Simulated Unmanned Aerial Vehicle Research Articles

An Adaptive Missing Data Restoration Method for UAV Confrontation Based on Deep Regression Model

Completing missions with autonomous decision-making unmanned aerial vehicles (UAV) is a development direction for future battlefields. UAV make decisions based on battlefield situation information collected by sensors and can quickly and accurately perform complex tasks such as path planning, cooperative reconnaissance, cooperative pursuit and attacks. Obtaining real-time situation information of enemy is the basis for realizing autonomous decision-making of the UAV. However, in practice, due to internal sensor failure or interference of enemy, the acquired situation information is prone to be missing, which affects the training and decision-making of autonomous UAV. In this paper, an adaptive missing situation data restoration method for UAV confrontation is proposed. The UAV confrontation situation data are acquired through JSBSim, an open-source UAV simulation platform. By fusing temporal convolutional network and long short-term memory sequences, we establish a deep regression method for missing data restoration and introduce an adaptive mechanism to reduce the training time of the restoration model in response to dynamic changes in the enemy’s strategy during UAV confrontation. In addition, we evaluate the reliability of the proposed method by comparing with different baseline models under different degrees of data missing conditions. The performance of our method is quantified by five metrics. The performance of our proposed method is better than the other benchmark algorithms. The experimental results show that the proposed method can solve the missing data restoration problem and provide reliable situation data while effectively reducing the training time of the restoration model.

Structural Design and Flight Simulation of Firefighting and Rescue UAV Based on Coaxial Dual Rotors

The technology of firefighting unmanned aerial vehicles (UAVs) used in fire scene reconnaissance, water disaster investigation, and rescue missions, has become a crucial asset in responding to fires and emergencies. The key to this technology is to swiftly and effectively improve the operational efficiency of UAVs. Based on the premise of rapid and effective rescue, the stability, tracking, and safety of hexacopter UAVs technology improved the design and optimization innovative design of the storage and transportation convenience structure of unmanned aerial vehicles were carried out. An innovative high-pressure ejection mechanism for fire bombs (delivery items) has been designed. So, a portable, efficient, and fast unmanned aerial vehicle structure was designed. The PX4 flight control system was used to conduct simulation experiments on the newly designed UAV, and it was found that the structural design was reasonable, and the UAV was operated smoothly and accurately during the simulation flight. This structural design and simulation analysis can provide new ideas for the design of new fire-fighting equipment.

Effects of secondary task eccentricity and visual salience on attention allocation in multitasking across screens

In digital control rooms, operators often need to monitor multiple information sources, which are spatially distributed on multiple screens, to manage multiple tasks simultaneously. Operators’ attention allocation between tasks are likely to be affected by the visual salience and eccentricity of secondary task screens. Their exact impacts and underlying mechanisms need to be investigated to inform system design to support balanced attention allocation strategies for optimal multitasking performance. This study aims to address these issues through a laboratory experiment using a simulated unmanned aerial vehicle monitoring platform. The participants performed a primary task and two secondary tasks concurrently, each shown on a separated screen. The visual salience and eccentricity of the maintenance task (one of the secondary tasks) were manipulated within-groups. Attention allocation behaviors were captured by eye movement data, and performance of both primary and secondary tasks were recorded. The participants were more likely to switch to and spent more time on the maintenance task of lower salience, showing a tendency to override the bottom-up influence of secondary task salience with top-down control. Large eccentricity of the secondary task, however, led to less attention allocation and lower task prioritization in paired event conflicts. The interaction effect of the salience and eccentricity of the maintenance task was significant for the accuracy of the primary task and the other secondary task. These findings offered insights into the joint design of salience and eccentricity of secondary tasks to enhance overall multitasking performance.

Gaze Sharing, a Double-Edged Sword: Examining the Effect of Real-Time Gaze Sharing Visualizations on Team Performance and Situation Awareness.

The goal of this study was to assess how different real-time gaze sharing visualization techniques affect eye tracking metrics, workload, team situation awareness (TSA), and team performance. Gaze sharing is a real-time visualization technique that allows teams to know where their team members are looking on a shared display. Gaze sharing visualization techniques are a promising means to improve collaborative performance on simple tasks; however, there needs to be validation of gaze sharing with more complex and dynamic tasks. This study evaluated the effect of gaze sharing on eye tracking metrics, workload, team SA, and team performance in a simulated unmanned aerial vehicle (UAV) command-and-control task. Thirty-five teams of two performed UAV tasks under three conditions: one with no gaze sharing and two with gaze sharing. Gaze sharing was presented using a fixation dot (i.e., a translucent colored dot) and a fixation trail (i.e., a trail of the most recent fixations). The results showed that the fixation trail significantly reduced saccadic activity, lowered workload, supported team SA at all levels, and improved performance compared to no gaze sharing; however, the fixation dot had the opposite effect on performance and SA. In fact, having no gaze sharing outperformed the fixation dot. Participants also preferred the fixation trail for its visibility and ability to track and monitor the history of their partner's gaze. The results showed that gaze sharing has the potential to support collaboration, but its effectiveness depends highly on the design and context of use. The findings suggest that gaze sharing visualization techniques, like the fixation trail, have the potential to improve teamwork in complex UAV tasks and could have broader applicability in a variety of collaborative settings.

Integrated monitoring and prediction of thermal discharge from nuclear power plants using satellite, UAV, and numerical simulation.

Global nuclear power is surging ahead in its quest for global carbon neutrality, eyeing an anticipated installed capacity of 436 GW for coastal nuclear power plants by 2040. As these plants operate, they emit substantial amounts of warm water into the ocean, known as thermal discharge, to regulate the temperature of their nuclear reactors. This discharge has the potential to elevate the temperature of the surrounding seawater, potentially influencing the marine ecosystem in the discharge vicinity. Therefore, our study area is on the Qinshan and Jinqimen Nuclear Power Plants in China, employing a blend of Landsat 8/9, and unmanned aerial vehicle (UAV) imagery to gather sea surface temperature (SST) data. In situ measurements validate the temperature data procured through remote sensing. Leveraging these SST observations alongside hydrodynamic and meteorological data from field measurements, we input them into the MIKE 3 model to prognosticate the three-dimensional (3D) spatial distribution and temperature elevation resulting from thermal discharge. The findings reveal that (1) satellite remote sensing can instantly acquire the horizontal distribution of thermal discharge, but with a spatial resolution much lower than that of UAV. The spatial resolution of UAV is higher, but the imaging efficiency of UAV is only 1/40,000 of that of satellite remote sensing. (2) Numerical simulation models can predict the 3D spatial distribution of thermal discharge. Although UAV and satellite remote sensing cannot directly obtain the 3D spatial distribution of thermal discharge, using remotely sensed SST as the temperature field input for the MIKE 3 model can reduce the quantity of measured temperature data and lower the cost of numerical simulation. (3) In the process of monitoring and predicting the thermal discharge of nuclear power plants, achieving an effective balance between monitoring accuracy and cost can be realized by comprehensively considering the advantages and costs of satellite, UAV, and numerical simulation technologies.

Modelling and simulation of quadcopter UAV based on PID control

In the evolving field of aerial robotics, the precise control and stabilization of Unmanned Aerial Vehicles (UAVs), particularly quadrotors, stand as critical challenges due to their inherent nonlinear dynamics and susceptibility to external disturbances. This study embarks on addressing these challenges by first establishing a comprehensive mathematical model of a quadrotor's dynamics utilizing the Newton-Euler formulation. This foundational model serves as the basis for implementing a Proportional-Integral-Derivative (PID) control strategy, aimed at maintaining the UAV's desired flight trajectory and enhancing its stability against environmental perturbations. The effectiveness of the PID control approach is meticulously evaluated through a series of simulation tests conducted within the MATLAB/Simulink environment, focusing on the quadrotor's pitch, roll, and yaw control channels. The outcomes of these simulations provide convincing evidence of the PID scheme's ability to ensure rapid response times and precise attitude regulation, underpinning its viability for robust quadrotor UAV operations across diverse operational scenarios. Moreover, this research not only establishes a critical step towards the autonomous operation of UAVs but also accentuates the significance of sophisticated control mechanisms in the advancement of aerial robotics technology.

Brain–Computer Interface Based on Motor Imagery With Visual Guidance and its Application in Control of Simulated Unmanned Aerial Vehicle

Brain–Computer Interface Based on Motor Imagery With Visual Guidance and its Application in Control of Simulated Unmanned Aerial Vehicle

Enhancing multi-UAV air combat decision making via hierarchical reinforcement learning

In the realm of air combat, autonomous decision-making in regard to Unmanned Aerial Vehicle (UAV) has emerged as a critical force. However, prevailing autonomous decision-making algorithms in this domain predominantly rely on rule-based methods, proving challenging to design and implement optimal solutions in complex multi-UAV combat environments. This paper proposes a novel approach to multi-UAV air combat decision-making utilizing hierarchical reinforcement learning. First, a hierarchical decision-making network is designed based on tactical action types to streamline the complexity of the maneuver decision-making space. Second, the high-quality combat experience gained from training is decomposed, with the aim of augmenting the quantity of valuable experiences and alleviating the intricacies of strategy learning. Finally, the performance of the algorithm is validated using the advanced UAV simulation platform JSBSim. Through comparisons with various baseline algorithms, our experiments demonstrate the superior performance of the proposed method in both even and disadvantaged air combat environments.

A message verification scheme based on physical layer-enabled data hiding for flying ad hoc network

The use of Unmanned Aerial Vehicles (UAVs) for military as well as civilian applications such as search and rescue operations, disaster management, parcel delivery and agriculture, is becoming increasingly popular, mainly due to their versatile nature and relatively inexpensive operating costs. However, one of the most significant barriers in the widespread adoption of UAVs for the aforementioned applications is the increasing concern for wireless communication security within the network. Unfortunately, the nodes that comprise these networks are extremely constrained in terms of storage space, processing power and battery life, which require light-weight security protocols that align with these limitations. The current standards for message authentication and verification predominantly rely on lightweight cryptographic techniques. However, there is continual scope for improvement, particularly in the realm of computational efficiency, to better align with the constraints inherent to UAVs. This paper explores data hiding in a multi-UAV environment, to form a light-weight message verification scheme to confirm the identity of the transmitting node. First, five venues having the potential to hide data are analyzed. Based on the analysis of bit error rate obtained from the simulated multi-UAV environment, both cyclic prefix and padding bits are selected. Next, a codeword is generated for each transmission and it is embedded along with the unique ID key, κ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\kappa $$\\end{document} of the transmitting node into each venue respectively, and the output is transmitted as Hello packets. Subsequently, the receiving node extracts and decodes the codeword in order to verify the authenticity of the transmitting node. An evaluation of the proposed scheme has been conducted by using a simulated UAV environment. In the best case scenario, the proposed authentication scheme can achieve a successful verification rate of at least 80% at a maximum hop-count of 10 hops. Through computational cost analysis, in comparison to the conventional methods, the proposed scheme was found to have a significantly lower total execution time of 1.7μs\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$1.7\\mu s$$\\end{document}. In addition, the security analysis shows that when the proposed scheme is paired with physical unclonable functions (PUFs), it is able to resist common security attacks, including man-in-the-middle, replay and cloning attacks.

A case study on the modeling and simulation of UAVs

The current work presents the flow and structural analysis of the application design in un-manned aerial vehicles (UAVs) as well as indicates a case of the modeling and simulation study with the ANSYS Fluent and Mechanical programs. This research reveals the unmanned aerial vehicle’s structural and mechanical design, structure configurations, energy-flow and struc-tural analysis, propulsion and firing systems, prototype production and testing, and design flow models. This study aims to complete the unmanned aerial vehicle design by determining its aerodynamic configurations. Due to the complexity of the design, a preliminary prepa-ration for flow analysis is performed with simplified geometry as well as flow analysis. The unmanned aerial vehicle is tested at different velocities by numerical analysis. In addition, different density flow analyses provide predictions about the aerodynamic forces of the UAVs at different heights and temperatures. The thrust results are 4240 g, power became 1711.62 W with 2.48 g/W efficiency, and 12179 [rpm] revolution for 22.2 V voltage and 77.1 A current, respectively. The 5 different analyses are performed in the range of 2.9-12 million elements, and the solution meshes with the lowest number of elements by performing parametric stud-ies with the ANSYS program that gives the most accurate result.

Aerodynamic Interference Analysis for a Nonoverlapping Multirotor UAV Based on Dynamic Vortex Tube

With the wide application of unmanned aerial vehicles, interest in multirotor conifgurations has increased. The unique features of multirotor con guration have been intensely investigated, including aerodynamic interference, which is particularly important because it in uences the vibration, noise, and handling quality of rotorcraft. Most previous studies have used high-fidelity approaches, such as computational fluid dynamics to identify such interference. However, such an approach is inappropriate for real-time flight simulations. In this study, an improved aerodynamic interference analysis based on a dynamic vortex tube was established for performance prediction in real-time ight simulation. A simple and effective formulation is proposed for integration with rotor aerodynamics to evaluate the interference of multirotor con gurations. The present analysis is validated on various multirotor configurations. An investigation of interference in a multirotor unmanned aerial vehicle (UAV) is then presented. The analysis results exhibit good agreement with experimental results and high-fidelity predictions. Although the accuracy of the proposed analysis is lower than that of experimental studies and high-fidelity analyses, it is suf cient for capturing interference trends. The proposed analysis can account for aerodynamic interference in the flight simulation of a multirotor UAV.

Dynamics simulation of folding wing UAVs launched from a high-altitude balloon platform

It is a new application of the high-altitude balloon system to launch multiple small folding wing unmanned aerial vehicles (UAVs). Based on the design of the launching system, we conduct numerical simulations of the ejection process from the balloon platform and the subsequent UAV wing deployment with trajectory leveling operation. We use the Kane method to establish the dynamic model of the balloon launching platform in the inertial coordinate system (ICS). By introducing the degree of freedom of UAV sliding along the sliding track, we realize the dynamic simulation of eight UAV launching processes and obtain the instantaneous separated states. The folding wing UAV first deploys its wings after ejection, which has the coupling characteristics between structural deformation and attitude adjustment. It is regarded as a multi-rigid body connecting structure composed of the fuselage and four wings. We establish the dynamic simulation model by the Kane method in the UAV body coordinate system (BCS). The Radau pseudo-spectral method is used to calculate the flight trajectory of each UAV. This paper is an engineering application research and can provide a simulation reference for the launching test of the balloon-borne folding wing UAV program.

Modelling, control and simulation of a single rotor UAV with swashplateless torque modulation

Single rotor-base unmanned aerial vehicles (UAVs) represent the most minimalistic design to implement an aerial vehicle. We describe here a mono-rotor UAV, also named mono-spinner, that uses torque modulation (also called swashplateless mechanism) to provide roll and pitch control without the need of a complicated swashplate mechanism. A full non-linear model of the mono-spinner based on the Euler-Poincaré theoretical framework is given including a description of the structure of the drone and its rotor control system. Mechanical properties, kinetic energy and all of the external forces of interest to derive the equations of motion are described. A novel, Lyapunov-stable, quaternion-based nonlinear control law based on the decomposition of the orientation quaternion into a spin component and a reduced orientation component is provided. Finally, simulation results based on parameters measured from a real implementation of a mono-spinner are produced.

Adaptive attitude control of UAV based on neural network compensation

Aiming at the problem of unmanned aerial vehicle (UAV) attitude control with insufficient robustness and anti-disturbance, this paper proposes a UAV attitude control method combining incremental nonlinear dynamic inverse (INDI) and neural network (NN) adaptive compensation. Firstly, the attitude model of UAV with time scale separation is proposed. On this basis, the INDI control law is derived, and the angular acceleration information required by INDI method is feedback by the filter. Then, the adaptive NN compensation structure is introduced to eliminate the uncertainty caused by model mismatch, sensor delay and external disturbance. Finally, the UAV simulation is carried out. The result shows that this method improves robustness and anti-disturbance of original INDI method without changing structure and parameters of controller, and has a certain adaptive compensation ability for system uncertainty.

MARSIM: A Light-Weight Point-Realistic Simulator for LiDAR-Based UAVs

The emergence of low-cost, small form factor and light-weight solid-state LiDAR sensors have brought new opportunities for autonomous unmanned aerial vehicles (UAVs) by advancing navigation safety and computation efficiency. Yet the successful developments of LiDAR-based UAVs must rely on extensive simulations. Existing simulators can hardly perform simulations of real-world environments due to the requirements of dense mesh maps that are difficult to obtain. In this paper, we develop a point-realistic simulator of real-world scenes for LiDAR-based UAVs. The key idea is the underlying point rendering method, where we construct a depth image directly from the point cloud map and interpolate it to obtain realistic LiDAR point measurements. Our developed simulator is able to run on a light-weight computing platform and supports the simulation of LiDARs with different resolution and scanning patterns (e.g., spinning LiDARs and solid-state LiDARs), dynamic obstacles, and multi-UAV systems. Developed in the ROS framework, the simulator can easily communicate with other key modules of an autonomous robot, such as perception, state estimation, planning, and control. Finally, the simulator provides 10 high-resolution point cloud maps of various real-world environments, including forests of different densities, historic building, office, parking garage, and various complex indoor environments. These realistic maps provide diverse testing scenarios for an autonomous UAV. Evaluation results show that the developed simulator achieves superior performance in terms of time and memory consumption against Gazebo and that the simulated UAV flights highly match the actual one in real-world environments. We believe such a point-realistic and light-weight simulator is crucial to bridge the gap between UAV simulation and experiments and will significantly facilitate the research of LiDAR-based autonomous UAVs in the future.

Correction of Error of Airborne Anemometers Caused by Self-Excited Air Turbulence.

An airborne anemometer, which monitors wind on the basis of Meteorological Multi-rotor UAVs (Unmanned Aerial Vehicles), is important for the prevention of catastrophe. However, its performance will be affected by the self-excited air turbulence generated by UAV rotors. In this paper, for the purpose of the correction of an error, we developed a method for the elimination of the influence of air turbulence on wind speed measurement. The corresponding correction model is obtained according to the CFD (Computational Fluid Dynamics) simulation of a six-rotor UAV which is carried out with the sliding grid method and the S-A turbulence model. Then, the model is applied to the developed prototype by adding the angle of attack compensation model of the airborne anemometer. It is shown by the actual application that the airborne anemometer can maintain the original measurement accuracy at different ascent speeds.

Landslide surface horizontal displacement monitoring based on image recognition technology and computer vision

The deformation and displacement of landslides has always been the priority of landslide monitoring and early warning. With the development of image processing technology in recent years, the analysis of landslide surface deformation through image data has become a popular method and different methods of image analysis are rapidly increasing. However, the current method of analyzing landslide deformation and displacement using a single image processing method tends to analyze the displacement of the local deformation area of the landslide, which lacks overall monitoring of the slope and is difficult to use for pre-disaster monitoring of landslide hazards, easily leading to missed detection of landslide potential hazard points. Therefore, this paper combines image recognition technology and computer vision (IR-CV) to propose a method for monitoring and identifying the deformation displacement of landslide surfaces, which has been validated by indoor landslide model tests and unmanned aerial vehicle field simulation displacement recognition tests. In the model tests, the IR-CV method successfully identified the displacements generated during the deformation and damage of the landslide model while using the model deformation time series image data recorded by the camera to analyze the overall deformation of the surface of the landslide model. Further, in this paper, a field simulation displacement identification test was conducted using an unmanned aerial vehicle (UAV), and the orthophoto data obtained by the UAV verified that the IR-CV method can be used for overall deformation displacement identification of landslides in the field. The results of indoor model tests and field UAV simulation tests show that the monitoring method of IR-CV can effectively identify the deformation and displacement of landslide surfaces, which has good application prospects in pre-disaster monitoring and displacement identification of landslides, thus providing a cost-effective and feasible solution for monitoring the deformation and displacement of landslide surfaces.

The Mediating Effect of Emotions on Trust in the Context of Automated System Usage

Safety-critical systems are often equipped with warning mechanisms to alert users regarding imminent system failures. However, they can suffer from false alarms, and affect users emotions and trust in the system negatively. While providing feedback could be an effective way to calibrate trust under such scenarios, the effects of feedback and warning reliability on users emotions, trust, and compliance behavior is not clear. This paper investigates this by designing a 2 (feedback: present/absent) 2 (warning reliability: high/low) 4 (sessions) mixed design study where participants interacted with a simulated unmanned aerial vehicle (UAV) system to identify and neutralize enemy targets. Results indicated that feedback containing both correctness and affective components decreased users positive emotions and trust in the system, and increased loneliness and hostility (negative) emotions. Emotions were found to mediate the relationship between feedback and trust. Implications of our findings for designing feedback and calibration of trust are discussed in the paper.

An image-based visual servoing control method for UAVs based on fuzzy logic

Visual servoing is a method to achieve precise positioning and motion control of objects by visual feedback, and it is widely applied in the fields of robotics and unmanned aerial vehicles (UAVs) in recent years. This paper presents a novel image-based visual servoing (IBVS) control method for UAVs based on fuzzy logic to effectively solve the problem under field of view constraint and improve the control efficiency. In this paper, a fuzzy logic of servo gain is designed for the control input of visual servoing, which solves the problem of feature loss in IBVS and improves the efficiency. Meanwhile, a deep computing method based on known data is proposed to solve the unknown depth of Jacobian matrix, which makes the control easier to converge. The effectiveness of the proposed method is verified by the simulation of a quadrotor UAV equipped with a monocular camera.

Task-independent auditory probes reveal changes in mental workload during simulated quadrotor UAV training.

The event-related potential (ERP) methods based on laboratory control scenes have been widely used to measure the level of mental workload during operational tasks. In this study, both task difficulty and test time were considered. Auditory probes (ignored task-irrelevant background sounds) were used to explore the changes in mental workload of unmanned aerial vehicle (UAV) operators during task execution and their ERP representations. 51 students participated in a 10-day training and test of simulated quadrotor UAV. During the experiment, background sound was played to induce ERP according to the requirements of oddball paradigm, and the relationship between mental workload and the amplitudes of N200 and P300 in ERP was explored. Our study shows that the mental workload during operational task training is multi-dimensional, and its changes are affected by bottom-up perception and top-down cognition. The N200 component of the ERP evoked by the auditory probe corresponds to the bottom-up perceptual part; while the P300 component corresponds to the top-down cognitive part, which is positively correlated with the improvement of skill level. This paper describes the relationship between ERP induced by auditory probes and mental workload from the perspective of multi-resource theory and human information processing. This suggests that the auditory probe can be used to reveal the mental workload during the training of operational tasks, which not only provides a possible reference for measuring the mental workload, but also provides a possibility for identifying the development of the operator's skill level and evaluating the training effect.