Single UAV Research Articles

A Time and Energy Efficient Multi-UAV Coverage Planning for the Search and Rescue Mission under Wind fields

This paper discusses how to quickly and efficiently search for victims when a disaster occurs. Since the search area is large, the search time of a single UAV will be too long to satisfy the golden window of the search and rescue mission. Therefore, it is necessary to dispatch multiple UAVs to conduct a cooperative search by proposing a multi-UAV area coverage planning under wind fields (MUCPW) algorithm and its variant MUCPW-A to achieve the shortest mission time. It first averagely divides the search area into several regions, formulates equations to calculate the minimum mission time and corresponding energy consumption among all possible area partition methods. As compared to related work, simulation results show that MUCPW and MUCPW-A achieve the shortest mission time and the least energy consumption to search all victims in the search and rescue mission. Further, MUCPW-A achieves the smallest standard deviation in energy consumption.

Cooperative Elliptic Positioning Through Single UAV During GNSS Outages

Cooperative Elliptic Positioning Through Single UAV During GNSS Outages

Consensus-based virtual leader tracking swarm algorithm with GDRRT*-PSO for path-planning of multiple-UAVs

UAV technology is rapidly advancing and widely utilized, particularly in social and military domains, due to its extensive motion and maneuverability. Coordinating multiple UAVs enables more rapid and efficient task execution compared to a single UAV. The proliferation of UAVs across various sectors, including entertainment, transportation, delivery, and social domains, as well as military applications such as surveillance, tracking, and attack, has spurred research in swarm systems. In this study, a new swarm topology is presented by combining the Consensus-Based Virtual Leader Tracking Swarm Algorithm (CBVLTSA), which provides formation control in swarm systems, with the Goal Distance-based Rapidly-Exploring Random Tree with Particle Swarm Optimization (GDRRT*-PSO) route planning algorithm. Recently proposed, GDRRT* is notable for its efficient operation in expansive environments and rapid convergence to the goal. Within this framework, the path generated by GDRRT* is optimized using PSO to yield the shortest current route. CBVLTSA employs a potential push and pull function to facilitate cooperative, coordinated flight among swarm members. While applying pushing force to avoid collisions with each other and obstacles, members also exert pulling force to maintain flight formation while navigating to target points. This ensures controlled flight formation and collision-free traversal along the GDRRT*-PSO route. Consequently, unlike the others, the proposed algorithm achieves faster target reach with pre-planned routes, demonstrating a robust and flexible swarm topology with CBVLTSA. Moreover, we anticipate the significant utility of this algorithm across various swarm applications, including target detection, observation, tracking, trade and transportation logistics, and collective defense and attack strategies.

Aerial Surveillance Leveraging Delaunay Triangulation and Multiple-UAV Imaging Systems

In aerial surveillance systems, achieving optimal object detection precision is of paramount importance for effective monitoring and reconnaissance. This article presents a novel approach to enhance object detection accuracy through the integration of Delaunay triangulation with multi-unmanned aerial vehicle (UAV) systems. The methodology involves positioning multiple UAVs at pre-specified locations using the Delaunay triangulation algorithm with performance of O (n log n). This is compared with the conventional single UAV approach at a near distance. Our findings reveal that the collaborative efforts of multiple UAVs, guided by Delaunay triangulation, significantly improves object detection accuracy, especially when compared to a single UAV operating in close proximity. This research employs advanced image processing techniques to identify objects in the area under surveillance. Results indicate a substantial enhancement in the collective surveillance capabilities of the multi-UAV system, demonstrating its efficacy in unconstrained scenarios. This research not only contributes to the optimization of aerial surveillance operations but also underscores the potential of spatially informed UAV networks for applications demanding heightened object detection accuracy. The integration of Delaunay triangulation with multi-UAV systems emerges as a promising strategy for advancing the capabilities of aerial surveillance in scenarios ranging from security and emergency response to environmental monitoring.

Review of Aerial Transportation of Suspended-Cable Payloads with Quadrotors

Payload transportation and manipulation by rotorcraft drones are receiving a lot of attention from the military, industrial and logistics research areas. The interactions between the UAV and the payload, plus the means of object attachment or manipulation (such as cables or anthropomorphic robotic arms), may be nonlinear, introducing difficulties in the overall system performance. In this paper, we focus on the current state of the art of aerial transportation systems with suspended loads by a single UAV and a team of them and present a review of different dynamic cable models and control systems. We cover the last sixteen years of the existing literature, and we add a discussion for evaluating the main trends in the referenced research works.

UAV remote sensing image stitching via improved VGG16 Siamese feature extraction network

UAV remote sensing image stitching can provide a more comprehensive and continuous area by stitching several overlapping region images. It has been widely used in many fields, such as airborne reconnaissance and surveillance tasks, agriculture production, geological disaster monitoring, and so on. However, traditional feature-based image stitching methods often require hand-crafted features and multiple execution steps, which could extract many redundant feature points in non-overlapping regions and may fail to stitch images with weak textures. To solve this problem, we propose an improved VGG16 Siamese network for the UAV remote sensing image stitching model to achieve end-to-end stitching. The first 13 layers of the VGG16 network were intercepted to form a shared weights feature extraction network, and an improved Squeeze-and-Excitation module was introduced to effectively extract the features of the overlapping areas of the images. A network of regressing affine matrix was designed by using the LeakyReLu activation function to protect the relevant feature maps after feature fine matching, which improves the accuracy of image stitching. Besides, we constructed datasets to train our network: a single UAV remote sensing image was used to form a training image pair based on a limited degree of affine transformation, and the real label of affine transformation between the image pairs was obtained. We compared the SIFT+RANSAC, APAP, and deep homography estimation-based image stitching algorithm on the UAV remote sensing datasets. Experiments show the value of structural similarity(SSIM) increased by 24.1%, and the root mean square error(RMSE) was reduced by 14.69%. Moreover, the proposed stitching model also has good stitching results for UAV remote sensing images as well as weak texture images in subjective visual effects.

A Consistent Round-Up Strategy Based on PPO Path Optimization for the Leader-Follower Tracking Problem.

Single UAVs have limited capabilities for complex missions, so suitable solutions are needed to improve the mission success rate, as well as the UAVs' survivability. A cooperative multi-UAV formation offers great advantages in this regard; however, for large and complex systems, the traditional control methods will be invalid when faced with unstable and changing environments. To deal with the poor self-adaptability and high requirements for the environmental state information of traditional control methods for a multi-UAV cluster, this paper proposes a consistent round-up strategy based on PPO path optimization to track targets. In this strategy, the leader is trained using PPO for obstacle avoidance and target tracking, while the followers are expected to establish a communication network with the leader to obtain environmental information. In this way, the tracking control law can be designed, based on the consistency protocol and the Apollonian circle, to realize the round-up of the target and obstacle avoidance. The experimental results show that the proposed strategy can achieve the round-up of the target UAV and guide the pursuing multi-UAV group to avoid obstacles in the absence of the initial detection of the target. In multiple simulated scenarios, the success rates of the pursuit multi-UAV cluster for rounding up the target are maintained above 80%.

Cooperative Positioning Method of a Multi-UAV Based on an Adaptive Fault-Tolerant Federated Filter.

Aiming at the problem of the low cooperative positioning accuracy and robustness of multi-UAV formation, a cooperative positioning method of a multi-UAV based on an adaptive fault-tolerant federated filter is proposed. Combined with the position of the follower UAV and leader UAV, and the relative range between them, a cooperative positioning model of the follower UAV is established. On this basis, an adaptive fault-tolerant federated filter is designed. Fault detection and isolation technology are added to improve the positioning accuracy of the follower UAV and the fault tolerance performance of the filter. Meanwhile, the measurement noise matrix is adjusted by the adaptive information allocation coefficient to reduce the impact of undetected fault information on the sub-filter and global estimation accuracy. The simulation results show that the adaptive fault-tolerant federated algorithm can greatly improve the positioning accuracy, which is 83.4% higher than that of the absolute positioning accuracy of a single UAV. In the case of a gradual fault, the method has a stronger fault-tolerant performance and reconstruction performance.

UAV search optimization for recording emerging targets with camouflaging capabilities

This paper extends the emerging target information gathering domain by introducing a camouflaging component, investigating the benefit of using multiple UAVs, and studying the impact of allowing re-visits. Previous work in this area addresses the UAV Orienteering Problem for target detection where targets emerge according to non-homogeneous space–time Poisson processes. Our extension considers that emerged targets will camouflage themselves to become undetectable after a period of time and nodes can be revisited. Routes for single or multiple UAVs are generated using the Team Orienteering Problem with Time Windows and evaluated using simulation. In addition, a framework is developed for comparing routes, and the value added by increasing the solving time is investigated. Our computational testing reveals increasing the number of time windows used increases the expected route value. A factorial analysis is conducted which indicates the network topology, the number of time windows used, and the coefficient of variation for camouflage time generally have significant effects on the expected number of targets detected regardless of the number of UAVs used. In addition, increasing the amount of time spent solving the problem does not always increase the number of expected target detected.

Comparative Analysis of Different UAV Swarm Control Methods on Unmanned Farms

Unmanned farms employ a variety of sensors, automated systems, and data analysis techniques to enable fully automated and intelligent management. This not only heightens agricultural production efficiency but also reduces the costs associated with human resources. As integral components of unmanned farms’ automation systems, agricultural UAVs have been widely adopted across various operational stages due to their precision, high efficiency, environmental sustainability, and simplicity of operation. However, present-day technological advancement levels and relevant policy regulations pose significant restrictions on UAVs in terms of payload and endurance, leading to diminished task efficiency when a single UAV is deployed over large areas. Accordingly, this paper aggregates and analyzes research pertaining to UAV swarms from databases such as Google Scholar, ScienceDirect, Scopus, IEEE Xplorer, and Wiley over the past decade. An initial overview presents the current control methods for UAV swarms, incorporating a summary and analysis of the features, merits, and drawbacks of diverse control techniques. Subsequently, drawing from the four main stages of agricultural production (cultivation, planting, management, and harvesting), we evaluate the application of UAV swarms in each stage and provide an overview of the most advanced UAV swarm technologies utilized therein. Finally, we scrutinize and analyze the challenges and concerns associated with UAV swarm applications on unmanned farms and provide forward-looking insights into the future developmental trajectory of UAV swarm technology in unmanned farming, with the objective of bolstering swarm performance, scalability, and adoption rates in such settings.

Path planning for fast swarm source seeking in unknown environments

SummaryThis article proposes a novel path planning method for source seeking by UAV swarm using extremum seeking control mechanism and combined artificial potential functions, without the knowledge of position information. In particular, the swarm source seeking problem is reformulated as a minimum seeking of a combined artificial potential function that considers the potentials of obstacles, source, and collision avoidance between UAVs. The explicit form of the combined artificial potential function is unknown and its value can be measured by the sensors mounted on UAVs. The fast extremum seeking mechanism based on the normalized extremum seeking method is exploited to find the gradient of the combined artificial potential function and the target is located without using its position information. Finally, extensive simulations of source seeking for single UAV and swarm are presented to illustrate the effectiveness of the proposed path planning method.

Studying the Performance Features of the Scenario Algorithm in the Control System of an Unmanned Aerial System

In this paper, we consider the results of a full-scale experiment carried out under conditions most closely resembling real operating ones. The goal of the experiment is to identify the performance features of the control system inside an unmanned aerial system (UAS) when using the swarm control method of a decentralized UAS control strategy. The considered flight performance characteristics of a single UAV provide an opportunity to analyze the operation feasibility of the presented UAS to solve other kinds of problems. The results of the presented experiment enable the development of a set of recommendations for further research directions. Also, they provide future UAV operators with better and faster achievement of practical goals during the practical operation of UAS in various conditions. The described features and disadvantages of various BAS control strategies help determine probable options for the BAS configuration depending on the nature of the tasks to be solved later.

Uplink CoMP Transmission for Cellular-Connected UAV Networks

In this letter, we study the coordinated multipoint (CoMP) scheme in the uplink cellular-connected UAV network. By considering the LoS and NLoS components in air-to-ground (A2G) transmission, we adopt the strongest average received signal strength (RSS)-based CoMP scheme. We then develop an analytical framework to evaluate the network performance in terms of coverage probability and area spectral efficiency (ASE) for the case of two cooperative base stations (BSs), followed by an extension to a generalized number of cooperative BSs. Compared to the CoMP scheme with single UAV tier and Non-CoMP baseline, the considered CoMP scheme is able to leverage favored Line-of-Sight (LoS) transmission, lower path loss and BS cooperation gain.

ITUAVs: Intermittently Tethered UAVs for Future Wireless Networks

We propose the intermittently tethered unmanned aerial vehicle (iTUAV) as a tradeoff between the power availability of a tethered UAV (TUAV) and the flexibility of an untethered UAV. An iTUAV can provide cellular connectivity while being temporarily tethered to the most adequate ground anchor. Also, it can flexibly detach from one anchor, travel, then attach to another one to maintain/improve the coverage quality for mobile users. Hence, we discuss here the existing UAV-based cellular networking technologies, followed by a detailed description of the iTUAV system, its components, and mode of operation. Subsequently, we present a comparative study of the existing and proposed systems highlighting the differences in key features such as mobility and energy. To emphasize the potential of iTUAV systems, we conduct a case study, evaluate the iTUAV performance, and compare it to benchmarks. Obtained results show that with only 10 anchors in the area, the iTUAV system can serve up to 90% of the users covered by the untethered UAV swapping system. Moreover, results from a small case study prove that the iTUAV allows to balance performance/cost and can be implemented realistically. For instance, when user locations are clustered, with only 2 active iTUAVs and 4 anchors, achieved performance is superior to that of the system with 3 TUAVs, while when considering a single UAV on a 100 minutes event, a system with only 6 anchors outperforms the untethered UAV as it combines location flexibility with increased mission time.

Unmanned Aerial Vehicle Direct Seeding Versus Ground Seeding Mechanization Services in Smallholder Farming Systems of North West IGP on Energy Use Efficiency and Quality of Rice Culture: A Review

Various sectors use AI technologies to increase output and productivity. AI in agriculture also enables farmers to boost productivity while minimizing harmful environmental effects. AI is transforming the food-processing industry, where agricultural emissions have dropped by 20%. Mechanization is once again one of the top development policy targets for changing the smallholder agricultural system in the north west IGP. To support public investment or company development programs, however, previous and ongoing efforts frequently suffer from a lack of scientific data on end-user effective demand for various sorts of mechanical improvements. Rice production involves intricate agronomic procedures. Seeding, fertilizing, and pesticide application are labour- and time-intensive tasks that have low automation efficiency. Currently, a lot of research focuses on the single UAV operation on rice, but there aren't many applications that cover the entire process of sowing, fertilizing, and applying pesticides. Based on the intelligent operating platform, a mUAV was created to oversee the planting of rice. This aircraft accomplished three tasks on the same flight platform: seeding, fertilizer application, and pesticide application. Machine design was carried out using simulations of CFD. The cultivation patterns of mechanical rice direct seeder, mechanical rice transplanter, and mUAV seeding were compared to perform a comparative evaluation of the entire process. With improved rice automation, fewer labour inputs, and lower costs, it is intended that this evaluation will offer new machinery for rice farming patterns in various conditions.

MAESTRO-X: Distributed Orchestration of Rotary-Wing UAV-Relay Swarms

This work details a scalable framework to orchestrate a swarm of rotary-wing UAVs serving as cellular relays to facilitate beyond line-of-sight connectivity and traffic offloading for ground users. First, a Multiscale Adaptive Energyconscious Scheduling and TRajectory Optimization (MAESTRO) framework is developed for a single UAV. Aiming to minimize the time-averaged latency to serve user requests, subject to an average UAV power constraint, it is shown that the optimization problem can be cast as a semi-Markov decision process, and exhibits a multiscale structure: outer actions on radial wait velocities and terminal service positions minimize the longterm delay-power trade-off, optimized via value iteration; given these outer actions, inner actions on angular wait velocities and service trajectories minimize a short-term delay-energy cost; finally, rate adaptation is embedded along the trajectory to leverage air-to-ground channel propagation conditions. A novel hierarchical competitive swarm optimization scheme is developed in the inner optimization, to devise high-resolution trajectories via iterative pair-wise updates. Next, MAESTRO is eXtended to UAV swarms (MAESTRO-X) via scalable policy replication, enabled by a decentralized command-and-control network augmented with: (1) spread maximization to proactively position UAVs to serve future requests; (2) consensus-driven conflict resolution to orchestrate scheduling decisions based on delay-energy costs including queuing dynamics; (3) adaptive frequency reuse to improve spectrum utilization across the network; and (4) a piggybacking mechanism allowing UAVs to serve multiple ground users simultaneously. Numerical evaluations show that, for user requests of 10 Mbits, generated according to a Poisson arrival process with rate 0.2 req/min/UAV, single-agent MAESTRO offers 3.8× faster service than a high-altitude platform and 29% faster than a static UAV deployment; moreover, for a swarm of 3 UAV-relays, MAESTRO-X delivers data payloads 4.7× faster than a successive convex approximation scheme; and remarkably, a single UAV optimized via MAESTRO outclasses 3 UAVs optimized via a deep-Q network by 38%.

Determine stormwater pond geometrics and hydraulics using remote sensing technologies: A comparison between airborne-LiDAR and UAV-photogrammetry field validation against RTK-GNSS

Abstract Flow-regulated stormwater ponds providing safe outflow discharges prevail as the primary stormwater management tool for stream protections. Detailed pond geometries are essential metrics in pond monitoring technologies, which convert the point-based water level measurements to areal/volumetric ponding water estimations. Unlike labour-intensive surveys (e.g., RTK-GNSS or total stations), UAV-photogrammetry and airborne-LiDAR have been advocated as cost-effective alternatives to acquire high-quality datasets. In this paper, we compare the use of these two approaches for stormwater pond surveys. With reference to RTK-GNSS in-situ observations, we identify their geometric and hydraulic discrepancies based on six stormwater ponds from three aspects: (i) DEMs, (ii) stage-curves and (iii) outflow discharges. Three main findings are outlined: (i) for wet ponds where moisture environments are dominant, UAV-photogrammetry outperforms (infrared) airborne-LiDAR, where airborne-LiDAR yields 0.15–0.54 NSEoutflow, which is unacceptable; (ii) for dry ponds, UAV-photogrammetry obtains 0.88–0.89 NSEoutflow as poor vegetation penetrations; two correction methods (i.e., grass removal and shifted stage-curves) are proposed, indicating good alignment to RTK-GNSS observations and (iii) UAV-photogrammetry delivers <0.1 m resolution in outlining break-line features for stormwater pond structures. With significant economic advantages, the multi-UAV collaborative photogrammetry would address the shortcomings of a single UAV and thus pave the way for large urban catchment/watershed survey applications.

Research on Insulator Defect Detection Based on Improved YOLOv7 and Multi-UAV Cooperative System

Insulator self-blasts, cracked insulators, and bird nests often lead to large-scale power outages and safety accidents, while the detection system based on a single UAV and YOLOv7 is difficult to meet the speed and accuracy requirements in actual detection. Therefore, a novel insulator defect detection method based on improved YOLOv7 and a multi-UAV collaborative system is proposed innovatively. Firstly, a complete insulator defects dataset is constructed, and the introduction of insulator self-blasts, cracked insulators, and bird nest images avoids the problem of low reliability for single defect detection. Secondly, a multi-UAV collaborative platform is proposed, which improves the search scope and efficiency. Most critically, an improved YOLOv7-C3C2-GAM is proposed. The introduction of the C3C2 module and the CNeB2 structure improves the efficiency and accuracy of feature extraction, and the introduction of a global attention mechanism (GAM) improved the feature extraction ability to extract key information about small targets or occluded targets and feature in the region of interest. Compared with YOLOv7, the accuracies of YOLOv7-C3C2 and YOLOv7-C3C2-GAM are improved by 1.3% and 0.5%, respectively, the speed of YOLOv7-C3C2 is improved by 0.1 ms, and the lightweight sizes are reduced by 8.2 Mb and 8.1 Mb, respectively. Therefore, the proposed method provides theoretical and technical support for power equipment defect detection.

Secure Transmission for Multi-UAV-Assisted Mobile Edge Computing Based on Reinforcement Learning

UAV communication has received widespread attention in MEC systems due to its high flexibility and line-of-sight transmission. Users can reduce their local computing pressures and computation delay by offloading tasks to the UAV as an edge server. However, the coverage capability of a single UAV is very limited. Moreover, the data offloaded to the UAV will be easily eavesdropped. Thus, in this paper, we propose two secure transmission methods for multi-UAV-assisted mobile edge computing based on the single-agent and multi-agent reinforcement learning, respectively. In the proposed methods, we first utilize the spiral placement algorithm to optimize the deployment of UAVs, which covers all users with the minimum number of UAVs. Then, to reduce the information eavesdropping by a flying eavesdropper, we utilize the reinforcement learning to optimize the secure offloading to maximize the system utility by considering different types of users’ tasks with diverse preferences for residual energy of computing equipment and processing delay. Simulation results indicate that compared with the single-agent method and the benchmark, the multi-agent method can optimize the offloading in a better manner and achieve larger system utility.

Research on the Influence of UAV Anti-collision Device on Aerodynamic Shape

With the widespread use of UAVs, accidents with UAVs are inevitable. It is necessary to design an anti-collision UAV suitable for aircraft inspection. CFD simulation software is used to study the effect of adding anti-collision device on the hovering, flow field characteristics and aerodynamic shape of the UAV. The model parameters are set according to the actual situation, and the flow field characteristics of a single propeller, UAV and anti-collision UAV are simulated and analyzed in the hovering state. The analysis of the flow field determines the aerodynamic interference of the UAV anti-collision device, which provides a basis for verifying the feasibility of the design scheme.