Simulated Unmanned Aerial Vehicle Research Articles

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

AbstractThe 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, $$\kappa $$ κ 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 $$1.7\mu s$$ 1.7 μ s . 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.

Missing data reconstruction in attitude for quadrotor unmanned aerial vehicle based on deep regression model with different sensor failures

Forming a UAV cluster with multiple UAVs to collaborate in accomplishing tasks is the future development of battlefield. The UAV cluster can collaborate by communicating through inter-aircraft links and can quickly and accurately perform complex tasks such as path planning, collaborative reconnaissance, collaborative sensing and attack. In the process of Unmanned Aerial Vehicle (UAV) cooperative operation, obtaining the real-time attitude information of each UAV is preliminary to implement collaborative cooperation. However, in practice, due to the internal malfunction of sensors and mutual interference among UAVs, the acquired attitudes often suffer from data missing. This paper proposed a novel missing data reconstruction method for UAV’s attitude in the case of sensor failures. The attitude data of UAV is obtained through an advanced UAV simulation platform AirSim. Through fusing the temporal convolutional network (TCN) and Bi-directional long short-term memory (Bi-LSTM), a deep regression framework is established for missing data construction. In addition, we evaluate the reliability of the proposed method by comparing with different baseline models and different combinations of data missing. The performance of our method is quantified using metrics of root-mean-square-error (RMSE), mean absolute error (MAE), determination coefficient (DC), mean absolute percentage error (MAPE), and symmetric mean absolute percentage error (SMAPE) score. The averaged performance values of our proposed method outperform other baseline algorithms. The results of the experiment show that the study could well-address the issue of missing data reconstruction and provide reliable attitude data.

Attitude tracking control design of fixed-wing UAVs having uncertain dynamics and corrupted gyro sensor outputs

Flight performance of unmanned aerial vehicles (UAVs) strongly depends on implemented attitude tracking control. For designing better controllers, nonlinear control design techniques are often opted instead of control design based on linearized models. Uncertainty in nonlinear dynamics estimation may arise due to inaccuracies in aerodynamic derivatives and simplifications/assumptions made during the derivation of nonlinear models. This paper considers attitude tracking control of fixed-wing UAVs having uncertain dynamics and corrupted gyro sensor outputs. An integral chain differentiator (ICD) is used to provide the analytical redundancy to the gyros used to measure the angular rates. Two control design schemes are proposed, a neuro-fuzzy adaptive sliding mode control (NFASMC) and an ICD approximation-based fuzzy adaptive sliding mode control (ICD-FASMC). In NFASMC, the uncertain part of the dynamics is estimated using an adaptive radial basis function neural network. Gyro sensor output errors are estimated in real-time, using ICD based error estimation scheme and used in the control law along with the sensor’s corrupted outputs. In ICD-FASMC, the uncertain dynamics and angular rates of UAV are estimated using the ICD such that the requirement of the gyro sensor outputs for control design is bypassed. The switching gain of the designed controllers is made adaptive using fuzzy logic to mitigate the chattering effect. The stability of the proposed controllers is proved using the Lyapunov approach. The proposed schemes are implemented using a nonlinear simulation of a fixed-wing UAV. Simulation results are presented to show the effectiveness of the proposed techniques.

Mapping soil available copper content in the mine tailings pond with combined simulated annealing deep neural network and UAV hyperspectral images

Improper discharge of slag from mining will pollute the surrounding soil, thereby affecting the ecology and becoming an important global problem. The available copper (ACu) content in polluted soil is an important factor affecting plant growth and development. When investigating a large area of soil with ACu, manual sampling by points and inspection are mainly used, due to the heterogeneity of soil, the efficiency and accuracy are lower. The Unmanned aerial vehicle (UAV) equipped with a hyperspectral sensor as a remote sensing technology is widely used in soil indicator monitoring because of its rapid and convenience. Meanwhile, using the relationship between soil organic matter and available copper has the potential to predict available copper. In this study, we selected the study area with tailings area in the Jianghan Plain of China and used a UAV equipped with a hyperspectral sensor to predict ACu and soil organic matter (SOM) in the soil with two datasets. Firstly, 74 soil samples were collected in the study area, and the ACu and SOM of the soil samples were determined. Second, a hyperspectral image of the study area is obtained using a UAV equipped with a hyperspectral sensor. Thirdly, we combine hyperspectral data with competitive adaptive reweighted sampling (CARS) to obtain feature bands and utilize simulated annealing deep neural network (SA-DNN) to generate estimation models. Finally, maps of the distribution of ACu and SOM in the area were generated using the model. In two datasets, the model of ACu with R2 values both are 0.89, and R2 on the model of SOM is 0.89 and 0.88. The results show that the combination of UAV hyperspectral imagery with the SA-DNN model has good performance in the prediction of organic matter and available copper, which is helpful for soil environmental monitoring.

Development of an MUM-T Integrated Simulation Platform

In this paper, we present a platform that can simulate Manned-UnManned Teaming (MUM-T) situations using various hardware and software being integrated. MUM-T, which combines unmanned aerial vehicles (UAVs) with an existing manned vehicle (MV), is growing into an indispensable strategy thanks to enormous advantages it provides. In order to test this indispensable strategy which involves flight control algorithms being applied for various MUM-T situations, it is certainly needed to run and evaluate three-dimensional (3D) MV-UAVs combined simulations in realistic environments. However, there is no such simulator that enables such 3D MUM-T simulations as well as the Human Interface (HI) for both MV pilot and the mission manager at a ground station. In this paper, we propose an MUM-T integrated simulation platform (MUM-T ISP) that combines a single full-scale helicopter simulator (for MV simulation) and multiple Iris/PX4-Autopilot (for UAVs simulation), along with specially developed HI devices (TM and TV). Tactical Monitor (TM), one of the HI devices, is a touchable tablet which allows the MV pilot to interact with UAVs by simple actions (e.g. touching a button), and the other HI device called Tactical Viewer (TV) allows the mission manager at a ground station to closely monitor the MUM-T situation via realistic 3D displays of MV and UAVs. The proposed MUM-T ISP is tested on several mission scenarios and then evaluated by actual pilots and the mission manager based on sensible criteria. It is expected that the proposed simulation platform may serve well as a training tool for prospective MV pilots working with UAVs, and also as a research tool for flight engineers developing control algorithms that make MV and UAVs work together.