Search Results (364)

Area coverage and target tracking are important applications of UAV swarms. However, attempting to perform both tasks simultaneously can be a challenge, particularly under resource constraints. In such scenarios, UAV swarms must collaborate to cover extensive areas while simultaneously tracking multiple targets. This paper proposes a deep reinforcement learning (DRL)-based, scalable UAV swarm control method for a simultaneous coverage and tracking (SCT) task, called the SCT-DRL algorithm. SCT-DRL simplifies the interaction between UAV swarms into a series of pairwise interactions and aggregates the information of perceived targets in advance, based on which forms the control framework with a variable number of neighboring UAVs and targets. Another highlight of SCT-DRL is using the trajectories of the traditional one-step optimization method to initialize the value network, which encourages the UAVs to select the actions leading to the state with less rest time to task completion to avoid extensive random exploration at the beginning of training. SCT-DRL can be seen as a special improvement of the traditional one-step optimization method, shaped by the samples derived from the latter, and gradually overcomes the inherent myopic issue with the far-sighted value estimation through RL training. Finally, the effectiveness of the proposed method is demonstrated through numerical experiments. Full article

In order to improve the network performance of multi-unmanned ground vehicle (UGV) systems in urban environments, this article proposes a novel online autonomous motion-control method for the relay UAV. The problem is solved by jointly considering unknown RF channel parameters, unknown multi-agent mobility, the impact of the environments on channel characteristics, and the unavailable angle-of-arrival (AoA) information of the received signal, making the solution of the problem more practical and comprehensive. The method mainly consists of two parts: wireless channel parameter estimation and optimal relay position search. Considering that in practical applications, the radio frequency (RF) channel parameters in complex urban environments are difficult to obtain in advance and are constantly changing, an estimation algorithm based on Gaussian process learning is proposed for online evaluation of the wireless channel parameters near the current position of the UAV; for the optimal relay position search problem, in order to improve the real-time performance of the method, a line search algorithm and a general gradient-based algorithm are proposed, which are used for point-to-point communication and multi-node communication scenarios, respectively, reducing the two-dimensional search to a one-dimensional search, and the stability proof and convergence conditions of the algorithm are given. Comparative experiments and simulation results under different scenarios show that the proposed motion-control method can drive the UAV to reach or track the optimal relay position and improve the network performance, while demonstrating that it is beneficial to consider the impact of the environments on the channel characteristics. Full article

Accurately understanding the stand composition of shelter forests is essential for the construction and benefit evaluation of shelter forest projects. This study explores classification methods for dominant tree species in shelter forests using UAV-derived RGB, hyperspectral, and LiDAR data. It also investigates the impact of individual tree crown (ITC) delineation accuracy, crown morphological parameters, and various data sources and classifiers. First, as a result of the overlap and complex structure of tree crowns in shelterbelt forests, existing ITC delineation methods often lead to over-segmentation or segmentation errors. To address this challenge, we propose a watershed and multi-feature-controlled spectral clustering (WMF-SCS) algorithm for ITC delineation based on UAV RGB and LiDAR data, which offers clearer and more reliable classification objects, features, and training data for tree species classification. Second, spectral, texture, structural, and crown morphological parameters were extracted using UAV hyperspectral and LiDAR data combined with ITC delineation results. Twenty-one classification images were constructed using RF, SVM, MLP, and SAMME for tree species classification. The results show that (1) the proposed WMF-SCS algorithm demonstrates significant performance in ITC delineation in complex mixed forest scenarios (Precision = 0.88, Recall = 0.87, F1-Score = 0.87), resulting in a 1.85% increase in overall classification accuracy; (2) the inclusion of crown morphological parameters derived from LiDAR data improves the overall accuracy of the random forest classifier by 5.82%; (3) compared to using LiDAR or hyperspectral data alone, the classification accuracy using multi-source data improves by an average of 7.94% and 7.52%, respectively; (4) the random forest classifier combined with multi-source data achieves the highest classification accuracy and consistency (OA = 90.70%, Kappa = 0.8747). Full article

The nozzle is a crucial component in unmanned aerial vehicle (UAV) sprayers. The centrifugal nozzle offers unique advantages; however, there is a scarcity of published research regarding the structural parameters, spraying parameters, and practical applications specifically for UAV spraying. Furthermore, there is a need for UAV-specific nozzles that demonstrate high efficiency and excellent atomization performance. In this present study, a multi-disc centrifugal nozzle (MCN) capable of controlling droplet size was designed and optimized. The droplet size spectra with different atomizing discs were tested, and indoor and field tests were conducted to investigate the atomization and spray deposition characteristics of the MCN. It was found that the MCN with six atomizing discs with a curved groove, a disc angle of 120°, and a disc diameter of 77 mm demonstrated better atomizing performance. The volume median diameter was 96–153 μm, and the relative span was 1.0–1.3. Compared with the conventional hydraulic nozzle, this nozzle increased the effective spray swath width from 2.5–3.0 m to 4.0–5.0 m and promoted the average deposition rate by 132.4% at a flying height of 1.0 m and a flying speed of 3.0 m/s, which tends to raise the operation efficiency by four to five times. This study can provide a reference for the design and optimization of centrifugal nozzles for a UAV sprayer and the selection of operating parameters in aerial spraying operations. Full article

Background: Given the severe economic burden that citrus greening disease imposes on fruit farmers and related industries, rapid and accurate disease detection is particularly crucial. This not only effectively curbs the spread of the disease, but also significantly reduces reliance on manual detection within extensive citrus planting areas. Objective: In response to this challenge, and to address the issues posed by resource-constrained platforms and complex backgrounds, this paper designs and proposes a novel method for the recognition and localization of citrus greening disease, named the HHS-RT-DETR model. The goal of this model is to achieve precise detection and localization of the disease while maintaining efficiency. Methods: Based on the RT-DETR-r18 model, the following improvements are made: the HS-FPN (high-level screening-feature pyramid network) is used to improve the feature fusion and feature selection part of the RT-DETR model, and the filtered feature information is merged with the high-level features by filtering out the low-level features, so as to enhance the feature selection ability and multi-level feature fusion ability of the model. In the feature fusion and feature selection sections, the HWD (hybrid wavelet-directional filter banks) downsampling operator is introduced to prevent the loss of effective information in the channel and reduce the computational complexity of the model. Through using the ShapeIoU loss function to enable the model to focus on the shape and scale of the bounding box itself, the prediction of the bounding box of the model will be more accurate. Conclusions and Results: This study has successfully developed an improved HHS-RT-DETR model which exhibits efficiency and accuracy on resource-constrained platforms and offers significant advantages for the automatic detection of citrus greening disease. Experimental results show that the improved model, when compared to the RT-DETR-r18 baseline model, has achieved significant improvements in several key performance metrics: the precision increased by 7.9%, the frame rate increased by 4 frames per second (f/s), the recall rose by 9.9%, and the average accuracy also increased by 7.5%, while the number of model parameters reduced by ${0.137\times 10}^7$. Moreover, the improved model has demonstrated outstanding robustness in detecting occluded leaves within complex backgrounds. This provides strong technical support for the early detection and timely control of citrus greening disease. Additionally, the improved model has showcased advanced detection capabilities on the PASCAL VOC dataset. Discussions: Future research plans include expanding the dataset to encompass a broader range of citrus species and different stages of citrus greening disease. In addition, the plans involve incorporating leaf images under various lighting conditions and different weather scenarios to enhance the model’s generalization capabilities, ensuring the accurate localization and identification of citrus greening disease in diverse complex environments. Lastly, the integration of the improved model into an unmanned aerial vehicle (UAV) system is envisioned to enable the real-time, regional-level precise localization of citrus greening disease. Full article

The architecture of the Manned/Unmanned Aerial Vehicle Collaborative Operation System (MAV/UAV COS) is crucial for improving combat effectiveness and resource utilization efficiency. Optimizing this architecture involves managing complex, interdependent components, which presents a constrained multi-objective optimization challenge. Initially, the elements of the MAV/UAV COS architecture were analyzed and formally expressed, transforming the architecture optimization problem into a multi-objective optimization problem, with the objectives of maximizing total system effectiveness, command-and-control performance, and system execution performance. Constraints were formulated based on mission and payload information. Subsequently, a Quantum Non-Dominated Sorting Genetic Algorithm based on Preference Guidance (PGQNSGA-II) was developed, incorporating an adaptive quantum gate mechanism based on preference information to enhance chromosome updating, ensuring that the probability amplitude of quantum bits aligns more closely with the optimal chromosome. The simulation results demonstrate that the proposed PGQNSGA-II algorithm significantly enhances the global search capability and efficiency compared to traditional quantum genetic algorithms, making it well-suited for optimizing MAV/UAV COS architectures. Full article

The emergence of large language models (LLMs), such as GPT (Generative Pre-trained Transformer), has had a profound impact and brought about significant changes across various sectors of human society. Integrating GPT-3.5 into a multi-robot control system, termed MultiBotGPT (Multi-Robot Control System with GPT), represents a notable application. This system utilizes layered architecture and modular design to translate natural language commands into executable tasks for UAVs (Unmanned Aerial Vehicles) and UGVs (Unmanned Ground Vehicles), enhancing capabilities in tasks such as target search and navigation. Comparative experiments with BERT (Bidirectional Encoder Representations from Transformers) in the natural language-processing component show that MultiBotGPT with GPT-3.5 achieves superior task success rates (94.4% and 55.0%) across 50 experiments, outperforming BERT significantly. In order to test the auxiliary role of the MultiBotGPT-controlled robot on a human operator, we invited 30 volunteers to participate in our comparative experiments. Three separate experiments were performed, Participant Control (Manual Control only), Mix Control (Mix Manual Contr and MultiBotGPT Control), and MultiBotGPT Control (MultiBotGPT Control only). The performance of MultiBotGPT is recognized by the human operators and it can reduce the mental and physical consumption of the human operators through the scoring of the participants’ questionnaires. Full article

This paper investigates the guaranteed performance resilient security consensus control of nonlinear networked control systems (NCSs) subject to asynchronous denial-of-service (DoS) cyber attacks, where the communication channel disruptions and recoveries occur randomly. The main works of this paper are outlined as follows: (1) a rigorous quantitative modeling of asynchronous DoS cyber attacks is formulated, leveraging connectivity analysis and the graph theory; (2) an innovative guaranteed performance function is introduced, which imposes constraints on the system’s convergence behavior while alleviating restrictions on initial tracking errors; (3) to address the challenge of estimating unmeasurable system states arising from the output-feedback scheme, a novel fuzzy state observer is devised; and (4) based on the aforementioned designs, a switching guaranteed performance resilient security consensus controller is proposed. This controller is tailored to the network connectivity characteristics of NCSs, ensuring resilient convergence of the system despite asynchronous DoS attacks. Notably, consensus tracking errors are maintained within predefined performance bounds. The experiment results of numerical simulation and hardware-in-the-loop simulation of multiple unmanned aerial vehicles (multi-UAVs) networks illustrate the effectiveness and practicality of proposed control scheme. Full article

To address the issues of local minima, target unreachability, and significant formation disruption during obstacle avoidance in the conventional artificial potential field (APF), a control approach that integrates APF with optimal consensus control which can achieve cooperative obstacle avoidance is proposed. Based on the double integrator multi-UAV formation model with a fixed undirected communication topology, the optimal consensus control protocol incorporating an obstacle avoidance cost function is introduced. This addresses the limitations of APF-based obstacle avoidance while simultaneously managing multi-UAV formation control. Training interactions in randomly generated unknown obstacle environments are conducted using Random Search for Hyperparameter Optimization (RSHO). Combined with the evaluation model, select the optimal solution of the consensus performance index, control consumption performance index, and obstacle avoidance performance index parameters of the multi-UAV formation control system. Furthermore, a virtual repulsive potential field is designed for each UAV to prevent inter-UAV collisions during obstacle avoidance. Simulation results show that the improved APF (IAPF) with optimal consensus control effectively overcomes the limitations of conventional APF. It achieves multi-UAV formation obstacle avoidance control in unknown environments and avoids the phenomenon of inter-UAV collisions during the obstacle avoidance process while maintaining formation integrity, accelerating formation reconfiguration and convergence, reducing consensus consumption and control loss due to obstacle avoidance, shortening mission time, and enhancing obstacle avoidance efficiency, highlighting the superiority of multi-UAV formation obstacle avoidance. Full article

Small air-launched unmanned aerial vehicles (UAVs) face challenges in range and endurance due to their compact size and lightweight design. To address these issues, this paper introduces a multi-phase wind energy harvesting trajectory planning method designed to optimize the onboard electrical energy consumption during rendezvous and formation flight of air-launched fixed-wing swarms. This method strategically manages gravitational potential energy from air-launch deployments and harvests wind energy that aligns with the UAV’s flight speed. We integrate wind energy harvesting strategies for single vehicles with the spatial–temporal coordination of the swarm system. Considering the wind effects into the trajectory planning allows UAVs to enhance their operational capabilities and extend mission duration without changes on the vehicle design. The trajectory planning method is formalized as an optimal control problem (OCP) that ensures spatial–temporal coordination, inter-vehicle collision avoidance, and incorporates a 3-degree of freedom kinematic model of UAVs, extending wind energy harvesting trajectory optimization from an individual UAV to swarm-level applications. The cost function is formulized to comprehensively evaluate electrical energy consumption, endurance, and range. Simulation results demonstrate significant energy savings in both low- and high-altitude mission scenarios. Efficient wind energy utilization can double the maximum formation rendezvous distance and even allow for rendezvous without electrical power consumption when the phase durations are extended reasonably. The subsequent formation flight phase exhibits a maximum endurance increase of 58%. This reduction in electrical energy consumption directly extends the range and endurance of air-launched swarm, thereby enhancing the mission capabilities of the swarm in subsequent flight. Full article

With the continuous development of modern UAV technology and communication technology, UAV-to-ground communication relay has become a research hotspot. In this paper, a Multi-Agent Reinforcement Learning (MARL) method based on the ε-greedy strategy and multi-agent proximal policy optimization (MAPPO) algorithm is proposed to address the local optimization problem, improving the communication efficiency and task execution capability of UAV cluster control. This paper explores the path planning problem in multi-UAV-to-ground relay communication, with a special focus on the application of the proposed Mix-Greedy MAPPO algorithm. The state space, action space, communication model, training environment, and reward function are designed by comprehensively considering the actual tasks and entity characteristics such as safe distance, no-fly zones, survival in a threatened environment, and energy consumption. The results show that the Mix-Greedy MAPPO algorithm significantly improves communication probability, reduces energy consumption, avoids no-fly zones, and facilitates exploration compared to other algorithms in the multi-UAV ground communication relay path planning task. After training with the same number of steps, the Mix-Greedy MAPPO algorithm has an average reward score that is 45.9% higher than the MAPPO algorithm and several times higher than the multi-agent soft actor-critic (MASAC) and multi-agent deep deterministic policy gradient (MADDPG) algorithms. The experimental results verify the superiority and adaptability of the algorithm in complex environments. Full article

Coastal areas support seagrass meadows, which offer crucial ecosystem services, including erosion control and carbon sequestration. However, these areas are increasingly impacted by human activities, leading to habitat fragmentation and seagrass decline. In situ surveys, traditionally performed to monitor these ecosystems, face limitations on temporal and spatial coverage, particularly in intertidal zones, prompting the addition of satellite data within monitoring programs. Yet, satellite remote sensing can be limited by too coarse spatial and/or spectral resolutions, making it difficult to discriminate seagrass from other macrophytes in highly heterogeneous meadows. Drone (unmanned aerial vehicle—UAV) images at a very high spatial resolution offer a promising solution to address challenges related to spatial heterogeneity and the intrapixel mixture. This study focuses on using drone acquisitions with a ten spectral band sensor similar to that onboard Sentinel-2 for mapping intertidal macrophytes at low tide (i.e., during a period of emersion) and effectively discriminating between seagrass and green macroalgae. Nine drone flights were conducted at two different altitudes (12 m and 120 m) across heterogeneous intertidal European habitats in France and Portugal, providing multispectral reflectance observation at very high spatial resolution (8 mm and 80 mm, respectively). Taking advantage of their extremely high spatial resolution, the low altitude flights were used to train a Neural Network classifier to discriminate five taxonomic classes of intertidal vegetation: Magnoliopsida (Seagrass), Chlorophyceae (Green macroalgae), Phaeophyceae (Brown algae), Rhodophyceae (Red macroalgae), and benthic Bacillariophyceae (Benthic diatoms), and validated using concomitant field measurements. Classification of drone imagery resulted in an overall accuracy of 94% across all sites and images, covering a total area of 467,000 m². The model exhibited an accuracy of 96.4% in identifying seagrass. In particular, seagrass and green algae can be discriminated. The very high spatial resolution of the drone data made it possible to assess the influence of spatial resolution on the classification outputs, showing a limited loss in seagrass detection up to about 10 m. Altogether, our findings suggest that the MultiSpectral Instrument (MSI) onboard Sentinel-2 offers a relevant trade-off between its spatial and spectral resolution, thus offering promising perspectives for satellite remote sensing of intertidal biodiversity over larger scales. Full article

The saliency feature is a key factor in achieving vision-based tracking for multi-UAV control. However, due to the complex and variable environments encountered during multi-UAV operations—such as changes in lighting conditions and scale variations—the UAV’s visual features may degrade, especially under high-speed movement, ultimately resulting in failure of the vision tracking task and reducing the stability and robustness of swarm flight. Therefore, this paper proposes an adaptive active light source system based on light intensity matching to address the issue of visual feature loss caused by environmental light intensity and scale variations in multi-UAV collaborative navigation. The system consists of three components: an environment sensing and control module, a variable active light source module, and a light source power module. This paper first designs the overall framework of the active light source system, detailing the functions of each module and their collaborative working principles. Furthermore, optimization experiments are conducted on the variable active light source module. By comparing the recognition effects of the variable active light source module under different parameters, the best configuration is selected. In addition, to improve the robustness of the active light source system under different lighting conditions, this paper also constructs a light source color matching model based on light intensity matching. By collecting and comparing visible light images of different color light sources under various intensities and constructing the light intensity matching model using the comprehensive peak signal-to-noise ratio parameter, the model is optimized to ensure the best vision tracking performance under different lighting conditions. Finally, to validate the effectiveness of the proposed active light source system, quantitative and qualitative recognition comparison experiments were conducted in eight different scenarios with UAVs equipped with active light sources. The experimental results show that the UAV equipped with an active light source has improved the recall of yoloV7 and RT-DETR recognition algorithms by 30% and 29.6%, the mAP50 by 21% and 19.5%, and the recognition accuracy by 13.1% and 13.6, respectively. Qualitative experiments also demonstrated that the active light source effectively improved the recognition success rate under low lighting conditions. Extensive qualitative and quantitative experiments confirm that the UAV active light source system based on light intensity matching proposed in this paper effectively enhances the effectiveness and robustness of vision-based tracking for multi-UAVs, particularly in complex and variable environments. This research provides an efficient and computationally effective solution for vision-based multi-UAV systems, further enhancing the visual tracking capabilities of multi-UAVs under complex conditions. Full article

This paper presents LEVIOSA, a novel framework for text- and speech-based uncrewed aerial vehicle (UAV) trajectory generation. By leveraging multimodal large language models (LLMs) to interpret natural language commands, the system converts text and audio inputs into executable flight paths for UAV swarms. The approach aims to simplify the complex task of multi-UAV trajectory generation, which has significant applications in fields such as search and rescue, agriculture, infrastructure inspection, and entertainment. The framework involves two key innovations: a multi-critic consensus mechanism to evaluate trajectory quality and a hierarchical prompt structuring for improved task execution. The innovations ensure fidelity to user goals. The framework integrates several multimodal LLMs for high-level planning, converting natural language inputs into 3D waypoints that guide UAV movements and per-UAV low-level controllers to control each UAV in executing its assigned 3D waypoint path based on the high-level plan. The methodology was tested on various trajectory types with promising accuracy, synchronization, and collision avoidance results. The findings pave the way for more intuitive human–robot interactions and advanced multi-UAV coordination. Full article

The Grey Wolf Optimizer (GWO) algorithm is recognized for its simplicity and ease of implementation, and has become a preferred method for solving global optimization problems due to its adaptability and search capabilities. Despite these advantages, existing Unmanned Aerial Vehicle (UAV) path planning algorithms are often hindered by slow convergence rates, susceptibility to local optima, and limited robustness. To surpass these limitations, we enhance the application of GWO in UAV path planning by improving its trajectory evaluation function, convergence factor, and position update method. We propose a collaborative UAV path planning model that includes constraint analysis and an evaluation function. Subsequently, an Enhanced Grey Wolf Optimizer model (NI–GWO) is introduced, which optimizes the convergence coefficient using a nonlinear function and integrates the Dynamic Window Approach (DWA) algorithm into the model based on the fitness of individual wolves, enabling it to perform dynamic obstacle avoidance tasks. In the final stage, a UAV path planning simulation platform is employed to evaluate and compare the effectiveness of the original and improved algorithms. Simulation results demonstrate that the proposed NI–GWO algorithm can effectively solve the path planning problem for UAVs in uncertain environments. Compared to Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), GWO, and MP–GWO algorithms, the NI–GWO algorithm can achieve the optimal fitness value and has significant advantages in terms of average path length, time, number of collisions, and obstacle avoidance capabilities. Full article