Operation Of Unmanned Aerial Vehicles Research Articles

Finite time fault tolerant tracking control for multiple unmanned aerial vehicles with actuator faults

SummaryAn adaptive fast nonsingular integral terminal sliding mode control scheme is proposed for the distributed formation control problem of multiple unmanned aerial vehicles (UAVs) affected by actuator faults and external disturbances. In order to achieve a good cooperative tracking performance of UAVs during mission execution, the adverse effect of the lumped disturbances composed of actuator failures, wind and vortex disturbances is estimated by the cerebellar model articulation neural network, meanwhile the tangent function is introduced to eliminate the chattering of the sliding mode. Then the analysis of superior convergence performance of the fast nonsingular integral terminal sliding manifold is implemented, and the finite time stability of the closed‐loop formation flight control system is proved. Finally, the simulation experiments verify that the proposed approach is superior to the nonsingular integral terminal sliding mode control for the formation control system of faulty multiple UAVs.

Learning to Optimise a Swarm of UAVs

The use of Unmanned Aerial Vehicles (UAVs) has shown a drastic increase in interest in the past few years. Current applications mainly depend on single UAV operations, which face critical limitations such as mission range or resilience. Using several autonomous UAVs as a swarm is a promising approach to overcome these. However, designing an efficient swarm is a challenging task, since its global behaviour emerges solely from local decisions and interactions. These properties make classical multirobot design techniques not applicable, while evolutionary swarm robotics is typically limited to a single use case. This work, thus, proposes an automated swarming algorithm design approach, and more precisely, a generative hyper-heuristic relying on multi-objective reinforcement learning, that permits us to obtain not only efficient but also reusable swarming behaviours. Experimental results on a three-objective variant of the Coverage of a Connected UAV Swarm problem demonstrate that it not only permits one to generate swarming heuristics that outperform the state-of-the-art in terms of coverage by a swarm of UAVs but also provides high stability. Indeed, it is empirically demonstrated that the model trained on a certain class of instances generates heuristics and is capable of performing well on instances with a different size or swarm density.

MALE UAV selection in interval Type-2 fuzzy sets environment

Unmanned Aerial Vehicles (UAVs) are increasingly used in the military field. Especially in recent years, UAVs have been a very effective instrument in gaining airspace superiority and military success. Many countries compete with each other to develop better UAV technology or improve the technical features of UAVs. Therefore, it is critical to determine which UAV has the best performance, considering technical and operational characteristics, because the vehicles with more advanced performance can provide countries with strategic superiority. The purpose of this study is to investigate the technical, cost, and operational performance of Medium Altitude Long Endurance UAVs (MALE UAVs). In the study, as a result of a wide literature review, we determined a performance criterion for this type of vehicle. The model presented here uses an Interval Type-2 Fuzzy Analytical Hierarch Process (IT2FAHP) and an Interval Type-2 Fuzzy Technique for Order of Preference by Similarity to an Ideal Solution (IT2FTOPSIS) hybrid method. The findings indicate that some MALE UAVs have superior technical and operational performance over others and demonstrate that range, max take-off weight, and payload are important criteria in determining the performance and superiority of these vehicles.

Energy Efficient Task Caching and Offloading in UAV-Enabled Crowd Management

Unmanned aerial vehicle(UAV)-enabled mobile edge computing(MEC) networks provide ubiquitous communication and computing capacity for mobile users compared with terrestrial networks. In crowd management, the UAV base station(UAV-BS) collect computation tasks from the Internet of Things (IoT) devices and process tasks with terrestrial MEC networks cooperatively. However, energy efficiency(EE) and user mobility are the bottlenecks of UAV performance. Therefore, it is crucial to maximizing the energy efficiency(EE) of UAVs. In this paper, we propose an energy-efficient UAV-enabled MEC network composed of IoT devices, the UAV-BS, edge cloud, and the data center, and propose a Green-UAV-CoCaCo algorithm to jointly optimize communications, caching, and computation for EE of UAV. Specifically, we design a UAV trajectory model based on a greedy algorithm to predict the user’s coordinates and choose the proper edge server for task offloading. Then, the UAV-CoCaCo algorithm is proposed to maximize the EE of the task caching and offloading. Simulation results demonstrate the effectiveness of the proposed algorithm.

The Study of Electrical Energy Power Supply System for UAVs Based on the Energy Storage Technology

Unmanned aerial vehicles (UAVs) are increasingly attracting investment and development attention from many countries all over the world due to their great advantages. However, one of the biggest challenges for researchers is the problem of supplying energy to UAVs to ensure they can operate for a longer time. Especially in the case of rotary wings, they consume more energy than other UAV types as the motors need to spend a lot of energy to operate in order to overcome the gravity of the earth. The article aims to research power supply, energy consumption on UAVs, and a method of taking advantage of external energy sources to provide power for the operation of UAVs and discuss UAVs’ structure, categories, and control. Two experiments were conducted separately to evaluate the energy consumption of UAVs and the energy conversion from external energy sources to electrical energy. A test bench was designed to evaluate and determine the maximum efficiency using regenerative braking mode. The measuring device was manufactured to measure the necessary parameters to calculate the energy consumption and performance of the system. Experimental numerical results show that energy conversion from external sources is one of methods that can help increase the flight time of the UAV.

Topical issues of personnel training in the field of unmanned aircraft systems

The legal regulation, and hence, the training system in the field of unmanned aircraft systems (UAS) in the Russian Federation, the European Union and the United States is based on the unmanned aerial vehicles (UAV) rating with respect to UAS maximum take-off weight (MTOW) and their purpose (method of use). In this regard, small-unmanned aircraft (sUAS) are identified – in our country up to 30 kg, in the EU and the USA up to 55 lbs (25 kg) and UAV with larger weight. In the USA and Europe, the training of remote pilots for sUAS is differentiated based on the degree of risk that UAV can represent for public safety. Thus, the training of remote pilots to use UAVs with MTOW less than 25 kg (55 lbs) in a sparsely populated area during daylight hours under the conditions of visual range is conducted in the online format, the result of which is taking tests. In the United States and Europe, the UAV application with MTOW more than 25 kg (55 lbs) or performing UAV operations, presenting a potential risk for public safety, requires more comprehensive and long-term training of remote pilots. In the Russian Federation, UAS personnel training is conducted in educational organizations according to different programs, which vary significantly depending on a specific type of aviation UAVs refers to: State, Civil or Experimental. UAS personnel training programs for various aviation types are not harmonized, which leads to the failure to credit previously received education in training to perform activities in another aviation type. The article describes the analysis results of the international and national experience, perspectives for the development of the UAS personnel training system, as well as formulates the proposals concerning further development of the national system for UAS specialists training.

U-Space and UTM Deployment as an Opportunity for More Complex UAV Operations Including UAV Medical Transport.

Unmanned Aerial Vehicles (UAVs) or Unmanned Aircraft Systems (UASs) commonly called drones are relatively new entrants to the airspace. The regulatory agencies, numerous States and entities are involved in creation of the safe integration with manned aviation. The so-called U-space concept announced by the European Commission is one of the approaches to achieve that goal. There is also known concept of Unmanned Traffic Management (UTM) – a tool which would enable the services needed for safe conduct of UAV flights in generally accessible airspace. There are quite a few European projects which focuses on testing UTM capabilities in order to find a solution which could enable the market and ensure safe UAV operations. One of those systems is PansaUTM – which was developed in order to coordinate drone flights in different types of airspace in Poland. The first part of the paper will present an example of the implementation of this system as a foundation for new possible applications of drones and increasing number of operations. The conclusion of the first part of the article is that, in line rapid growth of UAS flights and different applications of drone services, the European drone ecosystem should evolve even further to deploy very complex drone operations in scalable manner. In order to accommodate unmanned air taxi operations, cargo flights, medical cargo flights, automatic surveillance flights, etc. Europe is preparing towards deployment of Advanced Air Mobility (AAM).The second part of the text indicate the possibility of extensive use of drones in medical logistics as well as minimizing the epidemiological risk as a result of the use of this mean of transport. At the same time, it should be stressed out that the medical transport using drones can be used in urgent situations, where the main variable that has an impact on the success of life and health saving is the breaking of barriers to reaching difficult-to-reach places. In addition, the development of transport using drones can have a lasting impact on improving the quality of life of chronically ill patients who experience severe disease recurrence and thus on the need to implement emergency prevention or treatment measures.The second part of the article focuses as well on the U-space concept as an opportunity for UAVs to be widely used in the field of day-to-day supplies as well as health-related supplies. In the context of the spread of SARS-CoV-2 virus, drones may be used to provide diagnostic screening tests, medicinal products and septic materials, transport of samples of biological material, as well as an information campaign on how to deal with an epidemic, quarantine or isolation at home. The use of UAV for medical supplies is economically and legally justified. The U-space environment from the operational and regulatory side is a multidisciplinary approach that requires the interaction of aviation, law, medicine, robotics, mechatronics and engineering experts. The legal framework for the development of U-space should be taken into account, as well as sector-specific regulations taking into account the principles of the use of drones in strictly defined areas, including in the process of medical supply, and liability for damage caused by UAV medical supply or AI-controlled intelligent machines.

Rotate artificial potential field algorithm toward 3D real-time path planning for unmanned aerial vehicle

As one of the key technologies in the development of Unmanned Aerial Vehicle (UAV), the path planning method has an intuitive impact on the real-time performance of UAV. Most existing path planning algorithms have drawbacks in global optimization ability or optimization speed, and cannot meet the requirements of practical applications. To improve the effectiveness and practicability of the path planning algorithm for UAV obstacle avoidance, this paper proposes the Rotate Artificial Potential Field (R-APF) method based on the traditional artificial potential field method. By introducing a new potential field function and stimulating rotating repulsive force, the R-APF solves the problems of local minimum as well as inaccessibility to targets near obstacles, respectively. Convergence of the algorithm is discussed to demonstrate the feasibility of R-APF. This paper also designs and implements algorithm feasibility verification experiments, obstacle shuttle experiments, as well as running time, and efficiency comparison experiments. In addition, the improved Visual-Inertial Navigation System obstacle detection algorithm is introduced to carry out physical verification experiments. Theoretical proof and experimental results show the proposed R-APF method has higher obstacle avoidance capability, shorter running time, and higher success rate compared to other algorithms, and is promising in practical application.

An Adaptive Robust Attitude Tracking Control of Quadrotor UAV with the Modified Rodrigues Parameter

In this paper, a novel adaptive attitude tracking control method is investigated to enhance tracking performance and robustness for a quadrotor unmanned aerial vehicle (UAV) against the modeling uncertainties and external disturbances. First, the attitude dynamics of a quadrotor UAV based on the modified Rodrigues parameter (MRP) is derived. Then, an adaptive dynamic surface technique with robust integral of the sign of the error (RISE) control approach are designed to improve the tracking performance and robustness of the quadrotor attitude control system suffering from the uncertainties and disturbances. A prescribed performance function is employed to improve the tracking performance of the quadrotor UAV by restraining the tracking errors range. The stability analysis proves the presented control scheme can guarantee all the close-loop control system signals are bounded and control system is uniformly ultimately bounded. Simulation results are carried out to demonstrate the superior and effective performance of the presented control method.

A Survey on UAV Computing Platforms: A Hardware Reliability Perspective

This study describes the Computing Platforms (CPs) and the hardware reliability issues of Unmanned Aerial Vehicles (UAVs), or drones, which recently attracted significant attention in mission and safety-critical applications demanding a failure-free operation. While the rapid development of the UAV technologies was recently reviewed by survey reports focusing on the architecture, cost, energy efficiency, communication, and civil application aspects, the computing platforms’ reliability perspective was overlooked. Moreover, due to the rising complexity and diversity of today’s UAV CPs, their reliability is becoming a prominent issue demanding up-to-date solutions tailored to the UAV specifics. The objective of this work is to address this gap, focusing on the hardware reliability aspect. This research studies the UAV CPs deployed for representative applications, specific fault and failure modes, and existing approaches for reliability assessment and enhancement in CPs for failure-free UAV operation. This study indicates how faults and failures occur in the various system layers of UAVs and analyzes open challenges. We advocate a concept of a cross-layer reliability model tailored to UAVs’ onboard intelligence and identify directions for future research in this area.

An investigation on the aerodynamic performance of a hand-launched solar-powered UAV in flying wing configuration

Aiming to realize the long-endurance ecological environmental monitoring and the rapid takeoff and landing mission in Tibet, a hand-launched solar-powered unmanned aerial vehicle (UAV) in flying wing configuration is introduced and analyzed in this paper. The low-Reynolds-number (LRN) flow is quasi-steadily simulated by solving the full three-dimensional Reynolds Averaged Navier-Stokes (RANS) governing equations coupled with the kT-kL-ω transition model while the propeller performance is evaluated using the multiple-rotating reference frame (MRF) method. Both the cruise and the low-speed takeoff aerodynamic analyses which include the aerodynamic force analysis and the propeller/wing integrated flow mechanism analysis are performed in details. The results indicate that the present UAV can meet the high-efficiency cruise requirements quite well, at the same time, has the ability to take off at the low-speed hand-launched state and a rather low Reynolds number by making good use of the energy injected from the propeller rotation into the flow field.

Multimodal Fusion of Voice and Gesture Data for UAV Control

To enable unmanned aerial vehicle (UAV) operators to efficiently and intuitively convey their commands to a swarm of UAVs, we propose the use of natural and human-centric input modalities, such as voices and gestures. This paper addresses the fusion of input modalities such as voice and gesture data, which are captured through a microphone and a Leap Motion controller, respectively, to control UAV swarms. The obtained experimental results are presented, and the achieved performance (accuracy) is analyzed. Finally, combined human factor ergonomics test with a questionnaire to verify the method’s validity.

A non-intrusive reduced order model using deep learning for realistic wind data generation for small unmanned aerial systems in urban spaces

Realistic wind data are essential in developing, testing, and ensuring the safety of unmanned aerial systems in operation. Alternatives to Dryden and von Kármán turbulence models are required, aimed explicitly at urban air spaces to generate turbulent wind data. We present a novel method to generate realistic wind data for the safe operation of small unmanned aerial vehicles in urban spaces. We propose a non-intrusive reduced order modeling approach to replicate realistic wind data and predict wind fields. The method uses a well-established large-eddy simulation model, the parallelized large eddy simulation model, to generate high-fidelity data. To create a reduced-order model, we utilize proper orthogonal decomposition to extract modes from the three-dimensional space and use specialized recurrent neural networks and long-term short memory for stepping in time. This paper combines the traditional approach of using computational fluid dynamic simulations to generate wind data with deep learning and reduced-order modeling techniques to devise a methodology for a non-intrusive data-based model for wind field prediction. A simplistic model of an isolated urban subspace with a single building setup in neutral atmospheric conditions is considered a test case for the demonstration of the method.

Field Applicability of Earthwork Volume Calculations Using Unmanned Aerial Vehicle

The earthwork volume must be calculated as accurately as possible for economical construction and cost savings. In particular, when calculating the areas and volumes of irregular curved terrains such as mountains, reservoirs, lakes, and coasts, the vertical assumptions for the boundary equation must be rationally and systematically established. This study focuses on earthwork volume calculation technology using UAV (unmanned aerial vehicle). UAV can be used for various types of work, including checking the progress of construction at construction sites and calculating the earthwork volume for large areas. However, earthwork volume calculation technology using UAV is inefficient in terms of information production, management, and reuse because the quantitative guidelines for UAV operations are insufficient, and the output quality, analysis method, and analysis results differ. To solve these problems, the authors investigated an earthwork volume calculation method for construction sites that use UAV to automate the on-site calculation of construction errors and support an on-site monitoring system using building information modeling (BIM). To calculate the earthwork volume of the target site, a chain method with a planned plane map based on the average end-area method was used as a representative earthwork volume calculation method. The digital surface model method was applied to review the optimization of the earthwork volume calculation using UAV. This study is a process of analyzing construction data, and aims to strengthen the linkage of 3D data and provide construction management information specialized in excavation work. Through this earthwork analysis using UAV, it is possible to intuitively review the progress of earthwork in 3D by linking the current site with the planned plane.

LiDAR Based Detect and Avoid System for UAV Navigation in UAM Corridors

In this work, a Detect and Avoid system is presented for the autonomous navigation of Unmanned Aerial Vehicles (UAVs) in Urban Air Mobility (UAM) applications. The current implementation is designed for the operation of multirotor UAVs in UAM corridors. During the operations, unauthorized flying objects may penetrate the corridor airspace posing a risk to the aircraft. In this article, the feasibility of using a solid-state LiDAR (Light Detecting and Ranging) sensor for detecting and positioning these objects was evaluated. For that purpose, a commercial model was simulated using the specifications of the manufacturer along with empirical measurements to determine the scanning pattern of the device. With the point clouds generated by the sensor, the system detects the presence of intruders and estimates their motion to finally compute avoidance trajectories using a Second Order Cone Program (SOCP) in real time. The method was tested in different scenarios, offering robust results. Execution times were of the order of 50 milliseconds, allowing the implementation in real time on modern onboard computers.

Simulation tools, environments and frameworks for UAVs and multi-UAV-based systems performance analysis (version 2.0)

ABSTRACT During the last decade, Unmanned Aerial Vehicles (UAVs) gained wide attention and are integrated into diverse systems and deployed into many contexts. The need to assess the UAVs’ performances, impacts of applications, routing protocols, mobility as well as other features in a network is necessary. However, conducting real experiments within UAV-based systems and particularly with multi-UAV-based systems can be extremely costly and complex. To fill this gap simulators, emulators, and frameworks are used to evaluate the performance and viability of UAV-based systems with lower cost and in a short period. Accordingly, researchers and software engineers developed several simulators and co-simulators dedicated to the performance evaluations of UAV-based systems. In this regard, this investigation highlights and identifies the most suitable simulators, co-simulators, emulators, and frameworks for UAV flights and management. Both the tools dedicated to mono-UAV-based systems and the tools dedicated to multi-UAV-based systems are pinpointed. In this paper, the goals, the requirements, the strengths, and the limitations of each studied tool are detailed. Besides that, a UAV simulators catalog is provided. It includes guidelines that will be useful for researchers and help them to identify and to select the adequate tools for UAVs performance analysis that meet their needs.

Integrated Scheduling of Sensing, Communication, and Control for mmWave/THz Communications in Cellular Connected UAV Networks

By providing ultra-high transmission data rate, millimeter wave (mmWave) and terahertz (THz) communications are promising to enable backhaul data transmission in cellular connected unmanned aerial vehicle (UAV) networks. In such networks, with little or no human assistance, connected autonomous UAVs (CA-UAV) can build air-ground networks and achieve seamless wide-area coverage. With the usage of high frequency (i.e., mmWave/THz), radio/radar sensing function is expected to be achieved in wireless networks, which can be used to track UAV for beam tracking in mmWave/THz communications and motion control of UAV. However, it is extremely difficult to jointly design sensing, communication, and motion control since they have been developing in relatively parallel with limited intersections. In this paper, we propose a new integrated scheduling method of sensing, communication, and control for mmWave/THz communications in UAV networks to enable data transmission of the backhaul from UAV to the ground base station (BS). In the proposed method, we first analyze the interactions among sensing, communication, and motion control, where sensing and motion control are strongly coupled to form the sensing-control pattern. Then, we provide a new definition from motion control perspective, i.e., state-to-noise-ratio, which links the relationship between sensing-control pattern activation and data rate determined by beam alignment in mmWave/THz communications. Finally, a closed-form expression is obtained for data rate triggered sensing-control pattern activation design, where both data rate requirement in mmWave/THz communications and motion control performance of UAV are guaranteed. Simulation results show remarkable performance of the proposed method.

Cyber Attack Detection and Isolation for a Quadrotor UAV With Modified Sliding Innovation Sequences

Common vulnerabilities in typical intelligent cyber-physical systems such as unmanned aerial vehicles (UAVs) can be easily exploited by cyber attackers to cause serious accidents and harm. For successful UAV operations, security against cyber attacks is imperative. In this paper, we propose a modified sliding innovation sequences (MSIS) detector, based on the extended Kalman filter optimal state estimation, for a dynamic quadrotor system to detect cyber attacks inflicted on both its actuators and sensors in real time. These cyber attacks include random attacks, false data injection (FDI) attacks and denial-of-service (DoS) attacks. The MSIS detector computes the operator norm of the normalized innovation (residual) sequence within a sliding time window and triggers the alarm if the value is above the preset threshold. For a quadrotor undergoing rapid turns in a complex trajectory, the detector observes a reduced false alarm rate as compared to other state estimation-based detectors. To address the initial estimation error problem, we implement an iteration procedure to initiate and calibrate the detector. By evaluating the sample covariance of the normalized innovation sequence, the MSIS detector has the capability to isolate cyber attacks. Finally, simulation results of a quadrotor in a periodic, complex trajectory flight are provided to verify the effectiveness of the MSIS detection and isolation method.

Energy Minimization for Cellular-Connected UAV: From Optimization to Deep Reinforcement Learning

Cellular-connected unmanned aerial vehicles (UAVs) are expected to become integral components of future cellular networks. To this end, one of the important problems to address is how to support energy-efficient UAV operation while maintaining reliable connectivity between those aerial users and cellular networks. In this paper, we aim to minimize the energy consumption of cellular-connected UAV via jointly designing the mission completion time and UAV trajectory, as well as communication base station (BS) associations, while ensuring a satisfactory communication connectivity with the ground cellular network during the UAV flight. An optimization problem is formulated by taking into account the UAV’s flight energy consumption and various practical aspects of the air-ground communication models, including BS antenna pattern, interference from non-associated BSs and local environment. The formulated problem is difficult to tackle due to the lack of closed-form expressions and non-convexity nature. To this end, we first assume that the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">channel knowledge map</i> (CKM) or radio map for the considered area is available, which contains rich information about the relatively stable (large-scale) channel parameters. By utilizing <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">path discretization</i> technique, we obtain a discretized equivalent problem and develop an efficient solution based on graph theory by employing convex optimization technique and a dynamic-weight shortest path algorithm over graph. Next, we study the more practical case that the CKM is unavailable initially. By transforming the optimization problem to a Markov decision process (MDP), we develop a deep reinforcement learning (DRL) algorithm based on multi-step learning and double Q-learning over a dueling Deep Q-Network (DQN) architecture, where the UAV acts as an agent to explore and learn its moving policy according to its local observations of the measured signal samples. Extensive simulations are carried out and the results show that our proposed designs significantly outperform baseline schemes. Furthermore, our results reveal new insights of energy-efficient UAV flight with connectivity requirements and unveil the tradeoff between UAV energy consumption and time duration along line segments.

Deep Reinforcement Learning for Integrated Non-Linear Control of Autonomous UAVs

In this research, an intelligent control architecture for an experimental Unmanned Aerial Vehicle (UAV) bearing unconventional inverted V-tail design, is presented. To handle UAV’s inherent control complexities, while keeping them computationally acceptable, a variant of distinct Deep Reinforcement Learning (DRL) algorithm, namely Deep Deterministic Policy Gradient (DDPG) is proposed. Conventional DDPG algorithm after being modified in its learning architecture becomes capable of intelligently handling the continuous state and control space domains besides controlling the platform in its entire flight regime. Nonlinear simulations were then performed to analyze UAV performance under different environmental and launch conditions. The effectiveness of the proposed strategy is further demonstrated by comparing the results with the linear controller for the same UAV whose feedback loop gains are optimized by employing technique of optimal control theory. Results indicate the significance of the proposed control architecture and its inherent capability to adapt dynamically to the changing environment, thereby making it of significant utility to airborne UAV applications.