Safety Of Unmanned Aerial Vehicles Research Articles

Safe Maneuvering Planning for Flights in Complex Environments

Safe trajectory planning of unmanned aerial vehicles (UAVs) has drawn significant attention. For avoiding obstacles, the two prevailing methods of distance penalty and convex polyhedra confinement ignore the flight velocity, aggressive maneuvering increases the risk of collision. Other approaches ensure safety by limiting the flight speed, leading to conservative and slow trajectories. This paper presents a motion planning method that ensures flexible maneuverability and a high level of UAV safety in unknown complex environments. Firstly, an improved path planning method considering safety and search efficiency is proposed to generate discrete primitives. By representing the confident flight area with ball-shaped areas expected no obstacles included, the safe region constraint is designed and incorporated into the first-stage optimization with the low-order kinematic model. Subsequently, an agile maneuvering approach with environmental adaptability is integrated into the second-stage optimization with the high-order kinematic model. This approach can effectively adjust the agile flight according to the obstacle distribution and motion velocity. Simulations and real-world experiments are conducted to demonstrate the robustness and effectiveness of the presented approach. Moreover, benchmark comparison manifests that the proposed method outperforms the cutting-edge methods from the aspects of the perception uncertainty adaptation and flight safety.

Development and testing of immersive virtual reality environment for safe unmanned aerial vehicle usage in construction scenarios

Robotics and autonomous systems are increasingly being used on construction sites to complete tasks and complement human effort. One such autonomous system is unmanned aerial vehicles (UAV), although their wrong use could be potentially hazardous to humans and the environment. Given its relative novelty in construction, there remains a dearth of knowledge about safety risks associated with their use as well as knowledge on skills and protocols for their safe operation. With construction being one of the most dangerous and accident-prone sectors, there is even more impetus to develop UAV safety competence. In this study, we explore the application of immersive virtual reality (VR) use for purposes of safety training pertaining to the use of UAVs in construction scenarios. Whereas, previous studies, focussed on effectiveness of VR as a tool for training individuals on UAV operations and safety risks in general, this study evaluates its effectiveness across distinctive themes of safety in order to compare its effectiveness in different domains of UAV safety. The study adopts design science for development of bespoke UAV safety training tool in VR using Building Information Modelling (BIM) and game engine-driven virtual prototyping. The immersive training tool was then applied in an experiment of participants (n = 100) with (n = 50) constituting a control group using more traditional training methods and a series of pre/post-test assessments. The findings confirmed the relative superiority of VR training over traditional methods, improving performance and retention by up to 22 % on average. The immersive VR was found to be most effective on participants retention of knowledge in general flight preparation, air traffic documentation, visibility management and warning signals. Further usability tests were performed on all participants (n = 100) revealing general presence and positive attitudes towards VR based UAV safety training when compared to traditional training. From a practice perspective this research proofs the effectiveness of a VR approach as more cost effective and safe approach to UAV training for both academia and practice with added advantage of being more realistic simulation of the UAV risk scenarios.

THE PROCEDURE FOR THE USE OF UNMANNED AERIAL VEHICLES TO CONDUCT RECONNAISSANCE ON THE GROUND IN WARTIME CONDITIONS

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.

ПРОБЛЕМАТИКА ВИКОРИСТАННЯ БЕЗПРОВІДНИХ СЕНСОРНИХ МЕРЕЖ У ВІЙСЬКОВИХ ЦІЛЯХ

The article analyzes wireless sensor networks and identifies their advantages and disadvantages when used for military purposes. It is proposed to consider the architecture of the network based on a four-level model, which consists of the level of sensors, data channels, data processing and applications for creating various types of services. This creates a versatile and effective set of instruments for solving various tasks of special purpose for military purposes. The structure of a wireless sensor network for military use is considered in detail, taking into account the variety of sensors and their purpose. It also emphasizes the importance of compact and efficient sensors for military purposes, which contain means of digital information processing, receiver and transmitter, including measurements of electromagnetic waves, light, pressure, sound. The possibility of creating sensor fields based on unmanned aerial vehicles and their use for security and information gathering is noted. Data link parameters are detailed, including signal strength and signal level at the receiving end. The importance of space scanning radars for ensuring the safety of unmanned aerial vehicles in the conditions of wind speed and obstacles is noted. The article points out the problems of energy and computing resources when using sensors, in particular, the limitation of energy consumption and the length of the information transmission channel. Methods of rationalizing energy consumption by aggregating data and using clustering protocols are proposed. The main idea of the article is that the use of wireless sensor networks for military purposes requires attention to architectural details, sensor selection and power consumption optimization to ensure effective and reliable use of this system for its intended purpose, which will lead to the successful completion of the task by the unit. Therefore, the main advantages include the simplicity and flexibility of deploying sensor fields and the prompt receipt of information directly from the contact line, which is the key to a quick response to the enemy's actions.

Enhancing UAV safety with an innovative anti-collision cage: Design, testing, and future prospects

The rapid evolution of Unmanned Aerial Vehicles (UAVs) or Unmanned Aircraft Systems (UAS) has revolutionized aviation across military and civilian domains with their pilotless flight capabilities. Despite their versatility, UAVs pose challenges in in-flight piloting precision, leading to potential mishaps. Addressing these concerns, this research introduces an innovative UAV Anti-Collision Cage designed to enhance drone safety. Constructed from C60-shaped carbon fiber rods and 3D-printed connectors, the cage resembles a footballs geometry, offering 360 protection. Experimental validations, including rolling, collision, and flying tests, were conducted to assess the cages performance. While the cage demonstrated resilience against minor impacts, significant impacts posed challenges. The study concludes with recommendations for future improvements, emphasizing geometric refinements, material choices, enhanced rolling abilities, sensor integration, and payload capacity. This research underscores the importance of safety mechanisms in UAV operations, paving the way for safer and more efficient drone operation in confined and complex environments.

Electric and Magnetic Fields Analysis of the Safety Distance for UAV Inspection around Extra-High Voltage Transmission Lines

The deployment of small unmanned aerial vehicles (UAVs), or drones, for transmission line inspections, has brought attention to the potential impact of electromagnetic fields (EMFs) on UAV operations. This work describes a mathematical model based on the finite elements method (FEM), designed to examine the electric and magnetic fields produced by extra-high voltage (EHV) conductors. The current study extends the analysis to encompass both electric and magnetic fields and evaluates the safe distances for UAVs operating near 345 kV, 500 kV, and 765 kV transmission lines. The electromagnetic environment around these EHV transmission lines was simulated using electrostatic, magnetostatic, and transient magnetic modules within the QuickField software 6.6. Electric and magnetic profiles were estimated using 2D finite element analysis, including a numerical simulation for phase-to-phase fault EMFs for the above transmission lines. These results were then cross-verified with theoretical calculations at specific intervals and further validated using the EMFACDC analytical method developed by the International Telecommunication Union. This comprehensive assessment concludes that precise distance considerations are necessary to ensure UAV safety during power line inspections, mitigating potential risks from EMF interference.

Rule-Based Verification of Autonomous Unmanned Aerial Vehicles

Automation enhances the capabilities of unmanned aerial vehicles (UAVs) by enabling self-determined behavior, while reducing the need for extensive human involvement. Future concepts envision a single human operator commanding multiple autonomous UAVs with minimal supervision. Despite advances in automation, there remains a demand for a “human in command” to assume overall responsibility, driven by concerns about UAV safety and regulatory compliance. In response to these challenges, a method for runtime verification of UAVs using a knowledge-based system is introduced. This method empowers human operators to identify unsafe behavior without assuming full control of the UAV. Aspects of automated formalization, updating and processing of knowledge elements at runtime, coupled with an automatic reasoning process, are considered. The result is an ontology-based approach for runtime verification, addressing the growing complexity of UAVs and the need to ensure safety in the context of evolving aviation regulations.

OoD-Control: Generalizing Control in Unseen Environments.

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.

Real Time Route Adjustment of a UAV Based on Dust Measurement with an Onboard Sensor

This research is driven by a pressing requirement to improve the effectiveness and safety of unmanned aerial vehicle (UAV) operations, specifically in atmospheric particle sensing. Airborne particulates present a substantial health hazard, leading to an increasing demand for inventive solutions capable of autonomously guiding UAVs along optimal routes, steering clear of regions with heightened dust concentrations. This research aims to address this challenge by developing a sophisticated approach that integrates atmospheric particle sensors with UAV flight control systems. The approach outlined in this study relies on atmospheric sensors mounted on a UAV to measure dust levels in a specified region. The UAV, following a predetermined route over the area, detects dust pollution. It dynamically adjusts its route based on the observed dust levels, avoiding areas with high concentrations of dust. This adaptive route determination aims to identify safe paths, avoiding regions with elevated dust levels that may pose risks to human health. The flight strategy and area-scanning methodology tailored for this objective are established, enabling the UAV to execute the assigned task. The ultimate goal is to create a system that not only minimizes energy consumption but also prioritizes human health by autonomously redirecting the UAV away from potentially harmful dust levels. The real-time monitoring and telemetry data feedback mechanisms further contribute to the advancement of UAV technology for environmental sensing and risk mitigation. Additionally, the integration of the laser-based dust sensor, i.e., Gp2y10, with the ArduPilot autopilot flight computer ensures seamless coordination, with the UAV adjusting its flight path based on the sensor's output.

Vehicle-to-Vehicle Based Autonomous Flight Coordination Control System for Safer Operation of Unmanned Aerial Vehicles

The exponential growth of unmanned aerial vehicles (UAVs) or drones in recent years has raised concerns about their safe operation, especially in beyond-line-of-sight (BLOS) scenarios. Existing unmanned aircraft system traffic management (UTM) heavily relies on commercial communication networks, which may become ineffective if network infrastructures are damaged or disabled. For this challenge, we propose a novel approach that leverages vehicle-to-vehicle (V2V) communications to enhance UAV safety and efficiency in UAV operations. In this study, we present a UAV information collection and sharing system named Drone Mapper®, enabled by V2V communications, so that UAVs can share their locations with each another as well as with the ground operation station. Additionally, we introduce an autonomous flight coordination control system (AFCCS) that augments UAV safety operations by providing two essential functionalities: UAV collision avoidance and UAV formation flight, both of which work based on V2V communications. To evaluate the performance of the developed AFCCS, we conducted comprehensive field experiments focusing on UAV collision avoidance and formation flight. The experimental results demonstrate the effectiveness of the proposed system and show seamless operations among multiple UAVs.

Remaining Flying Time Prediction of Unmanned Aerial Vehicles Under Different Load Conditions

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.

Fine‐grained crack segmentation for high‐resolution images via a multiscale cascaded network

AbstractHigh‐resolution (HR) crack images offer more detailed information for assessing structural conditions compared to low‐resolution (LR) images. This wealth of detail proves indispensable in bolstering the safety of unmanned aerial vehicle (UAV)‐based inspection procedures and elevating the precision of small crack segmentation. Nonetheless, achieving a balance between segmentation accuracy and GPU memory consumption poses a substantial challenge for deep learning models when processing HR crack images. To overcome this challenge, a novel “HR crack segmentation framework” (HRCSF) is proposed, specifically designed to meticulously segment crack images with resolutions exceeding 4K. First, a multiscale crack feature extraction network (MsCFEN) was proposed with the embedment of the strip pooling operation to enhance the representation of the transverse and longitudinal crack pixels from the complex backgrounds. Subsequently, two cascaded operations were tailored to MsCFEN, enabling a comprehensive refinement process that incorporates both global and local aspects. Furthermore, to fully leverage the potential of each proposed component in the refinement process, the complete architecture was trained using a loss function with embedded boundary optimization. Conclusively, a UAV‐based case study was conducted on a real bridge in Changsha, demonstrating HRCSF's practicability in segmenting HR crack images. The implementation of HRCSF allows the UAV to perform crack inspection effectively from a distance of 3 m away from the girder, resulting in a significant 50% reduction in inspection time compared to LR segmentation methods while maintaining high detection accuracy.

An obstacle avoidance approach for UAV path planning

The recent pandemic of COVID-19 has proven to be a test case for Unmanned Aerial Vehicles (UAVs). UAVs have shown great potential for plenty of applications in the face of this pandemic, but their scope of applications becomes limited due to the dependency on ground pilots. Irrespective of the application, it is imperative to have an autonomous path planning to utilize UAVs to their full potential. Collision-free trajectories are expected from the path planning process to ensure the safety of UAVs and humans on the ground. This work proposes a path planning technique where collision avoidance is mathematically proven under an uncertainty prerequisite, that the UAV follows its requested moving position within some threshold distance. This scheme ensures UAV safety by considering the underlying control’s system overshoots. Obstacles play a guiding role in selecting collision-free trajectories. These obstacles are modeled as rectangular shapes with interest points defined around their corners. These points further define collision-free permissible edges, and later we apply the Dijkstra algorithm to these edges before having the desired trajectory. Regardless of the size of deployment area, our proposed scheme incurs low computational load due to the dependency on pre-defined interest points only thereby making it suitable for real-time path planning. Simulation results obtained using MATLAB’s UAV Toolbox show that the proposed method succeeds in getting short collision-free trajectories.

Review on Type of Sensors and Detection Method of Anti-Collision System of Unmanned Aerial Vehicle.

Unmanned aerial vehicle (UAV) usage is increasing drastically worldwide as UAVs are used in various industries for many applications, such as inspection, logistics, agriculture, and many more. This is because performing a task using UAV makes the job more efficient and reduces the workload needed. However, for a UAV to be operated manually or autonomously, the UAV must be equipped with proper safety features. An anti-collision system is one of the most crucial and fundamental safety features that UAVs must be equipped with. The anti-collision system allows the UAV to maintain a safe distance from any obstacles. The anti-collision technologies are of crucial relevance to assure the survival and safety of UAVs. Anti-collision of UAVs can be varied in the aspect of sensor usage and the system's working principle. This article provides a comprehensive overview of anti-collision technologies for UAVs. It also presents drone safety laws and regulations that prevent a collision at the policy level. The process of anti-collision technologies is studied from three aspects: Obstacle detection, collision prediction, and collision avoidance. A detailed overview and comparison of the methods of each element and an analysis of their advantages and disadvantages have been provided. In addition, the future trends of UAV anti-collision technologies from the viewpoint of fast obstacle detection and wireless networking are presented.

Track Planning for Damage Detection on the Building Enclosure by UAV Considering Image Detection

Considering the rapid acquisition and detection task of building enclosure damage information and the natural environment characteristics of building enclosure, a new unmanned aerial vehicle (UAV) track planning framework considering image detection and UAV dynamic characteristics is proposed. Firstly, considering the position of UAV in the task space for track planning, combined with the characteristics of the sensor detection equipment carried by UAV, and the task requirements of extracting and identifying the damage edge features and texture features on the shell of the building; the detection efficiency and detection safety of UAV are improved to the maximum extent. Secondly, the Improved Circle Chaos Initialization Strategy, Adaptive Weight Strategy and Elite Perturbation Mechanism are introduced to solve the problem that the traditional honey badger algorithm (HBA) is easy to fall into local optimal solution and to ensure that the designed algorithm is more in line with the inspection planning requirements of the outer surface of the building. Furthermore, improved honey badger algorithm (IHBA) is compared with particle swarm optimization (PSO), whale optimization algorithm (WOA) and traditional badger algorithm in multi-dimensional horizontal through design simulation experiments, which shows that the design algorithm in this paper is efficient in solving the damage information collection of building enclosure.

Detection of Power Poles in Orchards Based on Improved Yolov5s Model

During the operation of agricultural unmanned aerial vehicles (UAVs) in orchards, the presence of power poles and wires pose a serious threat to flight safety, and can even lead to crashes. Due to the difficulty of directly detecting wires, this research aimed to quickly and accurately detect wire poles, and proposed an improved Yolov5s deep learning object detection algorithm named Yolov5s-Pole. The algorithm enhances the model’s generalization ability and robustness by applying Mixup data augmentation technique, replaces the C3 module with the GhostBottleneck module to reduce the model’s parameters and computational complexity, and incorporates the Shuffle Attention (SA) module to improve its focus on small targets. The results show that when the improved Yolov5s-Pole model was used for detecting poles in orchards, its accuracy, recall, and mAP@50 were 0.803, 0.831, and 0.838 respectively, which increased by 0.5%, 10%, and 9.2% compared to the original Yolov5s model. Additionally, the weights, parameters, and GFLOPs of the Yolov5s-Pole model were 7.86 MB, 3,974,310, and 9, respectively. Compared to the original Yolov5s model, these represent compression rates of 42.2%, 43.4%, and 43.3%, respectively. The detection time for a single image using this model was 4.2 ms, and good robustness under different lighting conditions (dark, normal, and bright) was demonstrated. The model is suitable for deployment on agricultural UAVs’ onboard equipment, and is of great practical significance for ensuring the efficiency and flight safety of agricultural UAVs.

Influence of Operationally Consumed Propellers on Multirotor UAVs Airworthiness: Finite Element and Experimental Approach

Unmanned aerial vehicles (UAVs) are widely used in many applications due to the current technological trend. In these applications, understanding the concept of the operating frequency range is vital to ensure UAV safety. Although most current studies are focused on health monitoring and fault diagnostic methodologies, none of them has included a comprehensive knowledge of the behavior of the natural frequency behavior. This research examines the effectiveness of computer-aided design (CAD) and computer-aided engineering (CAE) in advancing finite element modal analysis. Theoretical results were further evaluated on an experimentally operated drone to understand the interference concept of natural and operational frequencies. Extensive, high-quality, and comprehensive soft-labeled data was acquired in the time-domain from the selected hovering quad-copter equipped with an accelerometer sensor connected to a data acquisition system and a PC platform. Healthy propellers and operationally consumed propellers vibration signal datasets were recorded. The collected datasets were transformed to frequency-domain using Fast Fourier Transform (FFT) spectrum analysis. In summary, the finite element modal analysis provided a thorough behavior understanding of the faulty operating drone status by comparing the natural frequencies to those acquired experimentally based on a detailed discussion. The relationship between natural and operational frequencies is then investigated and presented.

Opportunistic Autonomous Integrity Monitoring for Enhanced UAV Safety

An opportunistic advanced receiver autonomous integrity monitoring (OARAIM) algorithm for unmanned aerial vehicle (UAV) navigation is developed. This algorithm fuses ambient terrestrial signals of opportunity (SOPs) with global navigation satellite system (GNSS) signals to improve the capability of detecting GNSS and SOP measurement faults and provide tight protection level (PL) bounds. A receiver is assumed to make pseudorange measurements on multiple GNSS satellites and terrestrial SOPs. Experimental tests are presented injecting simulated faults into the OARAIM algorithm, which successfully detects the faults in GPS satellites and SOPs. Moreover, the OARAIM algorithm and the inclusion of the SOPs reduces the vertical and horizontal PLs by more than 55% and 70%, respectively, compared to only using GNSS measurements.

DP-Authentication: A novel deep learning based drone pilot authentication scheme

Unmanned Aerial Vehicles (UAVs), also known as drones, have recently been proposed as flying base stations for providing reliable service to IoT devices. However, due to the lack of effective authentication schemes, UAVs are often hijacked by adversaries, which raises a high potential for sensitive information leakage. Therefore, designing a real-time authentication scheme is essential to enhance UAV safety. Up to the present, several works exist about pilot authentication by classifying radio-control signals. As propagating through the open environment, radio-control signals can be sniffed, analyzed, and simulated, posing significant threats to UAV security. For this reason, we propose a novel deep learning-based drone pilot authentication scheme, DP-Authentication, to protect UAVs from malicious radio-manipulated attacks. Specifically, we collect UAV flight data from the onboard PX4 flight stack and feed them into the authentication scheme to validate pilot legal status dynamically. As verified by comprehensive experiments, the proposed authentication scheme can authenticate pilots with an accuracy of 95.24% and detect malicious hijacking with an accuracy of 96.82%. Thanks to the low system overhead, it holds great promise for deployment on the UAV side to monitor pilot legal status in real-time.

A Review of Unmanned Aerial Vehicle Applications in Construction Management: 2016–2021

With the rapid advancement of Unmanned Aerial Vehicle (UAV) technologies in recent years, their uses have been increasingly adopted in the architecture, engineering, and construction industries. To satisfy the needs of various types of construction projects, a considerable amount of research work has been performed to implement and refine the operations, safety, and accuracy of UAVs. This paper presents the findings of a comprehensive literature review that focuses on UAV research in construction management during the timeframe of 2016 to 2021. A total of 95 papers were identified and collected from a list of 21 relevant journals and conference proceedings, and were then categorized by their research topic, sensor types, and targeted structures. The results of 47 exemplary studies were reported in two categories, namely UAV uses and construction uses. The research topics identified for UAV uses include algorithm, applications, operations, framework, and training, while research topics identified for construction use include inspection, surveying, safety, and monitoring. The connection between the research topics, sensor types, targeted structures, and other advanced technologies were also discussed. This paper summarizes the current results of UAV research in construction management, reviews the methodology, benefits, and limitations of the reviewed literature, and provides valuable knowledge for the future trend of UAV applications in the civil, infrastructure, and construction industries.