance in the design of the controller, the prescribed performance control scheme is proposed in which the transformed prescribed form of the previously proposed recursive nonsingular terminal sliding surface is obtained to prove that the recursive nonsingular terminal sliding surface can converge to a preset region. Whereas unknown bounded uncertainty and disturbance always exist in the UAV system's dynamical model which decreases its performance and efficiency, the adaptive mechanism is applied to approximate uncertain bounds of uncertainty and disturbance to advance the implementation of the UAV system. Later, quick reachability of the prescribed form switching surface is demonstrated using Lyapunov concept. Ultimately, simulation results using MATLAB/Simulink over a quad-rotor system and real-time simulation using the Speedgoat Real-Time Target Machine platform are conducted to validate the feasibility and performance of the proposed method.

A novel recurrent self-evolving fuzzy neural network for consensus decision-making of unmanned aerial vehicles

Currently, for years, unmanned aerial vehicles have been widely applied in a comprehensive realm. By enhancing computer photography and artificial intelligence, it can automatically discriminate against environmental objectives and detect events that occur in the real scene. The application of collaborative unmanned aerial vehicles will offer diverse interpretations which support a multiperspective view of the scene. Due to diverse interpretations of unmanned aerial vehicles usually deviates, thus, unmanned aerial vehicles require a consensus interpretation for the scenario. To previous purposes, this study presents an original consensus-based method to pilot multi-unmanned aerial vehicle systems for achieving consensus on their observation as well as constructing a group situation-based depiction of the scenario. Further, a fuzzy neural network generalized prediction control system known as a recurrent self-evolving fuzzy neural network is mainly used to ensure stability through the use of a descending gradient online learning rule. At the same time, users can think along the lines of evolutionary biological design. Unmanned aerial vehicles can be modeled as system experts for solving group problems that require the definition of conditions that best describe the scene. First, this method allows each unmanned aerial vehicle to set high-level conditions for detection events by aggregating events based on fuzzy information. These aggregated events are modeled by a fuzzy system ontology, which allows each unmanned aerial vehicle to report its preferences in conditions. Therefore, the interpretation of each drone is compressed to achieve a collective interpretation of the state. The final polls, consent and affinity polls confirmed the final decision group’s reliability ratings. The rated consensus indicates how well the collective interpretation of the scene matches each drone’s point of view.

Full-scale simulator to test a control system of an engine-propeller powerplant of a convertible aerial vehicle

The article presents the results of work involved with developing a full-scale simulator for research into determining the structure and parameters of the control system for unmanned aerial copter-type vehicles with a powerplant comprising electric motors with fixed-pitch propellers. The features of the engineering implementation of the simulator, taking into account the prospects for its development in terms of greater maneuverability (pitch, roll and yaw), are presented. The implemented principle of the Simulink integration – a model of the control object, a controller based on the Arduino platform, a gyroscope-accelerometer to organize feedbacks for the purpose of forming algorithms of the automatic and positional (manual) pitch angle control, manual motor revs control is described. The analysis of the full-scale simulation results in terms of the quality of transients and power costs for various settings of the PID-regulator, which provides generating a signal of electric motor revolutions, is presented. It is concluded that it is feasible to create and use an experimental base to justify the use of adaptive control algorithms for unmanned aerial copter-type vehicles with elements of artificial intelligence to ensure the required flying characteristics in a wide range of properties of control objects.

Precise Drought Threshold Monitoring in Winter Wheat Using the Unmanned Aerial Vehicle Thermal Method

Accurate monitoring of crop drought thresholds at different growth periods is crucial for drought monitoring. In this study, the canopy temperature (Tc) of winter wheat (‘Weilong 169’ variety) during the three main growth periods was extracted from high-resolution thermal and multispectral images taken by a complete unmanned aerial vehicle (UAV) system. Canopy-air temperature difference (ΔT) and statistic Crop Water Stress Index (CWSIsi) indicators were constructed based on Tc. Combined experiment data from the field and drought thresholds for the ΔT and CWSIsi indicators for different drought levels at three main growth periods were monitored. The results showed a strong correlation between the Tc extracted using the NDVI-OTSU method and ground-truth temperature, with an R2 value of 0.94. The CWSIsi was more stable than the ΔT index in monitoring the drought level affecting winter wheat. The threshold ranges of the CWSIsi for different drought levels of winter wheat at three main growth periods were as follows: the jointing–heading period, where the threshold ranges for normal, mild drought, moderate drought, and severe drought are <0.30, 0.30–0.42, 0.42–0.48, and >0.48, respectively; the heading–filling period, where the threshold ranges for normal, and mild, moderate, and severe drought are <0.33, 0.33–0.47, 0.44–0.53, and >0.53, respectively; and the filling–maturation period, where the threshold ranges for normal, mild drought, moderate drought, and severe drought are <0.41, 0.41–0.54, 0.54–0.59, and >0.59, respectively. The UAV thermal threshold method system can improve the accuracy of crop drought monitoring and has considerable potential in crop drought disaster identification.

UAV-mounted thermal camera and its analysis on urban surface textures

Temperature increase, especially global warming, can be observed depending on various factors which led to several severe environmental problems. Urban areas are the most effected places by this temperature increase. Urban heat concentration, the so-called heat island effect, is high in structural areas. This situation causes human life to be adversely affected. Therefore, constant measurement and analyses are required to assess outdoor thermal comfort and thermal stress in urban areas. Today, unmanned aerial vehicle (UAV) systems are used as a rapid data production technique in Earth observation activities. Thermal cameras integrated into UAV systems can monitor the temperature values in urban areas precisely and constantly. This study focuses on the potential application of a UAV-mounted thermal camera system at a local scale due to its rapid response to surface temperature variables. A thermal camera UAV system to measure the energy fluxes and temperatures from the earth’s surface, which are integral to understanding landscape processes and responses. Thus, UAV thermal sensors were used directly for different land cover types in and around the Faculty of Engineering building of Kocaeli University in Turkey. Derived UAV surface temperatures were compared with simultaneously acquired in situ temperature measurements. Simultaneous terrestrial temperature measurements were obtained by using TFA ScanTemp 410 model surface temperature meter. A high correlation between UAV surface temperatures and terrestrial measurements was utilized by Pearson correlation with a 0.94 coefficient. It was concluded that the UAV-mounted thermal camera system is a promising tool that has increased opportunities to understand surface temperature variability at high spatial and temporal resolution.

Study on Practical Design of Datalink in Interoperable UAV Systems

Uumanned aerial vehicle(UAV) systems have been used in various fields including industry and military. According to increasing the number of UAVs, the attention on interoperable UAV systems is increasing. In this paper, we propose the practical design of datalink in interoperable UAV systems. For practical design, we firstly review the operational scenarios in the interoperable UAV system. We then propose the system model of the datalink in interoperable UAV system. Consequently, the technical components such as the design of the network, the link management, the support of the multicast transmission, the support for autonomous mission and flight safety, and the datalink security are derived and reviewed for the practical design.

Development of Unmanned Aerial Vehicle System Integration Laboratory(UAV SIL) for the Integrated Verification

This paper describes the results of the development of the the unmanned aerial vehicle system integration laboratory(UAV SIL) for the integrated verification. This UAV SIL is designed to test the robustness of the UAV system including the operational logics and the flight control system behaviors under many abnormal and emergency conditions such as data-link losses, airborne subsystem failures, engine shut down conditions, and ground control station faults.This paper presents how to build the UAV SIL and how to verify the in-development UAV system through the UAV SIL.

When tomorrow comes: A prospective risk assessment of a future artificial general intelligence-based uncrewed combat aerial vehicle system

There are concerns that Artificial General Intelligence (AGI) could pose an existential threat to humanity; however, as AGI does not yet exist it is difficult to prospectively identify risks and develop requisite controls. We applied the Work Domain Analysis Broken Nodes (WDA-BN) and Event Analysis of Systemic Teamwork-Broken Links (EAST-BL) methods to identify potential risks in a future ‘envisioned world’ AGI-based uncrewed combat aerial vehicle system. The findings suggest five main categories of risk in this context: sub-optimal performance risks, goal alignment risks, super-intelligence risks, over-control risks, and enfeeblement risks. Two of these categories, goal alignment risks and super-intelligence risks, have not previously been encountered or dealt with in conventional safety management systems. Whereas most of the identified sub-optimal performance risks can be managed through existing defence design lifecycle processes, we propose that work is required to develop controls to manage the other risks identified. These include controls on AGI developers, controls within the AGI itself, and broader sociotechnical system controls.

Autonomous Site Inspection of Power Transmission Line Insulators with Unmanned Aerial Vehicle System

The inspection of overhead power transmission line and assets is an essential aspect to improve the overhead power transmission efficiency and to ensure an uninterrupted power supply. This article has mainly focused on the progression and fabrication of indigenous quadcopter/Unmanned Aerial Vehicle (UAV) for carrying autonomous operations in a coordinated movement along the overhead transmission towers for capturing the images and videos of transmission insulators and assets. A custom based dataset of power line insulators is created by using the quadcopter for overcoming the data scarcity and to perform Deep Learning (DL) assessment for (i) inadequate data for training and (ii) power line insulator detection and faults. The experimental results showcase that, the suggested DL architecture identifies power line insulators and associated faults, such as cracks, broken disk and missing top caps etc. With a detection speed of 56.8 frames/sec and an accuracy of 94.1%, the proposed DL technique has much promise for intelligent examination of power grid insulators. Ecological Footprint assessment of different power line inspection methods are also examined in this study.

Fuzzy logic controller for UAV with gains optimized via genetic algorithm

A gains optimizer of a fuzzy controller system for an Unmanned Aerial Vehicle (UAV) based on a metaheuristic algorithm is developed in the present investigation. The contribution of the work is the adjustment by the Genetic Algorithm (GA) to tune the gains at the input of a fuzzy controller. First, a typical fuzzy controller was modeled, designed, and implemented in a mathematical model obtained by Newton-Euler methodology. Subsequently, the control gains were optimized using a metaheuristic algorithm. The control objective is that the UAV consumes the least amount of energy. With this basis, the Genetic Algorithm finds the necessary gains to meet the design parameters. The tests were performed using the Matlab-Simulink environment. The results indicate an improvement, reducing the error in tracking trajectories from 30% in some tasks and following trajectories that could not be completed without a tuned controller in other tasks.

Autopilot control unmanned aerial vehicle system for sewage defect detection using deep learning

AbstractThis work proposes the use of an unmanned aerial vehicle (UAV) with an autopilot to identify the defects present in municipal sewerage pipes. The framework also includes an effective autopilot control mechanism that can direct the flight path of a UAV within a sewer line. Both of these breakthroughs have been addressed throughout this work. The UAV's camera proved useful throughout a sewage inspection, providing important contextual data that helped analyze the sewerage line's internal condition. A plethora of information useful for understanding the sewerage line's inner functioning and extracting interior visual details can be obtained from camera‐recorded sewerage imagery if a defect is present. In the case of sewerage inspections, nevertheless, the impact of a false negative is significantly higher than that of a false positive. One of the trickiest parts of the procedure is identifying defective sewerage pipelines and false negatives. In order to get rid of the false negative outcome or false positive outcome, a guided image filter (GIF) is implemented in this proposed method during the pre‐processing stage. Afterwards, the algorithms Gabor transform (GT) and stroke width transform (SWT) were used to obtain the features of the UAV‐captured surveillance image. The UAV camera's sewerage image is then classified as “defective” or “not defective” using the obtained features by a Weighted Naive Bayes Classifier (WNBC). Next, images of the sewerage lines captured by the UAV are analyzed using speed‐up robust features (SURF) and deep learning to identify different types of defects. As a result, the proposed methodology achieved more favorable outcomes than prior existing approaches in terms of the following metrics: mean PSNR (71.854), mean MSE (0.0618), mean RMSE (0.2485), mean SSIM (98.71%), mean accuracy (98.372), mean specificity (97.837%), mean precision (93.296%), mean recall (94.255%), mean F1‐score (93.773%), and mean processing time (35.43 min).

Multi-UAV simultaneous target assignment and path planning based on deep reinforcement learning in dynamic multiple obstacles environments.

Target assignment and path planning are crucial for the cooperativity of multiple unmanned aerial vehicles (UAV) systems. However, it is a challenge considering the dynamics of environments and the partial observability of UAVs. In this article, the problem of multi-UAV target assignment and path planning is formulated as a partially observable Markov decision process (POMDP), and a novel deep reinforcement learning (DRL)-based algorithm is proposed to address it. Specifically, a target assignment network is introduced into the twin-delayed deep deterministic policy gradient (TD3) algorithm to solve the target assignment problem and path planning problem simultaneously. The target assignment network executes target assignment for each step of UAVs, while the TD3 guides UAVs to plan paths for this step based on the assignment result and provides training labels for the optimization of the target assignment network. Experimental results demonstrate that the proposed approach can ensure an optimal complete target allocation and achieve a collision-free path for each UAV in three-dimensional (3D) dynamic multiple-obstacle environments, and present a superior performance in target completion and a better adaptability to complex environments compared with existing methods.

A Fuzzy-Based System for Autonomous Unmanned Aerial Vehicle Ship Deck Landing.

This paper introduces a fuzzy logic-based autonomous ship deck landing system for fixed-wing unmanned aerial vehicles (UAVs). The ship is assumed to maintain a constant course and speed. The aim of this fuzzy logic landing model is to simplify the task of landing UAVs on moving ships in challenging maritime conditions, relieving operators from this demanding task. The designed UAV ship deck landing model is based on a fuzzy logic system (FLS), which comprises three interconnected subsystems (speed, lateral motion, and altitude components). Each subsystem consists of three inputs and one output incorporating various fuzzy rules to account for external factors during ship deck landings. Specifically, the FLS receives five inputs: the range from the deck, the relative wind direction and speed, the airspeed, and the UAV's flight altitude. The FLS outputs provide data on the speed of the UAV relative to the ship's velocity, the bank angle (BA), and the angle of descent (AOD) of the UAV. The performance of the designed intelligent ship deck landing system was evaluated using the standard configuration of MATLAB Fuzzy Toolbox.

GPS-Free Wireless Precise Positioning System for Automatic Flying and Landing Application of Shipborne Unmanned Aerial Vehicle.

This research is dedicated to developing an automatic landing system for shipborne unmanned aerial vehicles (UAVs) based on wireless precise positioning technology. The application scenario is practical for specific challenging and complex environmental conditions, such as the Global Positioning System (GPS) being disabled during wartime. The primary objective is to establish a precise and real-time dynamic wireless positioning system, ensuring that the UAV can autonomously land on the shipborne platform without relying on GPS. This work addresses several key aspects, including the implementation of an ultra-wideband (UWB) circuit module with a specific antenna design and RF front-end chip to enhance wireless signal reception. These modules play a crucial role in achieving accurate positioning, mitigating the limitations caused by GPS inaccuracy, thereby enhancing the overall performance and reception range of the system. Additionally, the study develops a wireless positioning algorithm to validate the effectiveness of automatic landing on the shipborne platform. The platform's wave vibration is considered to provide a realistic landing system for shipborne UAVs. The UWB modules are practically installed on the shipborne platform, and the UAV and the autonomous three-body vessel are tested simultaneously in the outdoor open water space to verify the functionality, precision, and adaptability of the proposed UAV landing system. Results demonstrate that the UAV can autonomously fly from 200 m, approach, and automatically land on the moving shipborne platform without relying on GPS.

Vibration Response Analysis of a Tethered Unmanned Aerial Vehicle System under Transient Wind Field

The transient wind is one of the dangerous conditions encountered by tethered UAVs operating at the ocean, making the cable and the UAV generate complex nonlinear vibration responses threatening normal operation, even safe flight. There is a lack of research on the nonlinear vibration of the tethered UAV in a transient wind field environment. This study analyzes the vibration response of a tethered quadrotor UAV under a transient wind field, which helps to boost the tethered UAV applications, providing engineering suggestions to design these systems. Considering both cable and UAV motions, the coupled dynamic equations of the tethered UAV system are established based on the Hamilton principle. The variation law of the cable vibration amplitudes with wind field and position is analyzed through a one-minus-cosine gust profile to describe the transient wind field, which can evaluate the dynamic behavior of systems in the actual flight. Moreover, the positions of maximum vibration amplitude in tangential and normal directions are found to be approximately 9/10 and 9/20 from the lower end of the cable. Furthermore, the tethered UAV system vibrations are investigated under different structural parameters. The results indicate that the UAV can maintain a stable single-period motion by increasing the length or elastic modulus or selecting the appropriate diameters of the cable. Finally, an experiment is implemented on the vibration response of the tethered UAV system in a wind tunnel. The theoretical result is compatible with the experimental one, demonstrating the theoretical method’s accuracy.

Integrating Unmanned Aerial Vehicle-Derived Vegetation and Texture Indices for the Estimation of Leaf Nitrogen Concentration in Drip-Irrigated Cotton under Reduced Nitrogen Treatment and Different Plant Densities

The accurate assessment of nitrogen (N) status is important for N management and yield improvement. The N status in plants is affected by plant densities and N application rates, while the methods for assessing the N status in drip-irrigated cotton under reduced nitrogen treatment and different plant densities are lacking. Therefore, this study was conducted with four different N treatments (195.5, 299, 402.5, and 506 kg N ha−1) and three sowing densities (6.9 × 104, 13.8 × 104, and 24 × 104 plants ha−1) by using a low-cost Unmanned Aerial Vehicle (UAV) system to acquire RGB imagery at a 10 m flight altitude at cotton main growth stages. We evaluated the performance of different ground resolutions (1.3, 2.6, 5.2, 10.4, 20.8, 41.6, 83.2, and 166.4 cm) for image textures, vegetation indices (VIs), and their combination for leaf N concentration (LNC) estimation using four regression methods (stepwise multiple linear regression, SMLR; support vector regression, SVR; extreme learning machine, ELM; random forest, RF). The results showed that combining VIs (ExGR, GRVI, GBRI, GRRI, MGRVI, RGBVI) and textures (VAR, HOM, CON, DIS) yielded higher estimation accuracy than using either alone. Specifically, the RF regression models had a higher accuracy and stability than SMLR and the other two machine learning algorithms. The best accuracy (R2 = 0.87, RMSE = 3.14 g kg−1, rRMSE = 7.00%) was obtained when RF was applied in combination with VIs and texture. Thus, the combination of VIs and textures from UAV images using RF could improve the estimation accuracy of drip-irrigated cotton LNC and may have a potential contribution in the rapid and non-destructive nutrition monitoring and diagnosis of other crops or other growth parameters.

High-Resolution Remote Sensing Image Object Detection System for Small Unmanned Aerial Vehicles Based on MPSOC

High-Resolution Remote Sensing Image Object Detection System for Small Unmanned Aerial Vehicles Based on MPSOC