Flight Task Research Articles

Exploring the impact of virtual reality flight simulations on EEG neural patterns and task performance

Neurophysiological measurements, such as electroencephalography (EEG), can be used to derive insight into pilots’ mental states during flight training and to track learning progress in order to optimize the training experience for each individual. Prior work has demonstrated that the level of fidelity of a flight simulation (2D Desktop vs. 3D VR) is associated with different cortical activity in relation to task demands. However, it remains unknown whether simulation fidelity affects flight performance, and whether this effect can be observed in EEG neurophysiological responses associated with workload. The current study therefore assessed whether an EEG-based index of workload and task engagement is predictive of performance during flight training in different simulation environments. We conducted a within-subject designed experiment with 53 novice participants who performed two flight tasks (speed change, medium turn) under two conditions (Desktop vs. VR). EEG signals were collected throughout the experiment to quantify mental workload using the beta-ratio (βα+θ). The VR condition showed increased beta-ratios in all lobes, including frontal and parietal areas, compared to the Desktop simulation. Additionally, we observed an effect of simulator environment on performance, as VR was associated with improved flight performance. However, we found no evidence of a relationship between the beta-ratio and performance. Our findings demonstrate that the brain responds differently to tasks in training environments of various levels of fidelity. However, more research into the neurometrics of flight training is needed in order to develop brain-computer interfaces that can enhance current pilot training methods by providing personalized feedback in real-time.

Attention demands modulate brain electrical microstates and mental fatigue induced by simulated flight tasks.

Prolonged engagement in tasks with varying attention demands is thought to elicit distinct forms of mental fatigue, potentially indicating variations in neural activity. This study aimed to investigate the association between mental fatigue and changes in electroencephalogram (EEG) microstate dynamics during tasks with varying attention demands. In the present study, we employed a 2×2 repeated measures ANOVA to analyze the temporal parameters of four distinct microstates (A, B, C, and D) across different levels of attention demands (high vs. low) and mental fatigue (high vs. low) within a controlled flight simulation task involving 17 college students. Significant variations in mean durations were observed, with microstates A and B exhibiting shorter durations under high fatigue during low attention demands, while their durations increased under high attention demands. Microstate C showed increased occurrences with high fatigue under low attention demands and decreased occurrences under high attention demands. The duration and occurrence of the microstates exhibited different trends throughout the course of mental fatigue, potentially reflecting distinct fatigue-related processes. These findings establish a link between different types of mental fatigue and microstate dynamics, contributing to a comprehensive understanding of the neural processing mechanisms underlying mental fatigue.&#xD.

Method for determining the effect of destabilizing factors of digital filters on the multicriteria synthesis of radio communications of aviation robotic devices

The purpose of the research is development of a method for determining the influence of destabilizing factors, such as the unevenness of the amplitude-frequency response (AFC) and the nonlinearity of the phase-frequency response (FFC), in non-recursive digital frequency selection filters on the target function of multicriteria structural-parametric synthesis of radio communications from the payload of aviation robotic devices.Methods. In conducting scientific research, probability theory, mathematical statistics, statistical radio engineering and computational mathematics were used. The study obtained mathematical expressions of the dependence of the probability of error per bit (BER) on the average signal-to-noise ratio when signals pass through symmetrical non-recursive digital filters with quadrature amplitude modulation with a positivity of no more than 4096, signal constellation irregularities of no more than 5, which are the basis for the energy balance of radio lines with aviation robotic devices. The expressions obtained are one of the "foundations" for the implementation of a multi-criteria synthesis of radio systems adaptive to a cognitively formed flight task. In this paper, all the results of the probability of reliability of the received data by means of communication are reflected for the case of rigid decision-making (Gray coding).Results. In the course of the conducted research: 1) a mathematical model of the structural-parametric synthesis of radio communications with aviation robotic devices is demonstrated, which is based on the criteria for minimizing: electronic computing resources with a given noise immunity; radiation outside the operating frequency band; peak factor signals; the effects of narrowband, broadband and structural interference in the radio channel at a given bandwidth; the use of structural and functional nodes specific to the existing information and communication environment; 2) the analytical dependence of the influence of the parameters of one of several structural and functional nodes - digital filters of frequency selection, on the criteria for the synthesis of communication means has been revealed. The paper considers the most common approximating functions - Kaiser, Tukey, Barlett-Khan, Chebyshev, and also shows the influence of the filter order on the equivalent of energy loss in information communication lines of aviation robotic devices.Conclusion. The scientific article presents a method for determining the effect of the uneven frequency response of a symmetrical non-recursive digital filter in devices for receiving and processing radio signals of multicriteria means of communication with the payload of aviation robotic devices. The proposed approach increases the reliability of determining the values of criteria for minimizing electronic computing resources for a given noise immunity, radiation outside the operating frequency band, as well as the use of structural and functional nodes characteristic of the existing information and communication environment.

Situation Awareness Discrimination Based on Physiological Features for High-Stress Flight Tasks

Situation Awareness Discrimination Based on Physiological Features for High-Stress Flight Tasks

Monitoring pilot trainees’ cognitive control under a simulator-based training process with EEG microstate analysis

The objective of pilot training is to equip trainees with the knowledge, judgment, and skills to maintain control of an aircraft and respond to critical flight tasks. The present research aims to investigate changes in trainees’ cognitive control levels during a pilot training process while they underwent basic flight maneuvers. EEG microstate analysis was applied together with spectral power features to quantitatively monitor trainees’ cognitive control under varied flight tasks during different training sessions on a flight simulator. Not only could EEG data provide an objective measure of cognitive control to complement the current subjective assessments, but the application of EEG microstate analysis is particularly well-suited for capturing rapid dynamic changes in cognitive states that may happen under complex human activities in conducting flight maneuvers. Comparisons were conducted between two types of tasks and across different training stages to monitor how pilot trainees’ cognitive control responds to varied flight task types and training stages. The present research provides insights into the changes in trainees’ cognitive control during a pilot training process and highlights the potential of EEG microstate analysis for monitoring cognitive control.

Prediction method for 4D trajectory of large fixed wing UAV under multi-layer convolution and bidirectional gating model

Abstract With the increase of low-altitude flight tasks, the importance of research on low-altitude flight safety and efficient operation has gradually become prominent. Therefore, it is necessary to try to establish a low-altitude airspace flight path pre-planning method based on 4D trajectory prediction, which can effectively support the above research. In order to improve the integrity, accuracy and reliability of the long-range trajectory prediction results of large fixed wing UAV (LFW UAV), based on the standard neural network as the basic concept to form a fusion model of improved convolutional neural network (CNN) and bidirectional gated recurrent neural network (BIGRU). At the same time, the flight trajectory fitting preprocessing is added to build the completed multilayer cyclic convolutional improvement model (MCCI). The experimental results show that the MCCI model has obvious advantages in terms of prediction accuracy, deviation range and predictable duration when dealing with the LFW UAV 4D trajectory prediction problem, and better reflects the characteristics of 3D position and spatiotemporal elements. The comparative analysis shows that the error values of the MCCI model are smaller than those of the comparison prediction models in any dimension, and the prediction results have better fit and model performance.

Insufficient safety capability among general aviation practitioners: Evidence from Chinese accident data

The rapid development of China’s general aviation (GA) industry has brought about an increase in safety risks and accidents. Despite extensive investigations into these accidents, the factors related to the safety capability (SC) of practitioners have been largely overlooked, resulting in a lack of targeted safety education and training policies. Hence, this study aims to investigate the characteristics of practitioners’ SC deficiencies in the GA industry. Data for this study were sourced from GA accident reports in China from 2018 to 2022. First, the reports were identified and encoded to extract relevant concepts and categories based on grounded theory. Second, the 24Model was used to classify these categories. After that, the relationship between the core category and the main category was delineated according to the accident scenarios. This comprehensive study has resulted in the development of a SC conceptual framework comprising five core categories and fifteen main categories. In addition, the correspondence between flight tasks, practitioners, and SC factors and the relationship between cases and SC factors are revealed. The findings underscore that insufficient theoretical knowledge and a lack of risk awareness, particularly among pilots, emerge as pivotal SC factors contributing to GA accidents, especially during flight training. In summary, this study introduces a framework that advances our comprehension of SC deficiencies among GA practitioners and offers valuable insights for the enhancement of GA safety through safety education and training. Additionally, it offers guidance to stakeholders in formulating the most effective strategies for cultivating SC.

Cardiac autonomic responses in relation to cognitive workload during simulated military flight

Understanding the operator's cognitive workload is crucial for efficiency and safety in human–machine systems. This study investigated how cognitive workload modulates cardiac autonomic regulation during a standardized military simulator flight. Military student pilots completed simulated flight tasks in a Hawk flight simulator. Continuous electrocardiography was recorded to analyze time and frequency domain heart rate variability (HRV). After the simulation, a flight instructor used a standardized method to evaluate student pilot's individual cognitive workload from video-recorded flight simulator data. Results indicated that HRV was able to differentiate flight phases that induced varying levels of cognitive workload; an increasing level of cognitive workload caused significant decreases in many HRV variables, mainly reflecting parasympathetic deactivation of cardiac autonomic regulation. In conclusion, autonomic physiological responses can be used to examine reactions to increased cognitive workload during simulated military flights. HRV could be beneficial in assessing individual responses to cognitive workload and pilot performance during simulator training.

Adaptive Factor Fuzzy Controller for Keeping Multi-UAV Formation While Avoiding Dynamic Obstacles

The development of unmanned aerial vehicle (UAV) formation systems has brought significant advantages across various fields. However, formation change and obstacle avoidance control have long been fundamental challenges in formation flight research, with the majority of studies concentrating primarily on quadrotor formations. This paper introduces a novel approach, proposing a new method for designing a formation adaptive factor fuzzy controller (AFFC) and an artificial potential field (APF) method based on an enhanced repulsive potential function. These methods aim to ensure the smooth completion of fixed-wing formation flight tasks in three-dimensional (3D) dynamic environments. Compared to the traditional fuzzy controller (FC), this approach introduces a fuzzy adaptive factor and establishes fuzzy rules to address parameter-tuning uncertainties. Simultaneously, improvements to the obstacle avoidance algorithm mitigate the issue of local optimal values. Finally, multiple simulation experiments were conducted. The findings show that the suggested method outperforms the proportional–integral–derivative (PID) control and fuzzy control methods in achieving formation transformation tasks, resolving formation obstacle avoidance challenges, enabling formation reconstruction, and enhancing formation safety and robustness.

Research on identification of flight cadets’ cognitive load based on multi-source physiological data and CGAN-DBN model

Modern aircraft cockpit system is highly information-intensive. Pilots often need to receive a large amount of information and make correct judgments and decisions in a short time. However, cognitive load can affect their ability to perceive, judge and make decisions accurately. Furthermore, the excessive cognitive load will induce incorrect operations and even lead to flight accidents. Accordingly, the research on cognitive load is crucial to reduce errors and even accidents caused by human factors. By using physiological acquisition systems such as eye movement, ECG, and respiration, multi-source physiological signals of flight cadets performing different flight tasks during the flight simulation experiment are obtained. Based on the characteristic indexes extracted from multi-source physiological data, the CGAN-DBN model is established by combining the conditional generative adversarial networks (CGAN) model with the deep belief network (DBN) model to identify the flight cadets’ cognitive load. The research results show that the flight cadets’ cognitive load identification based on the CGAN-DBN model established has high accuracy. And it can effectively identify the cognitive load of flight cadets. The research paper has important practical significance to reduce the flight accidents caused by the high cognitive load of pilots.

NEW APPROACH TO MILITARY PILOT EDUCATION AND TRAINING IN THE NEW SECURITY ENVIRONMENT

Military pilot education, including flight training, is a long, expensive, and complex process. The main goal is to produce as many skilled professionals as possible ready to perform flight tasks on the front-line aircraft and push them to their workload limits with complete control. To achieve this goal, it is necessary to select and educate young people with the aptitude and abilities to perform the most demanding military flight tasks. The creation of a model for the education and training of military pilots depends on the requirements of the Czech Air Force Command and the teaching capacities of the University of Defence and the Aviation Training Centre in Pardubice. Rearmament of the Czech Air Force and the required number of pilots requires a change in the new military pilot training model. This contribution describes a new approach to Czech Air Force pilot education and training in the current security conditions in the world.

Алгоритм планирования маршрута БПЛА в условиях недостатка визуальных ориентиров

Today, unmanned aircraft is used to solve a variety of tasks. One of the problems that arise when using unmanned aerial vehicles (UAVs) is the problem of navigation, in particular, determining their exact location in space. Today, there are various methods to solve this problem. A promising way to solve it in conditions where it is impossible to use a satellite navigation system is video navigation systems that use visual landmarks to determine location. In this paper, an overview of the existing types of UAV video navigation systems was carried out. The peculiarities of the operation of video navigation systems using the comparison of visual landmarks with satellite maps were also highlighted. The task was set to find a route in conditions of a lack of visual landmarks, in which the problem of losing reference points may arise. The graph data format used is described, which stores the values of the metric of the diversity of the underlying surface, which characterizes the magnitude of the probability of the problem of losing reference points. The existing algorithms for finding a path in a graph are considered and the need to develop a new algorithm is justified. A theoretical description of the algorithm was proposed, in which the path search process is divided into two stages: first, a solution option is searched for with the maximum value of the metric of diversity of the underlying surface, and then a route with the minimum length is selected. Several options of the implementation of the proposed algorithm were also presented and their asymptotic complexity was estimated. Based on the theoretical description, a software implementation of the proposed algorithm was developed and an experimental verification of the correctness of its operation on a test card with various flight tasks was performed. According to the test results, the proposed algorithm has shown its effectiveness by constructing routes correctly in all test examples. In the future, this algorithm can be used as part of the UAV video navigation system. The main advantages and disadvantages of the proposed algorithm were also described and ways for its further improvement were proposed.

Monocular-GPS Fusion 3D object detection for UAVs

Unmanned Aerial Vehicles (UAVs) exhibit superior maneuverability and large-scale movement capability in 3D space, making them ideal platforms for various autonomous applications. However, UAVs with cameras face challenges in 3D situational awareness, which limits their applications in 3D space, particularly in Lake Search and Rescue (LSAR) scenarios. In this paper, we propose a new LSAR Aerial 3D detection dataset (LA3D) and a pioneering end-to-end aerial Monocular-GPS Fusion 3D detector (MGF3D) to improve UAVs’ 3D understanding capability and mitigate the gap between research and real-world application. LA3D is collected by a well-designed hybrid-wing UAV platform on lake surface scenes, which has high-quality annotated 3D bounding boxes, aligned GPS altitudes, and comprehensive metrics. Moreover, MGF3D aggregates multi-modal features with rich geometric and semantic information and feeds them into the 3D detection head for final predictions. To achieve this, we design the Altitude Encoder (AE) module to extract geometric GPS altitude features and introduce Image Encoder (IE) to encode semantic image features respectively. The plug-and-play Monocular-GPS Fusion (MGF) module is proposed to fuse GPS altitude features with image features, which adaptively learns 3D space information and enhances long-distance depth estimation capability. The extensive experiments on the LA3D dataset validate that MGF3D significantly improves aerial 3D detection accuracy. Furthermore, the MGF3D is deployed on a UAV embedded platform to perform the challenging real-world LSAR flight task, demonstrating the effectiveness and practicality of our LA3D and MGF3D. We plan to release the dataset and code in here.

Fault diagnosis of drone motors driven by current signal data with few samples

Multi rotor unmanned aerial vehicles (UAVs) are extensively utilized across various domains, and the motor constitutes a pivotal element in the UAV power system. The majority of UAV failures and crashes stem from motor malfunctions, underscoring the imperative need for comprehensive research on fault diagnosis in UAV motors to ensure the stable and reliable execution of flight tasks. This study focuses on quadrotor UAVs as the research subject and devises targeted fault simulation experiments based on the structural features and operational characteristics of the DC brushless motor used in quadrotor UAVs, specifically examining the stator, rotor, and bearings. To address challenges related to the UAV’s own loads, limited space for redundant parts, and the high cost and difficulty associated with installing sensors for traditional fault diagnostic signals such as vibration and temperature, this study opts to use current signals as a substitute. This approach resolves the issue of challenging data collection for UAVs and investigates a current signal based fault diagnosis method for UAV motors. Lastly, in response to the limited training samples available for fault data due to the UAV’s highly sensitive characteristics regarding the health status of its components and flight stability, traditional machine learning and deep learning methods encounter difficulties in identifying representative features with a small number of training samples, leading to the risk of overfitting and reduced model accuracy in fault diagnosis. To overcome this challenge, we propose a hybrid neural network fault diagnosis model that incorporates a width learning system and a convolutional neural network (CNN). The width learning system eliminates temporal characteristics from the original current signal, capturing more comprehensive and representative sample features in the width feature space. Subsequently, the CNN is employed for feature extraction and classification tasks. In empirical small sample fault diagnosis experiments using current signal data for UAV motors, our proposed model outperforms other models used for comparison.

Deep reinforcement learning aided secure UAV communications in the presence of moving eavesdroppers

Integrating unmanned aerial vehicles (UAVs) with wireless systems is widely anticipated to bring enormous benefits to network users, and is considered as a key enabling technology for next generation wireless networks. However deployment of UAVs raises security concerns, as they are prone to eavesdropping attacks. In this paper we investigate secure data transmissions in a UAV-aided communication system from the physical layer security (PLS) perspective. The UAV is employed as a flying base station and transmit confidential information to a ground user. Specifically, an eavesdropper is moving in the vicinity of the ground user, attempting to intercept legitimate data transmission. We aim to optimize UAV trajectory to maximize secrecy throughput and due to the fast changing environment with unpredictable movements of the eave, this nonconvex problem is difficult to solve. Instead, we propose a deep reinforcement learning framework by reformulating this problem as a Markov Decision Process (MDP). By carefully designing state spaces, state-dependent action sets and time-dependant reward signals, we aim to help the agent learn an optimized trajectory and complete the flight task within time limit. We adapt the deep Q network (DQN) algorithm and extend it with prioritized experience replay (PER) and importance sampling method to ensure a stable and effective learning outcome. Simulation results validate the effectiveness of the learning algorithm and confirm that the agent is able to learn an effective policy with desirable and robust learning outcomes. Compared with benchmark schemes, our proposed scheme is shown to achieve considerable performance gain in secrecy throughput.

Control of a quadrotor on a mobile device using machine learning-based monocular gaze tracking

A machine learning-based monocular gaze tracking method for mobile devices is proposed. A non-invasive, convenient, and low-cost gaze tracking framework is developed using our constructed convolutional neural network. This framework is applied to the 3D motion control of quadrotors, which can convert the operator’s gaze attention into control intention for the quadrotor, thus allowing the operator to control the quadrotor to complete flight tasks through visual interaction. Extensive challenging indoor and outdoor real-world experiments and benchmark comparisons validate that the proposed system is robust and effective, even for unskilled operators. The proposed method can improve the smoothness and reasonableness of the motion trajectory of the quadrotor, make it more consistent with the operator’s control intention, and introduce diversity, convenience, and intuition into the control of the quadrotor. We released the source code3 3 https://github.com/hujavahui/Gaze_MAV of our system to benefit related research.

Effects of Task Proficiency and Automation Reliability on Pilot-ePilot Teaming in Intelligent Aircraft Cockpits

Intelligence of aircraft cockpits can be improved by creating an electronic pilot (ePilot) to establish a Pilot-ePilot teaming relationship. Effects of task proficiency and automation reliability on such kind of human-autonomy teaming were studied, including task performance, task load, situation awareness, trust in automation, and human errors. In a 2 × 3 within-subjects experiment (low, medium & high levels for task proficiency and automation reliability), 21 male participants completed simulated flight tasks within local airport traffic patterns. Participants were teamed up with a system which took over the flight control priority based on task requirements and cognitive states. Experimental data was analyzed by statistical tests on significance and effect sizes. Results indicated that automation reliability influenced subjective and objective measures more than task proficiency, while task proficiency partly compensated the loss of performance due to deteriorated automation reliability. Findings support that the pilot-centered design principle should still be adhered to for Pilot-ePilot teaming in intelligent aircraft cockpits.

IMPROVEMENT OF DESIGN PARAMETERS OF INERTIAL TYPE FUEL CONTINUITY SYSTEMS

Multiple restart of liquid-propellant rocket engines of spacecraft in zero gravity is one of the most difficult tasks that arise in the creation of new products of rocket technology. When performing a flight task, due to the movement of the spacecraft along the passive section of the trajectory, the components of the liquid fuel are mixed with the pressurized gas in its tanks in an arbitrary manner. There is a possibility that the gas phase will be located close to the drain hole. At the time of re-turning on the propulsion engines of the spacecraft, along with fuel, gas bubbles will enter the consumable line from the tank, which can, in turn, cause a failure of their launch.
 In order to avoid this emergency, special fuel continuity systems are used. Despite the significant diversity of these systems, all of them have significant shortcomings and limited working conditions. The main purpose of using fuel continuity systems of any type is to prevent the penetration of boost gas from the tank cavity into the flow line until the tank is completely emptied.
 The paper is devoted to the analysis of the possibilities of improving the design parameters of the two most common systems for ensuring fuel continuity by creating a combined "hybrid" system on their basis. Inertial and mesh systems for ensuring fuel continuity are considered. Theoretically, an analysis of the possibility of reducing the effect of pre-start acceleration for an inertial system for ensuring fuel continuity due to the use of a mesh phase separator in the fuel supply system is carried out. The paper considers the main design parameters of this combined system, which directly affect the level of its technical perfection, as well as the possibilities of their optimization.
 The results of the work can be useful in engineering practice in the creation of new promising products of rocket and space technology.

Attentional Tunneling in Pilots During a Visual Tracking Task With a Head Mounted Display.

We examined whether active head aiming with a Helmet Mounted Display (HMD) can draw the pilot's attention away from a primary flight task. Furthermore, we examined whether visual clutter increases this effect. Head up display symbology can result in attentional tunneling, and clutter makes it difficult to identify objects. Eighteen military pilots had to simultaneously perform an attitude control task while flying in clouds and a head aiming task in a fixed-base flight simulator. The former consisted of manual compensation for roll disturbances of the aircraft, while the latter consisted of keeping a moving visual target inside a small or large head-referenced circle. A "no head aiming" condition served as a baseline. Furthermore, all conditions were performed with or without visual clutter. Head aiming led to deterioration of the attitude control task performance and an increase of the amount of roll-reversal errors (RREs). This was even the case when head aiming required minimal effort. Head aiming accuracy was significantly lower when the roll disturbances in the attitude control task were large compared to when they were small. Visual clutter had no effect on both tasks. We suggest that active head aiming of HMD symbology can cause attentional tunneling, as expressed by an increased number of RREs and less accuracy on a simultaneously performed attitude control task. This study improves our understanding in the perceptual and cognitive effects of (military) HMDs, and has implications for operational use and possibly (re)design of HMDs.

Development and Proof of Concept of a Predictive Model of Flight Deck Cognitive Workload

Abstract: Workload can be regarded as the “cost” (in information processing terms) of performing a given flight task. Predictive models of workload are valuable at the early stages of system design to identify potentially excessive demands on the human operator before major system development commences. This paper describes the early development of a task-based predictive workload method. It utilizes a scenario-based timeline analysis supplemented by characterizations of the cognitive nature of the pilot’s tasks using Wickens’ multiple resource theory, followed by the identification of likely task conflicts and estimates of the influence of the task environment. Workload predictions were made for a manual approach, landing and roll-out scenario in a modern airliner. Predictions were compared against corresponding Bedford scale workload estimates. Results show that the predictive workload technique produced comparable estimates of cognitive workload measured using the Bedford scale.