Single Unmanned Aerial Vehicles Research Articles

Enhanced Multi-UAV Path Planning in Complex Environments With Voronoi-Based Obstacle Modelling and Q-Learning

To tackle the challenge of obstacle avoidance path planning for multiple unmanned aerial vehicles (UAVs) in intricate environments, this study introduces a Voronoi graph–based model to represent the obstacle-laden environment and employs a Markov decision process (MDP) for single UAV path planning. The traditional Q-learning algorithm is enhanced by adjusting the initial state of the Q-table and fine-tuning the reward and penalty values, enabling the acquisition of efficient obstacle avoidance paths for individual UAVs in complex settings. Leveraging the improved Q-learning algorithm for single UAVs, the Q-table is iteratively refined for a fleet of UAVs, with dynamic modifications based on the waypoints chosen by each UAV. This approach ensures the generation of collision-free paths for multiple UAVs, as validated by simulation results that showcase the algorithm’s effectiveness in learning from past training data. The proposed method offers a robust framework for practical UAV trajectory generation in complex environments.

Air-borne stereo observation of ocean wave height using two independent cameras from a single unmanned aerial vehicle

Air-borne stereo observation of ocean wave height using two independent cameras from a single unmanned aerial vehicle

Formation Control of Quadrotor UAVs

In recent years, the rapid development of quadrotor unmanned aerial vehicles (UAVs) has resulted in their widespread applications across various scenarios. However, single UAVs encounter multiple challenges during mission execution. Consequently, an increasing number of tasks are being conducted in the form of UAV formations. This paper focuses on quadrotor UAVs and proposes a formation control scheme that can be validated through simulation and experimentation. The research begins with the dynamic modeling of quadrotor UAVs and the design of an experimental platform for UAVs. The formation structure of UAVs is then developed based on the Leader-Follower theory, and a multi-type PID controller is employed to design the formation control algorithm.

An aerial framework for Multi-View grape bunch detection and route Optimization using ACO

Typically, commercial orchards and vineyards consist of large fields that encounter similar development phases at once. Thus, it becomes necessary to efficiently fly over all fields to detect fruit and identify their status in a very limited timeframe. For this purpose, Unmanned Aerial Vehicles (UAVs) path planning plays a pivotal role in agriculture as it enables optimal coverage of agricultural fields, leading to enhanced data acquisition and improved precision agriculture practices, for instance, disease assessment and pesticide application. In addition, deep learning techniques offer precise image analysis. On the one hand, object detection has been applied to agricultural fields to carry out a wide range of operations, such as detecting apples and predicting yield in vineyards, with higher detection accuracy when the fruits are fully visible. On the other hand, when crops present leaf-occlusion, the algorithms face difficulties and are unable to adapt to the specific characteristics of the field. Therefore, this study seeks to address this issue by developing a novel framework to enhance UAV path planning for data collection in vineyards, considering the current biophysical environment. To this end, the proposed framework requires two flights: i) a first flight (survey) to acquire insights on the crop structure and environment, and ii) a second flight using the Ant Colony Optimization Max-Min Ant System (ACO-MMAS) algorithm to enhance image acquisition by considering multiple angles to overcome partial leaf-occlusion. Further, the optimisation algorithm can potentially boost the acquisition of datasets for fruit detection by considering single and multiple UAVs flying synchronously while ensuring a safe distance between platforms and efficient coverage. The method was tested in two vineyards with different environmental characteristics, increasing levels of difficulty and acquired during two different growing seasons. It improved the length of the computed paths by up to 24%, compared to a base algorithm that considers only the closest point without any optimisation, improving the decision-making processes and resource allocation in crop management.

Energy-efficient UAV communication: A NOMA scheme with resource allocation and trajectory optimization.

This work investigates a downlink nonorthogonal multiple access (NOMA) scheme with unmanned aerial vehicle (UAV) aided wireless communication, where a single UAV was regarded as an air base station (ABS) to communicate with multiple ground users. Considering the constraints of velocity and maneuverability, a UAV energy efficiency (EE) model was proposed via collaborative design resource allocation and trajectory optimization. Based on this, an EE maximization problem was formulated to jointly optimize the transmit power of ground users and the trajectory of the UAV. To obtain the optimal solutions, this nonconvex problem was transformed into an equivalent convex optimization problem on the basis of three user clustering algorithms. After several alternating iterations, our proposed algorithms converged quickly. The simulation results show an enhancement in EE with NOMA because our proposed algorithm is nearly 99.6% superior to other OMA schemes.

Distributed formation control for leader‐follower quadrotor unmanned aerial vehicles system based on practical sliding mode and event‐triggered mechanisms

AbstractThis article investigates the distributed formation trajectory tracking problem of a homogeneous quadrotor unmanned aerial vehicles (UAVs) system using a leader‐follower consensus protocol in three‐dimensional space. To enable the follower UAVs in the swarm to track a single leader UAV and form a desired formation under undirected topology, a six‐degree‐of‐freedom dynamic model with bounded time‐varying disturbances is established, which is divided into a tracking subsystem and a stabilizing subsystem. A practical sliding mode control (PSMC) based event‐triggered algorithm is proposed to achieve distributed formation control for a leader‐follower quadrotor UAVs system and reduce the network transmission resources. To address the potential issue of inter‐UAVs collisions during formation, an artificial potential field (APF) method is introduced in the tracking subsystem, and an disturbance observer is introduced in the stabilizing subsystem to estimate the unknown external disturbances. The boundedness of signals in the closed‐loop system is demonstrated using Lyapunov stability theory, and the exclusion of Zeno behavior can be ensured by having a positive lower bound on the inter‐event time between two consecutive triggering events. Finally, the effectiveness of the proposed event‐triggered algorithm is validated through simulation examples.

UAV-Enabled Integrated Sensing and Communication: Opportunities and Challenges

Unmanned aerial vehicle (UAV)-enabled integrated sensing and communication (ISAC) has attracted growing research interests in the context of sixth-generation (6G) wireless networks, in which UAVs will be exploited as aerial wireless platforms to provide better coverage and enhanced sensing and communication (S&C) services. However, due to the size, weight, and power (SWAP) constraints of UAVs, their controllable mobility, and the line-of-sight (LoS) air-ground channels, UAVenabled ISAC introduces new opportunities and challenges. This article provides an overview of UAV-enabled ISAC and proposes various solutions for optimizing the S&C performance. In particular, we first introduce UAV-enabled joint S&C and discuss UAV motion control, wireless resource allocation, and interference management for ISAC systems employing single and multiple UAVs. Then, we present two application scenarios for exploiting the synergy between S&C, namely sensing-assisted UAV communication and communication-assisted UAV sensing. Finally, we highlight several interesting research directions to guide and motivate future work.

See farther and more: a master-slave UAVs based synthetic optical aperture imaging system with wide and dynamic baseline.

An open challenge remained in designing an optical system to capture the aerial image with a wide field of view (FoV) and high resolution. The optical system of one camera from a single unmanned aerial vehicle (UAV) can hardly promise the FoV and resolution. The conventional swarm UAVs can form the camera array with a short or fixed baseline. They can capture the images with a wide FoV and high resolution, but the cost is the requirement of many UAVs. We aim to design a camera array with a wide and dynamic baseline to reduce the demand for UAVs to organize a synthetic optical aperture. In this thought, we propose a master-slave UAVs-based synthetic optical aperture imaging system with a wide and dynamic baseline. The system consists of one master UAV and multiple slave UAVs. Master and slave UAVs provide the global and local FoVs, respectively, and improve the efficiency of image acquisition. In such a system, fusing UAV images becomes a new challenge due to two factors: (i) the small FoV overlap of slave UAVs and (ii) the gap in resolution scale from slave to master UAV images. To deal with it, a coarse-to-fine stitching method is proposed to stitch up the multi-view images into one to obtain a wide FoV with high resolution. A video stabilization method has also been designed for the proposed imaging system. Challenges caused by wide and dynamic baselines can thus be solved by the above methods. Actual data experiments demonstrate that the proposed imaging system achieves high-quality imaging results.

Unmanned Aerial Vehicle-Based Structural Health Monitoring and Computer Vision-Aided Procedure for Seismic Safety Measures of Linear Infrastructures.

Computer vision in the structural health monitoring (SHM) field has become popular, especially for processing unmanned aerial vehicle (UAV) data, but still has limitations both in experimental testing and in practical applications. Prior works have focused on UAV challenges and opportunities for the vibration-based SHM of buildings or bridges, but practical and methodological gaps exist specifically for linear infrastructure systems such as pipelines. Since they are critical for the transportation of products and the transmission of energy, a feasibility study of UAV-based SHM for linear infrastructures is essential to ensuring their service continuity through an advanced SHM system. Thus, this study proposes a single UAV for the seismic monitoring and safety assessment of linear infrastructures along with their computer vision-aided procedures. The proposed procedures were implemented in a full-scale shake-table test of a natural gas pipeline assembly. The objectives were to explore the UAV potential for the seismic vibration monitoring of linear infrastructures with the aid of several computer vision algorithms and to investigate the impact of parameter selection for each algorithm on the matching accuracy. The procedure starts by adopting the Maximally Stable Extremal Region (MSER) method to extract covariant regions that remain similar through a certain threshold of image series. The feature of interest is then detected, extracted, and matched using the Speeded-Up Robust Features (SURF) and K-nearest Neighbor (KNN) algorithms. The Maximum Sample Consensus (MSAC) algorithm is applied for model fitting by maximizing the likelihood of the solution. The output of each algorithm is examined for correctness in matching pairs and accuracy, which is a highlight of this procedure, as no studies have ever investigated these properties. The raw data are corrected and scaled to generate displacement data. Finally, a structural safety assessment was performed using several system identification models. These procedures were first validated using an aluminum bar placed on an actuator and tested in three harmonic tests, and then an implementation case study on the pipeline shake-table tests was analyzed. The validation tests show good agreement between the UAV data and reference data. The shake-table test results also generate reasonable seismic performance and assess the pipeline seismic safety, demonstrating the feasibility of the proposed procedure and the prospect of UAV-based SHM for linear infrastructure monitoring.

Co-Operatively Increasing Smoothing and Mapping Based on Switching Function

Collaborative localization is a technique that utilizes the exchange of information between multiple sensors or devices to improve localization accuracy and robustness. It has a wide range of applications in autonomous driving and unmanned aerial vehicles (UAVs). In the field of UAVs, collaborative localization can help UAVs perform autonomous navigation and mission execution in complex environments. However, when GNSS is not available, it becomes challenging to position the UAV swarm relative to each other. This is because the swarm loses its perception and constraint of the position relationship between each member. Consequently, the swarm faces the problem of the serious drift of relative accuracy for an extended period. Furthermore, when the environment is faced with complex obstruction challenges or a camera with low texture scenes, noise can make it more difficult to solve the relative position relationship between drones, and a single UAV may lose positioning capability. To solve these specific problems, this paper studies a swarm co-operative localization method in a GNSS-denied environment with loud noise interference. In this paper, we proposed a method that utilizes a distributed scheme based on an incremental smoothing and mapping (iSAM) algorithm for state estimation. It incorporates new anchor-free topological constraints to prevent positioning failures and significantly improve the system’s robustness. Additionally, a new switching function is applied in front of each factor of the loss function, which adjusts the switches in real time in response to the input information to improve observably the accuracy of the system. A novel co-operative incremental smoothing and mapping (CI-SAM) method is proposed and the method does not require a complete relative position measurement, which reduces the need for vehicle measurement equipment configuration. The effectiveness of the method is verified by simulation.

A Bio-Inspired Safety Control System for UAVs in Confined Environment With Disturbance.

This article presents a bio-inspired safety control scheme for unmanned aerial vehicles (UAVs) in confined environments with disturbance. Although there has been some existing research on the effect of disturbance for a single UAV, multi-UAV formation under external wind disturbances remains challenging, especially in a tight and confined environment. Inspired by nature, this study concentrates on an anti-disturbance mechanism for safe multi-UAV formation in a tight environment. The presented safety control system combines disturbance observer-based control (DOBC), bionic formation switching (BFS) strategy, and safety evaluation. Two safety issues are considered in this article. For a single UAV, the estimated disturbance is compensated in the inner-loop controller. While for multi-UAV formation, the BFS strategy attenuates the effect of external wind disturbance leveraging the formation configuration. The so-called group perturbation immune factor (GPIF) is designed to analyze and evaluate the safety of the overall formation. The experimental results validate the comprehensiveness and anti-disturbance capability of the system.

Mobile-IRS assisted next generation UAV communication networks

Prior research on intelligent reflection surface (IRS)-assisted unmanned aerial vehicle (UAV) communications has focused on a fixed location for the IRS or mounted on a UAV. The assumption that the IRS is located at a fixed position will prohibit mobile users from maximizing many wireless network benefits, such as data rate and coverage. Furthermore, assuming that the IRS is placed on a UAV is impractical for various reasons, including the IRS’s weight and size and wind speed in severe weather. Unlike previous studies, this study assumes a single UAV and an IRS mounted on a mobile ground vehicle (M-IRS) to be deployed in an Internet-of-Things (IoT) 6G wireless network to maximize the average data rate. Such a methodology for providing wireless coverage using an M-IRS assisted UAV system is expected in smart cities. In this paper, we formulate an optimization problem to find an efficient trajectory for the UAV, an efficient path for the M-IRS, and users’ power allocation coefficients that maximize the average data rate for mobile ground users. Due to its intractability, we propose efficient techniques to help find the optimization problem’s solution. First, we show that our dynamic power allocation technique outperforms the fixed power allocation technique in the network average sum rate. Then we employ the individual movement model (Random Waypoint Model) in order to represent the users’ movements inside the coverage area. Finally, we propose an efficient approach using a Genetic Algorithm (GA) to find an efficient trajectory for the UAV and an efficient path for the M-IRS to provide wireless connectivity for mobile users during their movement. We demonstrate through simulations that our methodology can enhance the average data rate by 15% on average compared with the static IRS and by 25% on average compared to without the IRS system.

UAVs Path Planning Using Visual-SLAM Technique Based Hybrid Particle Swarm Optimization

Abstract Due to their excellent mobility on various robotics platforms, unmanned-aerial vehicles (UAVs) are becoming extremely popular. We trace the UAV poses while simultaneously creating an iterative and progressive map of the surrounding area using a cutting-edge VSLAM technique, termed as visual simultaneous localization and mapping. In this case, a single UAV initially creates a map of the area of interest using a monocular vision-based method. In order to determine the best pathways for several UAVs, the created map is treated as an input for the optimization method. UAVs need to execute missions effectively, and they need to access the best route quickly in a challenging environment. This necessitates solving the automatic path planning problem. In this paper, a new hybrid particle swarm optimization (HPSO) technique is suggested as a solution to this issue. The proposed algorithm enhances the optimization capability and prevents dropping into local convergence by combining the simulated annealing algorithm; each particle integrates the advantageous information of the optimization method in accordance with the dimensional learning approach, which reduces the occurrence of particles fluctuation during the transition process and improves the convergence speed. Additionally, we proposed dynamic fitness function (DFF) in order to assess the path planner’s planning approach while taking into account a variety of optimization parameters, including the calculation of flight risk, energy usage, and operation completion time. The efficiency of our proposed H-PSO-VSLAM system, as shown by the simulation results, is validated by the recommended planner’s high fitness value and safe arrival at the destination while avoiding all unanticipated dangerous events and restricted locations.

Tracking and Grasping of Moving Objects Using Aerial Robotic Manipulators: A Brief Survey

Unmanned Aerial Vehicles (UAV) has evolved in recent years, their features have changed to be more useful to the society, although some years ago the drones had been thought to be teleoperated by humans and to take some pictures from above, which is useful; nevertheless, nowadays the drones are capable of developing autonomous tasks like tracking a dynamic target or even grasping different kind of objects. Some task like transporting heavy loads or manipulating complex shapes are more challenging for a single UAV, but for a fleet of them might be easier. This brief survey presents a compilation of relevant works related to tracking and grasping with aerial robotic manipulators, as well as cooperation among them. Moreover, challenges and limitations are presented in order to contribute with new areas of research. Finally, some trends in aerial manipulation are foreseeing for different sectors and relevant features for these kind of systems are standing out.

Three-Dimensional Reconstruction of Railway Bridges Based on Unmanned Aerial Vehicle–Terrestrial Laser Scanner Point Cloud Fusion

To address the incomplete image data collection of close-to-ground structures, such as bridge piers and local features like the suspension cables in bridges, obtained from single unmanned aerial vehicle (UAV) oblique photography and the difficulty in acquiring point cloud data for the top structures of bridges using single terrestrial laser scanners (TLSs), as well as the lack of textural information in TLS point clouds, this study aims to establish a high-precision, complete, and realistic bridge model by integrating UAV image data and TLS point cloud data. Using a particular large-scale dual-track bridge as a case study, the methodology involves aerial surveys using a DJI Phantom 4 RTK for comprehensive image capture. We obtain 564 images circling the bridge arches, 508 images for orthorectification, and 491 images of close-range side views. Subsequently, all images, POS data, and ground control point information are imported into Context Capture 2023 software for aerial triangulation and multi-view image dense matching to generate dense point clouds of the bridge. Additionally, ground LiDAR scanning, involving the placement of six scanning stations both on and beneath the bridge, was conducted and the point cloud data from each station are registered in Trimble Business Center 5.5.2 software based on identical feature points. Noise point clouds are then removed using statistical filtering techniques. The integration of UAV image point clouds with TLS point clouds is achieved using the iterative closest point (ICP) algorithm, followed by the creation of a TIN model and texture mapping using Context Capture 2023 software. The effectiveness of the integrated modeling is verified by comparing the geometric accuracy and completeness of the images with those obtained from a single UAV image-based model. The integrated model is used to generate cross-sectional profiles of the dual-track bridge, with detailed annotations of boundary dimensions. Structural inspections reveal honeycomb surfaces and seepage in the bridge piers, as well as painted rust and cracks in the arch ribs. The geometric accuracy of the integrated model in the X, Y, and Z directions is 1.2 cm, 0.8 cm, and 0.9 cm, respectively, while the overall 3D model accuracy is 1.70 cm. This method provides technical reference for the reconstruction of three-dimensional point cloud bridge models. Through 3D reconstruction, railway operators can better monitor and assess the condition of bridge structures, promptly identifying potential defects and damages, thus enabling the adoption of necessary maintenance and repair measures to ensure the structural safety of the bridges.

Cache‐aided UAV‐assisted relaying networks: Performance analysis and system optimization

AbstractThe utilization of distributed multi‐agent unmanned aerial vehicles (UAVs) for computing tasks in remote areas has gained significant traction in recent years due to their adaptability and capability to access hard‐to‐reach regions that are inaccessible to ground‐based methods. However, establishing wireless communication between UAVs and ground‐based data sources in remote areas presents considerable challenges, particularly when UAVs are in motion. To tackle this challenge, this article investigates a cache‐aided relaying system in the presence of UAVs, wherein a ground‐based decode‐and‐forward relay equipped with cache space is deployed to facilitate wireless communication between UAVs and a central data source. Within the scope of this system, we first analyze the probability of transmission outage, providing an analytical expression for performance evaluation. We commence with the case of a single stationary UAV, subsequently expanding to multiple stationary UAVs, and ultimately incorporating multiple dynamic UAVs. Subsequently, we enhance the system performance by minimizing the outage probability through efficient power resource allocation among users. By means of mathematical modeling and simulations, this research examines the influence of various factors, including the cache size at the relay and the working mode of the UAV, on the system performance. Finally, simulations are conducted to validate the proposed analysis.

Path Planning with Multiple UAVs Considering the Sensing Range and Improved K-Means Clustering in WSNs

Recently, an Unmanned Aerial Vehicle (UAV)-based Wireless Sensor Network (WSN) for data collection was proposed. Multiple UAVs are more effective than a single UAV in wide WSNs. However, in this scenario, many factors must be considered, such as collision avoidance, the appropriate flight path, and the task time. Therefore, it is important to effectively divide the mission areas of the UAVs. In this paper, we propose an improved k-means clustering algorithm that effectively distributes sensors with various densities and fairly assigns mission areas to UAVs with comparable performance. The proposed algorithm distributes mission areas more effectively than conventional methods using cluster head selection and improved k-means clustering. In addition, a postprocessing procedure for reducing the path length during UAV path planning for each mission area is important. Thus, a waypoint refinement algorithm that considers the sensing ranges of the sensor node and the UAV is proposed to effectively improve the flight path of the UAV. The task completion time is determined by evaluating how the UAV collects data through communication with the cluster head node. The simulation results show that the mission area distribution by the improved k-means clustering algorithm and postprocessing by the waypoint refinement algorithm improve the performance and the UAV flight path during data collection.

Distributed Navigation in Emergency Scenarios: A Case Study on Post-Avalanche Search and Rescue Using Drones

Unmanned aerial vehicles (UAVs) are increasingly employed in civil applications due to their ease of use and adaptability. This paper proposes a distributed navigation strategy for a formation of UAVs in post-avalanche search-and-rescue (SAR) operations. Formations offer a more efficient approach than single UAVs in dynamic and complex operational environments. Additionally, they can distribute different sensors, reducing payload and increasing robustness and overall efficiency. The proposed navigation algorithm relies on the Kalman filter (KF) based on consensus to distribute state estimation, and internodal transformation theory to improve system scalability, preserving the dynamic equivalence between the global and local models. The effectiveness of this approach was tested in two realistic scenarios, resulting in the ability to detect victims and maintain situational awareness while avoiding unsearched areas. The proposed approach offers a promising alternative to human-intensive SAR missions.

Improved path planning method for unmanned aerial vehicles based on artificial potential field

With the advancement of unmanned aerial vehicle (UAV) technology and its widespread use in many facets of manufacturing and daily life, the need for UAV mission automation is becoming more and more practical. In order to improve the automatic obstacle avoidance and path planning performance of UAVs, this essay proposes an optimized route planning algorithm based on the artificial potential field (APF) method, which have solved the typical issue of the APF. This method chooses to conduct a pre planning trajectory of the UAV based on a rapidly expanding random tree (RRT). The pre-planned path will be split into continuous particles, and then generating intermediate waypoints. A nearby waypoint offers a gravitational force to aid the UAV in escaping the local minimum when it enters it. At the same time, taking the distance from the UAV to the obstacle and the radius of influence of the obstacle itself into consideration, dynamically adjust the gravitational and repulsive coefficients, set up a non-gravitational zone around the obstacle, which is beneficial for the UAV to elude the obstacle. And set up a repulsive limited action zone to reduce unnecessary turns in the trajectory to achieve the effect of path optimization. Considering the difficulty of single UAV missions in most cases, this paper discusses cooperative flight path planning for multiple UAVs into consideration.

Cooperative situational awareness of multi-UAV system based on improved D-S evidence theory

Situational awareness (SA) of unmanned aerial vehicles (UAVs) has been a research hotspot over the decades. Existing research mainly focuses on the SA of a single UAV in two-dimensional planes, whereas obstacles are assumed a priori. To eliminate these constraints, this study considers the cooperative situational awareness (CSA) problem of multi-UAV systems in the scenario of crossing a three-dimensional (3D) obstacle belt while no prior information of obstacles is required. First, the distribution models of the multi-UAV system and the obstacles are built based on two reference frames. Second, various types of uncertainties are characterized, which reflect an urgent need for CSA. Thus, a centralized CSA scheme is proposed and conducted on multiple UAVs and at different times, and the Dempster-Shafer (D-S) evidence theory is introduced to address information uncertainties and achieve high-accuracy information fusion. Next, to deal with the high-conflict evidence situations that are common in practice, a modified D-S fusion method is further developed. A modified Pearson coefficient is utilized to measure the correlation between different pieces of evidence. Both information credibility and uncertainty are taken into account to evaluate the evidence from different perspectives, and a novel evidence weight assignment method is presented to treat high-conflict situations. Numerical simulations validate the effectiveness of the proposed CSA method. Compared to existing studies, the proposed method is applicable to different trust paradoxes and achieves the best performance among various fusion methods.