Reliability of Unmanned Aerial Vehicles in the Context of Selected Factors

The phased-mission reliability of unmanned aerial vehicle (UAV) swarm refers to its capability to successfully complete the missions of each phase under specified conditions for a specified period. In order to study the reliability of phased-mission in UAV swarm, this paper firstly studies the reliability of a single UAV under fault coverage. Then, considering the mission characteristics of UAV swarm, the consecutive k-out-of-n system is studied to model and predict the reliability of UAV swarm phase mission. Some importance measures are introduced to analyze the influence of UAV in different positions on the reliability of the whole system. Finally, numerical examples of linear and circular UAV swarms are given to demonstrate and verify the correctness of the model. The reliability modeling established in this paper can predict the phased-mission reliability of UAV swarm scientifically.

The unmanned aerial vehicle (UAV) has been developing rapidly recently, and the safety and the reliability of the UAV are significant to the mission execution and the life of UAV. Sensor and actuator failures of a UAV are one of the most common malfunctions, threating the safety and life of the UAV. Fault-tolerant control technology is an effective method to improve the reliability and safety of UAV, which also contributes to vehicle health management (VHM). This paper deals with the sliding mode fault-tolerant control of the UAV, considering the failures of sensor and actuator. Firstly, a terminal sliding surface is designed to ensure the state of the system on the sliding mode surface throughout the control process based on the simplified coupling dynamic model. Then, the sliding mode control (SMC) method combined with the RBF neural network algorithm is used to design the parameters of the sliding mode controller, and with this, the efficiency of the design process is improved and system chattering is minimized. Finally, the Simulink simulations are carried out using a fault tolerance controller under the conditions where accelerometer sensor, gyroscope sensor or actuator failures is assumed. The results show that the proposed control strategy is quite an effective method for the control of UAVs with accelerometer sensor, gyroscope sensor or actuator failures.

Evaluating the risk effectively is critical for the security and reliability of unmanned aerial vehicles (UAVs). With the improvement of related technologies, more and more condition monitoring (CM) parameters are collected from UAVs, which contains considerable information related to the condition risk. For the powerful capability to analyze these massive CM data, a data-driven fuzzy comprehensive evaluation method is proposed in this paper, which employs the feature engineering and the variable weight coefficients to achieve the accurate and timely condition risk assessment for UAVs. Given the CM data, the feature engineering is utilized to adaptively represent its historical normal status and provide the quantitative risk indications accurately reflecting its real-time risk. According to the real-time quantitative risk indications, the variable weight coefficients is utilized to dynamically adjust the initial weights of evaluating indices, which allows us to timely capture the slight condition risk of UAVs under the early abnormal status. At last, the risk membership vector of UAVs is obtained through the comprehensive evaluation to support the related decision-making. A case study using the real CM data of a UAV shows that the evaluation results provided by our proposed method are reasonable, comprehensive and interpretable.

This paper presents a method to increase the reliability of Unmanned Aerial Vehicle (UAV) sensor Fault Detection and Identification (FDI) in a multi-UAV context. Differential Global Positioning System (DGPS) and inertial sensors are used for sensor FDI in each UAV. The method uses additional position estimations that augment individual UAV FDI system. These additional estimations are obtained using images from the same planar scene taken from two different UAVs. Since accuracy and noise level of the estimation depends on several factors, dynamic replanning of the multi-UAV team can be used to obtain a better estimation in case of faults caused by slow growing errors of absolute position estimation that cannot be detected by using local FDI in the UAVs. Experimental results with data from two real UAVs are also presented.

The flying reliability of Unmanned Aerial Vehicles (UAVs) and flying robots, which directly determines the operational degree of safety, is becoming more important in recent intelligent decades. Reliability and a high level of safety are critical for autonomously controlled flying robots, especially in transportation and entertainment applications. Subsequently, monitoring UAV health is crucial and essential for system safety, cost savings, and excellent dependability. The development of numerous monitoring strategies has resulted from the requirement for a simple and accurate unbalance classification procedure. This paper provides an Unbalance Classification and Isolation (UCI) system for multirotor UAV propeller impairments. The technique is based on the processing of signal vectors from a vibration sensor positioned in the lines of the intersection of a modern-day drone's four propulsion units, which supply data for the Fast Fourier Transform (FFT) feature extraction. To identify and locate broken blades, characteristic fault signatures collected from vibration signals are employed and displayed in real-time on the programming platform. A noticeable maximum frequency shifting percentage value of 4.2% is acquired when deviating from a healthy state. The results reveal that identifying and isolating defective rotor states has high sensitivity and outperforms current studies in regard to unbalance classification of UAVs. The adopted technique is an efficient and low-cost solution that can be implemented in any multirotor UAV.

In the last decade, Unmanned Aerial Vehicles (UAVs) were applied in monitoring studies addressing to marine and terrestrial wildlife for their multiple advantages. Due to their operational flexibility and low cost, UAVs have been applied in studies aimed at studying behavior, estimating group size and abundance of species as well as at measuring the individuals through photogrammetry methods. In this work, numerical estimates of group size of Risso’s dolphin sighted in the Gulf of Taranto from UAVs video-analysis were compared from those recorded by MMO on board of a research vessel. Data were collected from April 2018 to September 2020 for a total of 109:58 minutes of recording. Aerial shots together traditional survey methods allowed a more thorough analysis of the number of individuals observed, offering a better estimates overview, with the possibility of viewing videos several times and taking screenshots. Using UAVs could provide more accurate estimates on Risso’s dolphin population. In conclusion, given the efficiency such systems found in this work, the use of UAV could be instrumental in broadening perspectives on marine mammal studies.

With the increasing demand for unmanned aerial vehicles (UAVs) in both military and civilian applications, critical safety issues need to be specially considered in order to make better and wider use of them. UAVs are usually employed to work in hazardous and complex environments, which may seriously threaten the safety and reliability of UAVs. Therefore, the safety and reliability of UAVs are becoming imperative for development of advanced intelligent control systems. The key challenge now is the lack of fully autonomous and reliable control techniques in face of different operation conditions and sophisticated environments. Further development of unmanned aerial vehicle (UAV) control systems is required to be reliable in the presence of system component faults and to be insensitive to model uncertainties and external environmental disturbances. This thesis research aims to design and develop novel control schemes for UAVs with consideration of all the factors that may threaten their safety and reliability. A novel adaptive sliding mode control (SMC) strategy is proposed to accommodate model uncertainties and actuator faults for an unmanned quadrotor helicopter. Compared with the existing adaptive SMC strategies in the literature, the proposed adaptive scheme can tolerate larger actuator faults without stimulating control chattering due to the use of adaptation parameters in both continuous and discontinuous control parts. Furthermore, a fuzzy logic-based boundary layer and a nonlinear disturbance observer are synthesized to further improve the capability of the designed control scheme for tolerating model uncertainties, actuator faults, and unknown external disturbances while preventing overestimation of the adaptive control parameters and suppressing the control chattering effect. Then, a cost-effective fault estimation scheme with a parallel bank of recurrent neural networks (RNNs) is proposed to accurately estimate actuator fault magnitude and an active fault-tolerant control (FTC) framework is established for a closed-loop quadrotor helicopter system. Finally, a reconfigurable control allocation approach is combined with adaptive SMC to achieve the capability of tolerating complete actuator failures with application to a modified octorotor helicopter. The significance of this proposed control scheme is that the stability of the closed-loop system is theoretically guaranteed in the presence of both single and simultaneous actuator faults.

Over the past years, the autonomy and reliability of Unmanned Aerial Vehicles (UAVs) has been improved significantly. It is generally acknowledged that their eventual utility in a battlefield or commercial environment will require multiple vehicles cooperating to achieve specific mission objectives. This paper introduces a differential game approach to control a swarm of UAVs. The approach decomposes the complete problem of controlling the entire swarm of UAVs simultaneously into a collection of smaller differential game problems that are easier to solve. Based on the solution to the individual games, a planner assigns tasks for each U A V to perform taking advantage of the vehicles’ current positions and their capabilities. A simple example is used to demonstrate the effectiveness and speed of this approach while some implementation issues are also discussed.

The growing demand and interest in the use of Unmanned Aerial Vehicles (UAVs) for various applications has highlighted the need to develop more robust structural monitoring systems, particularly for aircraft constructed with composite materials. Although these materials offer advantages in terms of lightness and strength, they are susceptible to delaminations and microcracks, which can compromise the safety and operational efficiency of UAVs. Early detection and characterization of these defects are key to preventive maintenance strategies and structural design optimization. This study presents a structural monitoring architecture based on Fiber Bragg Grating (FBG) sensors, combined with signal processing methods and computational intelligence, to evaluate the structural integrity of UAV wings. Luna Innovations’ os1200 and os3200 sensors were selected for their high sensitivity, immunity to electromagnetic interference, and multiplexing capability on a single fiber. The os1200 sensors are positioned in critical areas of the wing profile, allowing for the mapping of stress distribution in high-tension regions, while the os3200 sensors are placed in hard-to-reach areas where the use of metallic sensors is not feasible. This arrangement facilitates detailed data acquisition on the strain distribution across the UAV structure. For data analysis, a methodology based on signal processing and machine learning was employed. Filtering and conditioning techniques were applied to reduce noise, followed by Fourier and wavelet transforms, which enabled the identification of subtle changes in the structural response typically associated with the presence of faults. Additionally, artificial neural networks and machine learning algorithms were implemented for defect classification and severity assessment, leveraging patterns extracted from the sensor signals. Hybrid models combining wavelet transforms with supervised learning were explored, optimizing the detection and prediction of structural damage. The initial validation of the architecture was conducted in a controlled laboratory environment, using UAV wing profile prototypes subjected to static and dynamic load tests to induce different types of failures. FBG sensor measurements were correlated with visual inspections and non-destructive evaluation (NDE) techniques. The results obtained are expected to lay the groundwork for the development of a real-time structural monitoring system that enhances the safety and reliability of UAVs, reduces the risk of catastrophic failures, and provides a key tool for intelligent maintenance management.

This paper proposes a fault-tolerant formation algorithm based on the improved artificial potential field method to improve the mission reliability of Unmanned Aerial Vehicle (UAV) reconnaissance formation system, which includes task reassignment, formation transformation and anti-collision algorithm. In the case of single UAV fault, a target point value algorithm based on weight allocation is used to reallocate tasks. Then, the target position test function is established to determine whether the current position is the target position or not, so as to prevent the algorithm from falling into local optimum. Considering that the gravitation is extremely large in the distance, which leads to the problem of excessive acceleration in the traditional artificial potential field method, the gravitational function is optimized. The concept of charge radius of UAV is introduced to expand the charging range of UAV to a safe distance, which will prevent collision in formation transformation process. Finally, a simulation experiment is carried out to verify the feasibility of the algorithm, which shows that the algorithm has the function of multi-UAV online planning simultaneously.

Monitoring lemon production requires appropriate and efficient technology. The use of UAVs can addressed these challenges. The purpose of this study was to determine the best vegetation indices (VIs) for estimating chlorophyll content, plant height (PH), canopy area (CA), and fruit total numberas (FTN). CCM 200 was used as a tool to measure the chlorophyll content index (CCI), the number of fruits was measured by hand-counter, and other variables were recorded in meters. The UAV used was a Phantom 4 with a multispectral camera capable of capturing five different bands. The VIs was obtained via analysis of digital numbers generated by the multispectral camera. Then, the VIs was correlated with the CCI, PH, CA and FTN. VIs tested included the following: the normalized difference vegetation index (NDVI), the normalized difference vegetation index-green (NDVIg), the normalized different index (NDI), green minus red (GMR), simple ratio (SR), the Visible Atmospherically Resistant Index (VARI), normalized difference red edge (NDRE), simple ratio red-edge (SR<sub>RE</sub>), the simple ratio vegetation index (SR<sub>VI</sub>), and the Canopy Chlorophyll Content Index (CCCI). The best model for predicting CCI was obtained using the NDVIg (R<sup>2</sup>=0.8480; RMSE=6.1665 and RRMSE=0.0908). Meanwhile, SR turned out to be the best model for predicting PH (R<sup>2</sup>=0.8266; RMSE=15.6432 and RRMSE=0.0883), CA (R<sup>2</sup>=0.6886; RMSE= 0.8826 and RRMSE=0.1907), and FTN (R<sup>2</sup>=0.6850; RMSE=24.5574 and RRMSE=0.3503). The implication of these results for future activities includes establishing early monitoring and evaluation systems for lemon yield and production. This model was developed and tested in this specific location and under these environmental conditions.

In order to verify the real-time reliability of unmanned aerial vehicle (UAV) flight control system and comply with the airworthiness certification standard, we proposed a model-based integration framework for modeling and verification of time property. Combining with the advantages of MARTE, this framework uses class diagram to create the static model of software system, and utilizes state chart to create the dynamic model. In term of the defined transformation rules, the MARTE model could be transformed to formal integrated model, and the different part of the model could also be verified by using existing formal tools. For the real-time specifications of software system, we also proposed a generating algorithm for temporal logic formula, which could automatically extract real-time property from time-sensitive live sequence chart (TLSC). Finally, we modeled the simplified flight control system of UAV to check its real-time property. The results showed that the framework could be used to create the system model, as well as precisely analyze and verify the real-time reliability of UAV flight control system.

The accuracy and reliability of unmanned aerial vehicle (UAV) visual positioning systems are dependent on the performance of multi-source image matching algorithms. Despite many advancements, targeted performance evaluation frameworks and datasets for UAV positioning are still lacking. Moreover, existing consistency verification methods such as Random Sample Consensus (RANSAC) often fail to entirely eliminate mismatches, affecting the precision and stability of the matching process. The contributions of this research include the following: (1) the development of a benchmarking framework accompanied by a large evaluation dataset for assessing the efficacy of multi-source image matching algorithms; (2) the results of this benchmarking framework indicate that combinations of multiple algorithms significantly enhance the Match Success Rate (MSR); (3) the introduction of a novel Geographic Geometric Consistency (GGC) method that effectively identifies mismatches within RANSAC results and accommodates rotational and scale variations; and (4) the implementation of a distance threshold iteration (DTI) method that, according to experimental results, achieves an 87.29% MSR with a Root Mean Square Error (RMSE) of 1.11 m (2.22 pixels) while maintaining runtime at only 1.52 times that of a single execution, thus optimizing the trade-off between MSR, accuracy, and efficiency. Furthermore, when compared with existing studies on UAV positioning, the multi-source image matching algorithms demonstrated a sub-meter positioning error, significantly outperforming the comparative method. These advancements are poised to enhance the application of advanced multi-source image matching technologies in UAV visual positioning.

In order to improve the effectiveness and reliability of unmanned aerial vehicles (UAVs) automatic navigation and inspection for large photovoltaic (PV) power plants, a strategy of navigation and inspection based on infrared vision was proposed, according to the arrangement characteristics of PV strings. In order to implement the strategy, a PV strings recognition and localization algorithm combining color features and shape features was proposed to obtain information that can be used for navigation. At the same time, a navigation strategy based on the location of PV strings was presented for PV strings inspection. Experiments clearly demonstrated the adaptability and real-time of the proposed method, which makes a contribution to the automatic navigation and inspection for large PV power plants.

Unmanned aerial vehicles (UAVs) are increasingly and boldly used by various services, including transport services. This paper presents examples of actual and potential usage of UAVs by the fire services. Key technical parameters are defined and their measurement methodology for ensuring a high level of reliability is presented. The publication compares the obtained results with the technical parameters declared by various manufacturers. The conducted analyses of scientific literature prove that there is a research gap in the area of research on the reliability of the UAVs used in rescue and fire-fighting missions. The researchers' interest mostly focuses on the practical and technological aspects of using UAVs to prevent and respond to events such as local fires, disasters or other local threats, and more specifically, to search and rescue, provide air reconnaissance or diagnose aerial situations or inspect damaged building structures, or for other engineering inspections.