ALGA-DenseNet ground-based cloud classification network based on multi-scale features

Objective: The aim of the article is to present the state of the art on threats to UAVs posed by sudden changes of weather conditions, involving in particular storms and hail. Identifying such threats is likely to facilitate the development of hardware and software solutions aimed at improving the safety of the UAV missions. Introduction: Severe weather phenomena strongly affect the safety of unmanned aerial vehicle (UAV) missions. The article describes in detail the implementation concept of atmospheric discharge and precipitation images using wireless Internet connections for an exemplary mobile application for multi-rotor flight control. The concept was developed to reduce risks associated with flights in storm and precipitation areas, and to maximise the performance of electric multi-rotor UAVs with a maximum take-off mass of no more than 25 kg. This is dealt with in the introductory section and in three substantive sections. UAV capabilities are discussed in the first section, while the second one analyses specific weather conditions which merit particular attention when constructing new UAVs, and the third one outlines some of the solutions employed in UAV steering systems control apps. A number of concluding remarks are presented in the end. Conclusions: The analysis of usefulness has proven electric UAVs suitability for professional missions. However, these vehicles display a number of limitations, one of the most important being their high sensitivity to weather conditions. With no hermetically sealed hatch, on-board equipment is exposed to penetrating damp caused by various types of precipitation, including drizzle, rain and snow. These can cause a short-circuit and equipment damage, while hailstones can result in UAV mechanical damage. Severe turbulences and strong ascending or descending air currents, which are likely to occur during low-altitude flights, pose a threat of UAV's collision with overground objects. Using more resistant composite materials in the construction of hermetic UAVs could increase the application range of these vehicles. The wide range of possible uses of UAVs encourages further investigations aimed at ensuring their best possible working conditions.

The proliferation of unmanned aerial vehicles (UAVs) has driven the emergence of urban air mobility (UAM) as a solution to alleviate the increasing demand for UAVs applications in various domains. The concept of UAM envisions a future where UAVs play a pivotal role in short-distance transportation, delivery services, surveillance, emergency rescue, and more. However, the rapid growth of UAVs poses significant challenges arising from the need to ensure the safe coexistence of UAVs with other manned/unmanned aerial vehicles. To ensure the safe integration of UAVs into low-altitude urban airspace and support high-density on-demand mobility, this research focuses on handling the safety-centric challenges related to UAV collision risk assessment, obstacle avoidance, and conflict resolution in complex urban environments.
\nOne of the main challenges is the intrusion of UAVs into airport runways. Airports serve as critical hubs for civil aircraft takeoff and landing operations. However, the increasing presence of UAVs has raised concerns about intruding into airports, leading to flight delays and temporary runway closures. To quantify the potential interference of UAVs on civil aircraft, a collision-course based trajectory planning model is proposed to evaluate the collision risk between manned civil aircraft and intruding UAVs. In this risk assessment model, the trajectory of the intruding UAV is formulated using a collision-course concept, taking into account the limited trajectory information available about intruding UAVs. The planned trajectory of the UAVs is then used to predict its relative position concerning civil aircraft. To account for the possible disturbances of the relative distance, a stochastic position prediction model based on Brownian motion is developed. The collision risk is assessed based on this stochastic kinematic model, providing a quantitative measure of the potential collisions. The proposed model is validated through extensive simulations involving different UAV initial positions, position updates, and different sizes of collision zone tests, as well as comparative tests with the Monte-Carlo algorithm, demonstrating its robustness in assessing the collision risk of the airport restricted area. To facilitate the application, the model is finally used to develop a platform to generate runway heatmaps, accounting for airport runway risk distributions. 
\nAnother safety-centric challenge is ensuring autonomous obstacle avoidance and conflict resolution of UAVs. To enable UAV autonomous safe operations in uncontrolled urban low-altitude airspace, a comprehensive collision-free path planning framework is developed to address the avoidance of both static obstacles and dynamic threats. Firstly, to handle static obstacles, a novel algorithm of 3D voxel jump point research is introduced to generate a global reference path for UAV navigation to avoid static obstacles, particularly for a free flight at different altitudes. Additionally, to optimize the reference path for specific mission requirements, techniques such as trajectory de-diagonalization, reconstruction, and smoothness are proposed to reduce the risk of static collisions, shorten the fly range, and enable UAVs to negotiate turns more efficiently. Secondly, real-time conflict resolution actions based on the Markov decision process are developed to avoid local dynamic threats encountered along the optimized desired path. This allows the host UAV to dynamically adjust its course to a resolution trajectory while maintaining its planned speed without hovering to avoid the potential collision with the approaching intruders. By avoiding near mid-air collisions (NMAC) and preventing the UAV from hovering, the proposed method ensures the timely completion of UAV missions. Simulation results demonstrate that the developed method successfully achieves UAV autonomous global path planning and local real-time conflict resolution in complex urban low-altitude environments, thereby avoiding both static obstacles and dynamic threats. 
\nMoreover, to bridge the research gap of UAV hard conflict resolution in mitigating NMAC risks arising from local dynamic threats, a novel situational NMAC risk assessment approach is developed by integrating the conditional random field (CRF) algorithm with safety metrics and conflict resolution performance to facilitate UAV tiered aerial passage operations. To improve UAV operating environments in urban airspace, a concept of tiered aerial passages is first designed to provide a practical urban airspace planning scheme that addresses UAV heterogeneity and optimizes the utilization of urban airspace. For UAV tiered aerial passage operations, a situational NMAC risk assessment module coupled with a conflict resolution decision-making module is formulated, enabling the host UAV to evaluate and respond to real-time conflicts, ensuring avoidance of potential collisions. To assess NMAC risks, a general risk assessment model is proposed based on the CRF algorithm, considering UAV safety metrics and resolution performance. Then the overtaking, approaching head-on, and converging UAV encounters are analyzed, and situational risk potential scores are created using the Gaussian kernel function in the CRF algorithm, considering advisory, caution, and warning risk alert levels. To resolve conflict, acceptable, tolerable, and unacceptable scenarios are proposed to enhance the flexibility and personalization of the resolution decision-making. The proposed scheme is validated by simulating geometric encounter situations and comparative tests with the previous hard resolution technique. The results indicate that the proposed method outperforms UAV NMAC risk assessment and conflict resolution. Even in the most dangerous unacceptable resolution decision-making scenarios across three conflict encounters, the proposed algorithm achieves NMAC rates of 0.036, 0.070, and 0.043, which are lower than the corresponding NMAC rates associated with the hard resolution approach. Together with the acceptable and tolerable scenarios, the proposed negotiation resolution effectively tailors different risk requirements to accommodate diverse UAV missions. 
\nWith a primary focus on ensuring the safety of the UAM architecture, this research endeavors to assess UAV NMAC risks and efficiently manage potential conflicts. The proposed work are thoroughly validated to demonstrate their effectiveness in real-world scenarios. As a result, this study significantly contributes to the establishment of a robust UAM architecture, facilitating the realization of safe and reliable UAM.

Effective conflict resolution is crucial to ensure the safety of unmanned aerial vehicles (UAVs) in increasingly congested low-altitude airspace. However, the inefficiency in representing large-scale UAV information hinders the performance of existing learning-based methods. To overcome this challenge, we propose an enhanced graph-based reinforcement learning (GRL) approach that models multi-agent interactions relationships. Specifically, a novel multi-scale graph reinforcement learning (MS-GRL) approach is utilized to learn UAV avoidance strategies in a dense environment. MS-GRL utilizes the time-evolving intensity of local conflicts to evaluate the global attention weights for UAVs in conflict, while aggregating UAV observation data through graph embedding and requisite feature refinement. In addition, to adaptively limit the safety region of the action space while minimize deviations from the original trajectory, a safetyconstrained maneuver strategy is proposed. Experimental results demonstrate that MS-GRL outperforms state-of-the-art GRL methods when there are up to 100 UAVs and multiple obstacles.

Conflict Detection and Resolution (CD&R) for Unmanned Aerial Vehicles (UAVs) has become important consideration with remarkable development of sensor technology and autonomous control system. Since CD&R deals with safety of UAVs and aircraft, it has to be validated by rigorous analytical verification. This paper investigates the application of differential geometry to UAV CD&R algorithm for non-cooperating intruders such as static and dynamic obstacles. Two-dimensional geometry is used to detect a single conflict and multiple conflicts, and find the condition for resolution guidance. The existence and stability of the conflict resolution, which controls either heading angle only or heading angle and ground speed simultaneously, are then analysed. Whilst there exist region which the UAV can not resolve due to its physical and operational limitations as every CD&R algorithms, the resolution guidance can find the region and prove local stability using Lyapunov theory. The analysis and resolution guidance design do not rely on local linearisation and can be shown that the derived conflict resolution is equivalent concepts to Proportional Navigation Guidance (PNG) and Traffic alert and Collision Avoidance Systems (TCAS). The paper finishes with numerical simulation in two dimensions, illustrating the performance and properties of the proposed CD&R algorithm.

Aerial target detection and avoidance is vital for the flight safety of unmanned aerial vehicles (UAVs). The limited amount of feature points for small and mobile obstacles make them very difficult to be detected. In this paper, the background segmentation and small target detection algorithm is applied in the obstacle detection, and the obstacle localization is achieved by the triangulation method, after which, the threat of collision is evaluated using the safety envelope method. Based on the evaluation results, a UAV can avoid the obstacles by the target point avoidance method. Experiments using a six rotor equipped with a stereo vision system have been implemented to testify the method effectiveness and the results show that the UAV can detect and avoid obstacles effectively.

The article reveals the peculiarities of using unmanned aerial vehicles for reconnaissance in the field in wartime. The article reveals the essence and content of the use of unmanned aerial vehicles for reconnaissance, as well as the conditions and features of their effective use. Study of the possibilities of using unmanned to conduct reconnaissance on the ground in wartime conditions has shown that this technology has great potential to improve operational intelligence and ensuring the safety of military personnel. The advantages of using unmanned aerial vehicles for reconnaissance are obvious. They can quickly and efficiently penetrate enemy territory, collect information territory, collect information and transmit it in real time. This makes it possible to Reduce the risk to the lives of servicemen, as they do not need to go to the front line the front line to conduct reconnaissance. In addition, unmanned aerial vehicles can operate in hazardous environments, such as radioactive contamination or chemicals where humans cannot enter. The use of unmanned aerial vehicles also ensures greater accuracy and objectivity in collecting information. They are equipped with various types of sensors, such as high-resolution cameras, thermal cameras, radars, etc., which provide detailed information about the territory, enemy positions and movements. This significantly improves the quality of intelligence and allows for more informed decisions on further actions. In summary, research has shown that the use of unmanned aerial vehicles for reconnaissance in wartime has significant potential. They can collect information quickly, efficiently and safely, improving operational intelligence and ensuring the safety of soldiers. However, further research and development of new technologies is needed to improve the functionality and safety of unmanned aerial vehicles in warfare.

Conflict detection is an essential issue in flying ad hoc networks (FANETs) to ensure the safety of unmanned aerial vehicles (UAVs) during flights. This paper assesses the applicability and utilization of a conflict detection algorithm that sees immediate trajectory as a straight line for short periods with nonlinear mobility models such as Gauss–Markov (GM). First, we use a straight line conflict detection algorithm with two nonlinear mobility models. Then, we perform an extensive simulation study to evaluate the performance. Additionally, we present a comprehensive discussion to tune the collision detection parameters efficiently. Simulation results indicate that an algorithm considering the immediate trajectory as a straight line to predict conflicts between UAVs can be applied with nonlinear mobilities and can provide an acceptable performance measured in false and missed alarms.

Currently, the problem of ensuring safety of flights of unmanned aerial vehicles (UAVs) over the territory of transport infrastructure facilities (TIF), primarily airports, remains topical.In one of the previous works, the author together with the co-author proposed a method for increasing safety of movement of unmanned aerial vehicles and the system for controlling the routes of unmanned aerial vehicles (hereinafter – route control system, RCS) that implements it, which makes it possible to improve safety and security of UAV traffic at transport infrastructure facilities by limiting UAV traffic area strictly to a dedicated air corridor (DAC). The development of this system creates the prerequisites for removing the existing restrictions on the use of unmanned aerial vehicles at transport infrastructure facilities.For practical implementation of the proposed system, it is relevant to develop a method of placing RCS at transport infrastructure facilities. This condition can be justified by the fact that RCS, as a rule, will be located under conditions of dense infrastructural development, including dangerous technical elements of technical equipment, a collision of a UAV with which can lead to an emergency situation (ES); besides, the movement of air/ground vehicles will be carried out in the immediate vicinity of RCS along transport routes/corridors, and employees, passengers and visitors of TIF will move along the pedestrian paths.The objective of this study – to develop a methodology for placing systems controlling routes of unmanned aerial vehicles at transport infrastructure facilities.The study conducted with well-known scientific methods, including the basic routing problem posed by Dantzig and Ramser, modelling, analysis, and synthesis, made it possible to develop a method for placing RCS for unmanned aerial vehicles at transport infrastructure facilities. The practical application of the proposed methodology makes it possible to build routes for movement of UAV at TIF, to form a network of dedicated air corridors for UAV at TIF operated in relationship with the system, determine the optimal location of the RCS elements at TIF.

Purpose The rapid advancement of unmanned aerial vehicles (UAVs) presents significant opportunities for the architecture, engineering and construction (AEC) industry. However, despite their potential, UAV applications face numerous barriers, including technical, regulatory and safety challenges, which hinder their widespread adoption. While existing research highlights these challenges, it provides limited guidance on comprehensive strategies to address them, which in turn may hinder the wider adoption of UAVs. This study seeks to fill this gap by identifying the multi-dimensional challenges associated with UAVs in AEC and developing a strategic roadmap that offers actionable insights for stakeholders, facilitating safer and more efficient UAV integration. Design/methodology/approach The 24 interviews were conducted to identify the challenges and potential strategies of UAV applications in the AEC industry. Thematic analysis was used to analyze the collected text contents, qualitative analysis was used to construct a conceptual strategy roadmap and focus group meetings were used to verify the roadmap. Findings The primary technical challenges include short battery life, loss of global positioning system (GPS)/compass and UAV magnetic interference. The critical safety risks are collisions, falls and absence of safety awareness among remote pilots and workers. Additionally, some practitioners struggled to meet operational requirements while maintaining the productivity of AEC flight operations. Furthermore, existing remote pilot training programs are generic and lack trade-specific training content. Research limitations/implications Despite the valuable benefits and contributions of this study, it is essential to acknowledge certain limitations encountered, which are outlined below. It is essential to note that the respondents in this study primarily came from Hong Kong, and the challenges may vary in different countries or regions. However, it is worth mentioning that the study interviewed specialists with international project experience who provided their insights from a global perspective. Practical implications This study contributes to a better understanding of the multi-dimensional challenges and strategies of UAV adoption in the AEC industry. The strategic roadmap will guide stakeholders to take proper actions for the safe integration of UAVs into the AEC industry. Originality/value This empirical study contributes to understanding stakeholders’ needs and engagement with regard to the new technology adoption. It also proposes a novel strategic roadmap for addressing the challenges through collaborative efforts among all relevant stakeholders, promoting safe and efficient UAV uses in the AEC sector.

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.

Generalizing out-of-distribution (OoD) is critical but challenging in real applications such as unmanned aerial vehicle (UAV) flight control. Previous machine learning-based control has shown promise in dealing with complex real-world environments but suffers huge performance degradation facing OoD scenarios, posing risks to the stability and safety of UAVs. In this paper, we found that the introduced random noises during training surprisingly yield theoretically guaranteed performances via a proposed functional optimization framework. More encouragingly, this framework does not involve common Lyapunov assumptions used in this field, making it more widely applicable. With this framework, the upperbound for control error is induced. We also proved that the induced random noises can lead to lower OoD control errors. Based on our theoretical analysis, we further propose OoD-Control to generalize control in unseen environments. Numerical experiments demonstrate the superiority of the proposed algorithm, surpassing previous state-of-the-art by 65% under challenging unseen environments. We further extend to outdoor real-world experiments and found that the control error is reduced by 50% approximately.

Pineapple is one of the potential commodities in Indonesia. This is due to high market demand, potential suitable land in Indonesia and public awareness of fruit supply. The main factor of crop failure in pineapple plants is the delay in handling pineapple plant diseases. To identify in image processing requires class grouping. Self-Organizing Map (SOM) which divides the input pattern into several groups so that the network output is in the form of the group that is most similar to the given input. However, the SOM algorithm requires data input that characterizes an object to facilitate the identification process. So, in this study the SOM algorithm was improved through color feature extraction with parameters Red, Green, Blue, Hue, Saturation and Value, as well as texture feature extraction with parameters contrast, correlation, energy, and homogeneity in the Gray Level Co-occurrence Matrix (GLCM). Based on the results of tests carried out by the SOM algorithm with color and texture feature extraction parameters, it is able to assist in increasing the accuracy value. The results of testing the SOM model with color and texture feature extraction obtained a precision value of 93.33%, recall of 92.31% and accuracy of 92.78%.

The increase in the number of Unmanned Aerial Vehicles (UAVs) for liquid transport tasks, such as agricultural spraying, results in significant stability challenges. The free movement of the liquid, known as sloshing, generates unpredictable forces that destabilize the vehicle and increase collision risks. This study treats this problem by developing and validating a nonlinear control strategy to ensure precise trajectory tracking while actively suppressing liquid sloshing. The coupled dynamics of the system are modeled using the Euler–Lagrange formalism by representing the UAV as a planar vertical take-off and landing (PVTOL) aircraft and the liquid sloshing dynamics as an equivalent pendulum model. The stability of the entire closed-loop system is proven using Lyapunov’s direct method. The analytical results are validated through numerical simulations in MATLAB/Simulink, which demonstrate excellent tracking of desired altitude and horizontal trajectories. Crucially, the simulations confirm that the controller effectively attenuates the sloshing oscillations, offering a robust solution to enhance the safety and operational performance of UAVs in liquid transport applications.

Unmanned aerial vehicles (UAVs) are forecast to be widely used in the military and civilian domains. The remaining flying time is a critical parameter to monitor during a flight to ensure the safety of electric UAVs (e-UAVs). However, accurate remaining flying time prediction under different load conditions requires a large amount of data and is computationally expensive for online applications. To address these issues, a deep learning approach based on temporal convolutional networks and transfer learning is developed for lithium-ion battery systems for e-UAVs. A temporal convolutional network is used to extract features from monitoring data and predict the remaining flying time of flights under one load condition. A layer transfer strategy is then used to transfer the knowledge learned from one load condition to another load condition. Battery health monitoring data collected from a fixed-wing e-UAV are used to demonstrate the effectiveness of the proposed method. Experimental results show that the proposed temporal convolutional network with the transfer learning strategy can predict the remaining flying time of the e-UAV under two load conditions more efficiently and accurately than a temporal convolutional network without transfer learning.

Most current infrared small target detection methods attempt to fuse local and global information by using single-scale inputs and creating a multi-scale feature pyramid during network feeding forwards. Further to this, our research finds that using high-resolution inputs can improve recall, while low-resolution inputs improve precision. Nevertheless, solely focusing on global or local information can result in missing target and false alarm. To address these issues, we propose the BPR-Net to balance precision and recall via a novel multi-scale attention mechanism, which combines semantic and shallow features of multi-scale inputs. We first scale the input image into multiple images with varying resolutions and feed them into the network. In the encoder, Scale Fusion Module (SFM) fuses features from corresponding images of different resolutions. In the decoder, a Channel Fusion Module (CFM) fuses useful information from multiple channels. Furthermore, a Wavelet Transform cross-layer skip Layer (WTL) is employed to enhance the interaction between decoder layers for more effective multi-scale feature fusion. Experimental results demonstrate that our approach achieves a balance between recall and precision and yields state-of-the-art performance on challenging benchmarks including Sirst, MDvsFA, and SIATD. Notably, our approach achieves an F1 score of 0.9409 on the challenging benchmark SIATD, surpassing the state-of-the-art method by 16.7%.