Unmanned Aerial Vehicle Research Research Articles

Experimentation on Leader-Following Formation for Fixed-Wing UAVs

Fixed-wing unmanned aerial vehicles (UAVs) are a primary focus of current UAV research. Challenges arise in their flight due to high speed and complex maneuverability. This paper explores the coordinated turn guidance law for fixed-wing UAVs and validates an experimental leader-follower formation platform in flight. Results demonstrate the effectiveness of the proposed algorithm and platform in enabling actual leader-follower formation flights for fixed-wing UAVs.

Design and stabilization of a Coandă effect-based UAV: Comparative study between fuzzy logic and PID control approaches

Recent years have experienced a notable surge in unmanned aerial vehicles (UAV) research, prompting exploration into innovative concepts. This paper introduces a compact UAV harnessing the Coandă effect, an underexplored phenomenon in fluid mechanics. Featuring a single lift motor and two types of flaps, this UAV offers exceptional maneuverability, presenting significant challenges compared to conventional multi-rotor UAVs. To address these challenges, we explore the theoretical study, mechatronic design, and manufacturing complexities of the Coandă UAV. Emphasizing the distinctiveness of our work, we assess a Fuzzy Logic Controller (FLC) for UAV stabilization, marking the first application of such techniques to a Coandă-effect UAV, in contrast to the Proportional-Integral-Derivative (PID) control employed by other researchers. This innovative application of Fuzzy logic, particularly the Sugeno model, proves advantageous, offering faster and more robust control in uncertain or noisy environments. The proposed FLC strategy is systematically compared against a classical PID control approach, formulated based on the Mamdani and Sugeno models, optimized and manually tuned using a genetic algorithm. Our results showcase a significantly improved settling time of 0.417 s with the FLC strategy, surpassing the PID control approach by 35.23%. To substantiate our findings, we present comprehensive experimentation conducted at both software and hardware levels using Matlab® and Simulink for a microcontroller-based UAV. This groundbreaking fusion of novel design and advanced control techniques not only addresses the unique challenges posed by the Coandă UAV's aerodynamics but also contributes significantly to the field of UAV research.

UAV path planning based on a dual-strategy ant colony optimization algorithm

With the rapid development of modern communication and automatic control technologies, unmanned aerial vehicles (UAVs) have increasingly gained importance in both military and civilian domains. Path planning, a critical aspect for achieving autonomous aerial navigation, has consistently been a focal point in UAV research. However, traditional ant colony algorithms need to be improved for the drawbacks of susceptibility to local optima and weak convergence capabilities. Consequently, a novel path planning methodology is proposed based on a dual-strategy ant colony algorithm. In detail, an improved state transition probability rule is introduced, redefining ant movement rules by integrating the state transition strategy of deterministic selection during the iterative process. Additionally, heuristic information on adjacent node distance and mountain height is added to further improve the search efficiency of the algorithm. Then, a new dynamically adjusted pheromone update strategy is proposed. The update strategy is continuously adjusted during the iteration process, which is beneficial to the algorithm’s global search in the early stage and accelerated convergence in the later stage, preventing the algorithm from falling into local optimality and improving its convergence. Based on the above improvements, a new variation of ant colony optimization (ACO) called dual-strategy ACO algorithm is formed. Experimental results prove that dual-strategy ACO has superior global search capabilities and convergence characteristics from four key aspects: path length, fitness values, iteration number, and running time.

Review of Key Technologies of Rotary-Wing Mars UAVs for Mars Exploration

The sparse atmosphere on the surface of Mars provides the necessary flight conditions for Mars unmanned aerial vehicles (UAVs) to perform low-altitude flights. This work presents a comprehensive overview of key technologies in the development of Mars UAVs, with a specific focus on rotary-wing Mars UAVs. It summarizes prototypes of rotary-wing Mars UAVs developed by various global research institutions. It reviews essential technologies in rotary-wing Mars UAV research, including the Mars near-surface atmospheric environment, aerodynamic characteristics, and principles of low-pressure flight control. This work also summarizes various experimental setups and ground test results for rotary-wing Mars UAVs. Furthermore, it discusses the future development trends of rotary-wing Mars UAVs.

Meta-heuristic Algorithms in UAV Path Planning Optimization: A Systematic Review (2018–2022)

Unmanned Aerial Vehicles (UAVs), a subset of aerial robots, play crucial roles in various domains, such as disaster management, agriculture, and healthcare. Their application proves invaluable in situations where human intervention poses risks or involves high costs. However, traditional approaches to UAV path planning struggle in efficiently navigating complex and dynamic environments, often resulting in suboptimal routes and extended mission durations. This study seeks to investigate and improve the utilization of meta-heuristic algorithms for optimizing UAV path planning. Toward this aim, we carried out a systematic review of five major databases focusing on the period from 2018 to 2022. Following a rigorous two-stage screening process and a thorough quality appraisal, we selected 68 papers out of the initial 1500 to answer our research questions. Our findings reveal that hybrid algorithms are the dominant choice, surpassing evolutionary, physics-based, and swarm-based algorithms, indicating their superior performance and adaptability. Notably, time optimization takes precedence in mathematical models, reflecting the emphasis on CPU time efficiency. The prevalence of dynamic environmental types underscores the importance of real-time considerations in UAV path planning, with three-dimensional (3D) models receiving the most attention for accuracy in complex trajectories. Additionally, we highlight the trends and focuses of the UAV path planning optimization research community and several challenges in using meta-heuristic algorithms for the optimization of UAV path planning. Finally, our analysis further highlights a dual focus in UAV research, with a significant interest in optimizing single-UAV operations and a growing recognition of the challenges and potential synergies in multi-UAV systems, alongside a prevalent emphasis on single-target mission scenarios, but with a notable subset exploring the complexities of multi-target missions.

UAV mission scheduling with completion time, flight distance, and resource consumption constraints

Unmanned aerial vehicles (UAVs) are widely used in various military and civilian applications. UAV mission scheduling is a key issue in UAV applications and a central topic in UAV research. UAV task scheduling should include several constraints into consideration, such as completion time constraint, flight distance constraint, and resource consumption constraint. Furthermore, UAV task scheduling should be studied within the traditional framework of combinatorial optimisation. In this paper, we consider optimal mission scheduling for heterogeneous UAVs with completion time, flight distance, and resource consumption constraints. The contributions of the paper are summarised as follows. We define two combinatorial optimisation problems, namely, the NFTM (number of finished tasks maximisation) problem and the RFTM (reward of finished tasks maximisation) problem. We construct an algorithmic framework for both NFTM and RFTM problems, so that our heuristic algorithms (four for NFTM and two for RFTM) can be presented in a unified way. We derive upper bounds for optimal solutions, so that our heuristic solutions can be compared with optimal solutions. We experimentally evaluate the performance of our heuristic algorithms. To the best of our knowledge, this is the first paper studying UAV mission scheduling with time, distance, and resource constraints as combinatorial optimisation problems.

UAV SWARM SYSTEMS

The rapid development of technology has introduced new and groundbreaking approaches to unmanned aerial vehicles (UAVs). One such innovation is the swarm UAV systems. These systems represent a technology where multiple small and often autonomous aircraft collaborate to perform specific tasks. An important component of this system is artificial intelligence. Swarm UAV systems can operate more effectively and coordinatedly by utilising artificial intelligence algorithms. Each UAV is equipped with autonomous control systems and artificial intelligence software. Through data analysis, object recognition, route planning, environmental adaptation and other complex tasks, artificial intelligence aids these UAVs in successfully completing tasks. As a result, swarm UAVs are capable of performing more complex and dynamic missions. The integration of artificial intelligence into swarm UAV systems holds great potential for several application fields. For instance, in search and rescue operations, AI can perform image analysis to detect individuals trapped under debris. In agriculture applications, AI can detect plant diseases or pests and optimize irrigation requirements. In this article, we will examine what swarm UAV systems are, how they operate, and the contribution of integrating artificial intelligence to this technology. In this article, we will examine what swarm UAV systems are, how they operate, and the contribution of integrating artificial intelligence to this technology. In this article, we will examine what swarm UAV systems are, how they operate, and the contribution of integrating artificial intelligence to this technology. Additionally, we will focus on the potential future application areas of swarm UAV systems and the potential impacts of this integration. Swarm UAV systems and artificial intelligence represent a significant transformation in the world of technology, and this article will provide a resource to better understand these exciting developments. Keywords: Research of UAVs, integration of artificial intelligence, flight programs, autonomous control.

A Comprehensive Review of Recent Research Trends on Unmanned Aerial Vehicles (UAVs)

The growing interest in unmanned aerial vehicles (UAVs) from both the scientific and industrial sectors has attracted a wave of new researchers and substantial investments in this expansive field. However, due to the wide range of topics and subdomains within UAV research, newcomers may find themselves overwhelmed by the numerous options available. It is therefore crucial for those involved in UAV research to recognize its interdisciplinary nature and its connections with other disciplines. This paper presents a comprehensive overview of the UAV field, highlighting recent trends and advancements. Drawing on recent literature reviews and surveys, the review begins by classifying UAVs based on their flight characteristics. It then provides an overview of current research trends in UAVs, utilizing data from the Scopus database to quantify the number of scientific documents associated with each research direction and their interconnections. This paper also explores potential areas for further development in UAVs, including communication, artificial intelligence, remote sensing, miniaturization, swarming and cooperative control, and transformability. Additionally, it discusses the development of aircraft control, commonly used control techniques, and appropriate control algorithms in UAV research. Furthermore, this paper addresses the general hardware and software architecture of UAVs, their applications, and the key issues associated with them. It also provides an overview of current open source software and hardware projects in the UAV field. By presenting a comprehensive view of the UAV field, this paper aims to enhance our understanding of this rapidly evolving and highly interdisciplinary area of research.

Communication and Control in Collaborative UAVs: Recent Advances and Future Trends

The recent progress in unmanned aerial vehicles (UAV) technology has significantly advanced UAV-based applications for military, civil, and commercial domains. Nevertheless, the challenges of establishing high-speed communication links, flexible control strategies, and developing efficient collaborative decision-making algorithms for a swarm of UAVs limit their autonomy, robustness, and reliability. Thus, a growing focus has been witnessed on collaborative communication to allow a swarm of UAVs to coordinate and communicate autonomously for the cooperative completion of tasks in a short time with improved efficiency and reliability. This work presents a comprehensive review of collaborative communication in a multi-UAV system. We thoroughly discuss the characteristics of intelligent UAVs and their communication and control requirements for autonomous collaboration and coordination. Moreover, we review various UAV collaboration tasks, summarize the applications of UAV swarm networks for dense urban environments and present the use case scenarios to highlight the current developments of UAV-based applications in various domains. Finally, we identify several exciting future research direction that needs attention for advancing the research in collaborative UAVs.

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.

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

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

QoS-Oriented Sensing–Communication–Control Co-Design for UAV-Enabled Positioning

Unmanned aerial vehicle (UAV)-enabled positioning that uses UAVs as aerial anchor nodes has been envisioned as a promising solution for providing positioning services in harsh environments. In previous research, state sensing and control of UAVs was either ignored or assumed to be performed continuously, resulting in system instability or a waste of wireless resources. Therefore, in this paper, we propose a quality-of-service (QoS)-oriented sensing-communication-control (SCC) co-design scheme for UAV-enabled positioning systems. We first establish mathematical models of UAV state sensing and control. Then, we analyze the influence of sensing scheduling and transmission failure on the stability of UAV, as well as the performance of positioning services in the presence of UAV control error. Based on these models and analysis results, we further study the problem of minimizing the amount of data transmitted by optimizing the sensing scheduling and blocklength allocation under the condition of meeting each user’s requirement on position accuracy. Finally, a heuristic algorithm is developed to solve this mixed-integer nonlinear problem. Numerical results demonstrate the validity and superiority of the proposed scheme. Compared with two benchmark schemes, our scheme reduces the failure rate or resource consumption of positioning services by more than 75% or 80%.

Application of CFD Numerical Simulation Methods in the Field of UAV Research

Unmanned aerial vehicle (UAV) has become one of the hot topics in today’s research. A drone’s short life has always been one of the main drawbacks, and fluid mechanics is related to the development of computer technology. CFD was used to simulate the aerodynamic parameters of UAVs and has become a more economical choice, which can greatly save the cost of materials. Solid-liquid coupling calculation generally chooses the SST k - Ω model to calculate and will also include an energy equation in the calculation. Then, according to the precision needs to choose a different order of upwind format. Choose either steady state or transient simulation according to different needs. CFD is widely used in research on the overall stability and reliability of UAVs and the experiment on the aerodynamic characteristics of UAV components.

Global Research Trends for Unmanned Aerial Vehicle Remote Sensing Application in Wheat Crop Monitoring

Wheat is an important staple crop in the global food chain. The production of wheat in many regions is constrained by the lack of use of advanced technologies for wheat monitoring. Unmanned Aerial Vehicles (UAVs) is an important platform in remote sensing for providing near real-time farm-scale information. This information aids in making recommendations for monitoring and improving crop management to ensure food security. This study appraised global scientific research trends on wheat and UAV studies between 2005 and 2021, using a bibliometric method. The 398 published documents were mined from Web of Science, Scopus, and Dimensions. Results showed that an annual growth rate of 23.94% indicates an increase of global research based on wheat and UAVs for the surveyed period. The results revealed that China and USA were ranked as the top most productive countries, and thus their dominance in UAVs extensive usage and research developments for wheat monitoring during the study period. Additionally, results showed a low countries research collaboration prevalent trend, with only China and Australia managing multiple country publications. Thus, most of the wheat- and UAV-related studies were based on intra-country publications. Moreover, the results showed top publishing journals, top cited documents, Zipf’s law authors keywords co-occurrence network, thematic evolution, and spatial distribution map with the lack of research outputs from Southern Hemisphere. The findings also show that “UAV” is fundamental in all keywords with the largest significant appearance in the field. This connotes that UAV efficiency was important for most studies that were monitoring wheat and provided vital information on spatiotemporal changes and variability for crop management. Findings from this study may be useful in policy-making decisions related to the adoption and subsidizing of UAV operations for different crop management strategies designed to enhance crop yield and the direction of future studies.

Research and Application of 5G Remote Control UAV with Aerial Electronic Fence

Unmanned aerial vehicles (UAVs) are often out of control, lost, and crashed during use. The research of a 5G remote control UAV with an aerial electronic fence can solve these problems. The integrated application of 5G communication technology, Internet of Things technology, and electronic fence technology built through the GPS navigation cloud platform enables UAVs to achieve remote real-time control and electronic fence protection in the air through 5G. At the same time, UAVs can also achieve remote control, data transmission, image transmission, and other functions, so that UAVs can automatically and safely return in case of out-of-control failure, broaden the flight radius of UAVs, and improve the safety of UAVs.

Manipal-UAV person detection dataset: A step towards benchmarking dataset and algorithms for small object detection

Intelligent UAV video analysis has drawn the attention of many researchers due to the increasing demand for unmanned aerial vehicles (UAVs) in computer vision-related applications. Applications such as search and rescue, the military, and surveillance demand automatic detection of human targets in large-scale UAV images, which is very challenging due to the small size and inadequate feature representation of person objects. Despite the significant advancements in generic object detection tasks, the performance of the state-of-the-art small object detection algorithms falls below the satisfactory level due to the lack of a representative dataset and the limited information available for small objects. To facilitate advancements in UAV and small object detection research, we present a Manipal-UAV person detection dataset11https://github.com/Akshathakrbhat/Manipal-UAV-Person-Dataset. collected from two UAVs flying at varying altitudes, locations, and weather conditions. The dataset contains 13,462 sampled images from 33 videos having 1,53,112 person object instances. The videos are captured in an unconstrained environment with complex scenes covering small objects of varying scales, poses, illumination, and occlusion, making person detection extremely challenging on this newly created dataset. This article compares the characteristics of the Manipal-UAV dataset with the standard VisDrone and Okutama datasets having aerial view person objects. In addition, it provides baseline evaluation results of the various state-of-the-art object detection algorithms applied to the newly created Manipal-UAV Person detection dataset. The dataset is made publicly available at https://github.com/Akshathakrbhat/Manipal-UAV-Person-Dataset.

МЕТОД СТВОРЕННЯ НАВЧАЛЬНОГО БПЛА ЛІТАКОВОГО ТИПУ ЗА ДОПОМОГОЮ 3D-ДРУКУ

he concept of specialized training unmanned aerial vehicle (UAV) of aircraft type for operators of unmanned aerial vehicles is proposed. Training opportunities in various areas of operator training are highlighted. A method of manufacturing an aircraft-type training UAV taking into account aerodynamic and weight characteristics has been developed. The developed method is based on the production of aircraft parts using a 3D printer, which is modified to meet the requirements of small aircraft type aircraft. This technique also includes the process of selecting the most advantageous combination of materials to create a small aircraft. Tables with initial data of existing UAVs are given.The flight characteristics, aerodynamic schemes and type of engine that are most rationally suitable for unmanned aerial vehicles in accordance with this class are proposed. Based on these data, a prototype model was built, the method of UAV design was adopted, taking into account the choice of parameters of the power plant. A demonstration example of the choice of the main parameters of the training taking into account the UAV under specific requirements is given. The obtained main technical characteristics of the UAV are presented. Based on the calculated data, a three-dimensional geometric model of the UAV was developed and a flight sample was made. A series of test flights of the flight model was conducted, tested under difficult weather conditions, when the wind force is about 12 m / s. The ways of using the described method of developing UAV training and research, as well as many other factors that affect 3D printing without detecting defects and problems are considered.

An Optimal Fuzzy Logic-Based Energy Management Strategy for a Fuel Cell/Battery Hybrid Power Unmanned Aerial Vehicle

With the development of high-altitude and long-endurance unmanned aerial vehicles (UAVs), optimization of the coordinated energy dispatch of UAVs’ energy management systems has become a key target in the research of electric UAVs. Several different energy management strategies are proposed herein for improving the overall efficiency and fuel economy of fuel cell/battery hybrid electric power systems (HEPS) of UAVs. A rule-based (RB) energy management strategy is designed as a baseline for comparison with other strategies. An energy management strategy (EMS) based on fuzzy logic (FL) for HEPS is presented. Compared with classical rule-based strategies, the fuzzy logic control has better robustness to power fluctuations in the UAV. However, the proposed FL strategy has an inherent defect: the optimization performances will be determined by the heuristic method and the past experiences of designers to a great extent rather than a specific cost function of the algorithm itself. Thus, the paper puts forward an improved fuzzy logic-based strategy that uses particle swarm optimization (PSO) to track the optimal thresholds of membership functions, and the equivalent hydrogen consumption minimization is considered as the objective function. Using a typical 30 min UAV mission profile, all the proposed EMS were verified by simulations and rapid controller prototype (RCP) experiments. Comprehensive comparisons and analysis are presented by evaluating hydrogen consumption, system efficiency and voltage bus stability. The results show that the PSO-FL algorithm can further improve fuel economy and achieve superior overall dynamic performance when controlling a UAV’s fuel-cell powertrain.

Simulation analysis and experimental research on the safety distance of UAV inspection on transmission lines

In recent years, unmanned aerial vehicle (UAV) inspection has become one of the main means of daily operation and maintenance of overhead transmission lines. Since the key components of the UAV inspection system are composed of electronic components, it relies on data communication to realize data link transmission. In the actual operation process, the complex electromagnetic field environment has an obvious impact on its control and communication performance, especially when inspecting near the DC line. In this paper, combined with the research on the safety assurance technology of UAV inspection operations, the electromagnetic field simulation model of the UAV inspection operation on DC transmission lines is established. The electromagnetic field distribution around the wire is analyzed when the UAV is at different inspection distances, and the influence range and degree of the DC electromagnetic field on UAV inspection system are obtained. A true test platform for the inspection safety distance of the ±500 kV line UAV inspection was built, and the inspection safety distance test and research of the small rotary-wing UAV with a typical size, structure, and material was carried out. Through the test, when the current of the transmission line is 3 kA, the minimum safety distance of the UAV inspection operation of the ±500 kV DC transmission line straight tower is 3 m, and the distance is corrected in combination with the current. The research results of this paper can provide technical support for the inspection operation of small multi-rotor UAVs on ±500 kV transmission lines.

Obstacle Avoidance Path Planning for UAV Based on Improved RRT Algorithm

Obstacle avoidance path planning capability, as one of the key capabilities of UAV (Unmanned Aerial Vehicle) to achieve safe autonomous flight, has always been a hot research topic in UAV research filed. As a commonly used obstacle avoidance path planning algorithm, RRT (Rapid-exploration Random Tree) algorithm can carry out obstacle avoidance path planning in real time and online. In addition, it can obtain the asymptotically optimal obstacle avoidance path on the premise of ensuring the completeness of probability. However, it has some problems, such as high randomness, slow convergence speed, long transit time, and curved flight trajectory, so that it cannot meet the flight conditions of the actual UAV. To solve these problems, the paper proposes an improved RRT algorithm. In the process of extending the random tree, ACO (ant colony optimization) is introduced to make the planning path asymptotically optimal. The optimized algorithm can set pheromones on the path obtained by RRT and select the next extension point according to the pheromone concentration. And then through a certain number of iterations, it converges to an ideal path scheme. In addition, this paper also uses MATLAB to verify the effectiveness and superiority of the algorithm: Although RRT is easy to fall into local optimization, since the optimization method in this paper can almost certainly converge to the optimal solution, when it is necessary to preplan the path before UAV takeoff, it can be used.