摘要 ：

With the rapid advancements in unmanned aerial vehicle (UAV) technology, new solutions have emerged to address logistics challenges in certain areas. Drone deliveries can reach locations that are inaccessible to traditional logistics methods. However, existing cargo drones face difficulties with takeoff and landing on complex terrains due to their fixed landing gear. This paper introduces the design of a bionic specialized quadrupedal UAV, capable of moving and climbing on complex terrains, aimed at improving UAV cargo operations in challenging environments.

关键词 ：

bionic UAV Motion Simulation UAV climbing trot gait Flight Path Control UAV design

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摘要 ：

To satisfy the differentiated service requirements of delay-sensitive and computing-intensive tasks in unmanned aerial vehicle (UAV) networks, it is urgent to efficiently allocate limited network resources to improve network performance. In this paper, we propose an intelligent task offloading scheme to optimize resource allocation in UAV networks with content caching. Specifically, we formulate the joint optimization of task offloading and resource allocation as a latency minimization model for the caching-assisted UAV system. Then, a new deep reinforcement learning (DRL) algorithm is designed to make offloading and resource allocation decisions based on current network state information, significantly improving resource utilization. Numerical results indicate that the model significantly reduces network latency in comparison to its existing benchmarks in caching-assisted UAV networks.

关键词 ：

content caching task offloading resource allocation unmanned aerial vehicle networks deep reinforcement learning

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摘要 ：

As unmanned aerial vehicles (UAVs) continue to play an increasingly critical role in reconnaissance missions, establishing dependable communication links between UAVs and ground stations has become imperative. Nevertheless, ensuring reliable communication remains a great challenge, particularly in environments characterized by weak signals or high levels of electromagnetic interference. To tackle this challenge, this study presents a design and optimization approach for a miniature UAV antenna. This antenna achieves significant performance improvements by optimizing the magnetic field (MF) distribution and convergence within its central section. Specifically with the aim of capturing and amplifying signals in a specified direction, the antenna enhances reception sensitivity, especially in challenging operational settings. The structure ensures robust and consistent signal reception with a maximum gain of up to 12.8 dB and a converging MF magnitude of 2279 A/m at its center. Furthermore, it operates effectively within the C band, exhibiting a relative bandwidth of 12.2%. This capability empowers UAV to transmit reconnaissance data accurately and swiftly, regardless of the distance traveled or the complexity of the electromagnetic environment. This advancement not only enhances UAV capabilities but also opens new possibility for applications requiring dependable communication in diverse and demanding scenarios. © 2019 IEEE.

关键词 ：

Antenna grounds Galvanomagnetic effects Aircraft communication Magnetic levitation vehicles Miniature automobiles Unmanned aerial vehicles (UAV)

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The computation-intensive situational awareness (SA) task of unmanned aerial vehicle (UAV) is greatly affected by its limited power and computing capability. To solve this challenge, we consider the joint communication and computation (JCC) design for UAV network in this paper. Firstly, a multi-objective optimization (MOO) model, which can optimize UAV computation offloading, transmit power, and local computation resources simultaneously, is built to minimize energy consumption and task execution delay. Then, we develop Thompson sampling based double-DQN (TDDQN) learning algorithm which allows the agent to explore more deeply and effectively, and propose a joint optimization algorithm that combines TDDQN and sequential least squares quadratic programming (SLSQP) to handle the MOO problem. Finally, to enhance the training speed and quality, we incorporate federated learning (FL) into the presented joint optimization algorithm and propose hierarchical federated TDDQN with SLSQP (HF TDDQN-S) to implement the JCC design. Simulation results show that the introduced HF TDDQN-S can efficiently learn the best JCC strategy and minimize the average cost contrasted with the DDQN with SLSQP (DDQN-S) and TDDQN with SLSPQ (TDDQN-S) approach, and achieve the low average delay SA with power efficient.

关键词 ：

Hierarchical federated learning Thompson sampling UAV situational awareness Sequential least squares quadratic programming Communication and computation

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摘要 ：

Using Unmanned Aerial Vehicles as mobile base stations is a promising way to collect data from sensor nodes, especially for large-scale wireless sensor networks. Previous works mainly focus on improving the freshness of the collected data or the energy efficiency by scheduling UAVs. Considering the fact that the sensing data in some applications is time-sensitive, that is, the value of the sensing data is based on its Timeliness of Information (ToI), which decays over time. Therefore, in this paper, we investigate the UAV Trajectory optimization problem for Maximizing the ToI-based data utility (TMT). We propose an improved deep reinforcement learning-based algorithm to address the problem, and the experience results demonstrate the effectiveness of our designs. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词 ：

Unmanned aerial vehicles (UAV) Sensor nodes Deep reinforcement learning

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Foggy weather is often encountered on the battlefield, and environmental visibility will be reduced when conducting operations on foggy days, which often affects UAV reconnaissance of armored vehicles on the ground. To improve the detection accuracy of armored vehicles on foggy days, this paper first applies the AOD-Net network to defog the foggy images, then improves the YOLOv5 network model by replacing the original head structure with the DynamicHead structure and uses the RepGFPN pyramid network in the network to detect the target in the defogged images. The experimental results show that the accuracy and recall of the improved network model are increased by 1% and 3% respectively, and the mAP is increased by 2.7%, which effectively improves the accuracy of recognizing armored vehicles in foggy images. By the comparisons between the original images and the defogged ones by the improved model, the experimental results show that there are a significant increases in the detection accuracy of the tank targets after the images are defogged. © 2024 IEEE.

关键词 ：

Military photography Unmanned aerial vehicles (UAV) Tanks (military) Armored vehicles

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The Connected Sensor Problem (CSP) presents a prevalent challenge in the realms of communication and Internet of Things (IoT) applications. Its primary aim is to maximize the coverage of users while maintaining connectivity among K sensors. Addressing the challenge of managing a large user base alongside a finite number of candidate locations, this paper proposes an extension to the CSP: the h-hop independently submodular maximization problem characterized by curvature α. We have developed an approximation algorithm that achieves a ratio of [Formula presented]. The efficacy of this algorithm is demonstrated on the CSP, where it shows superior performance over existing algorithms, marked by an average enhancement of 8.4%. © 2024 The Author(s)

关键词 ：

Internet of things Approximation algorithms Unmanned aerial vehicles (UAV)

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摘要 ：

Mobile edge computing (MEC) paradigm supports cloud-like computing capabilities at the edge of the network and offers low-latency services. Proxy servers of MEC with mobility and limited computing, e.g., flying unmanned aerial vehicles (UAVs) have emerged as competitors in providing services. This work considers a task offloading problem for an UAV-assisted MEC system and designs an integrated cloud-edge network with multiple mobile users (MUs) and layered UAVs to improve MEC with a network of UAVs. In our system, edge UAVs (EUAVs) and the cloud collaborate to provide caching and computing services for MUs. We consider static and dynamic applications that support task offloading. Our proposed approach minimizes the weighted cost of latency and energy consumption by jointly optimizing caching and offloading, deployment of EUAVs, and allocation of computation resources. Simultaneously, this work also considers UAVs' caching and computation capacities while meeting MUs' latency and energy constraints. Thus, a constrained mixed integer nonlinear program for a layered UAV-assisted hybrid cloud-edge system is formulated. To solve it, this work designs a hybrid metaheuristic algorithm named adaptive and genetic simulated annealing (SA)-based particle swarm optimization (AGSP). Experimental results with a real-life dataset verify that the AGSP's system energy consumption and task latency are reduced by at least 7.4% and 8.46%, respectively, compared with the state-of-the-art algorithms, thus proving that AGSP greatly enhances the energy and latency of the system.

关键词 ：

Autonomous aerial vehicles Servers mobile edge computing (MEC) Cloud computing Computation offloading Task analysis wireless caching Computer architecture Relays Trajectory unmanned aerial vehicles (UAVs) particle swarm optimization (PSO)

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摘要 ：

Road segmentation is a fundamental task for dynamic map in unmanned aerial vehicle (UAV) path navigation. In unplanned, unknown and even damaged areas, there are usually unpaved roads with blurred edges, deformations and occlusions. These challenges of unpaved road segmentation pose significant challenges to the construction of dynamic maps. Our major contributions have: (1) Inspired by dilated convolution, we propose dilated cross window self-attention (DCWin-Attention), which is composed of a dilated cross window mechanism and a pixel regional module. Our goal is to model the long-range horizontal and vertical road dependencies for unpaved roads with deformation and blurred edges. (2) A shifted cross window mechanism is introduced through coupling with DCWin-Attention to reduce the influence of occluded roads in UAV imagery. In detail, the GVT backbone is constructed by using the DCWin-Attention block for multilevel deep features with global dependency. (3) The unpaved road is segmented with the confidence map generated by fusing the deep features of different levels in a unified perceptual parsing network. We verify our method on the self-established BJUT-URD dataset and public DeepGlobe dataset, which achieves 67.72 and 52.67% of the highest IoU at proper inference efficiencies of 2.7, 2.8 FPS, respectively, demonstrating its effectiveness and superiority in unpaved road segmentation. Our code is available at https://github.com/BJUT-AIVBD/GVT-URS.

关键词 ：

Unpaved road segmentation Dynamic map UAV imagery Global vision transformer DCWin-attention

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This paper introduces a collaborative allocation model designed for multiple UAVs and diverse targets in maritime combat situations. The model incorporates factors such as distance, angle, interception rate, and recognition rate to comprehensively represent the UAVs' overall damage advantage against targets. Given the complexity of real -world environments and real-time demands, large-scale UAV swarm missions necessitate swift and effective responses. To address this, the paper proposes a Two -Stage Greedy Auction Algorithm, enabling the rapid and efficient completion of cooperative strike tasks within large-scale UAV swarms while preventing deadlock occurrences. In the initial allocation stage, the entropy weight method is utilized to assess task advantages, ensuring a rational allocation criterion for various metrics during the strike process. Subsequently, to enhance the overall effective strike rate within all constraints, a reassignment algorithm is designed based on effective strike benefit indices and the initial assignment result. Simulation results demonstrate the algorithm's quick and stable running time in small-scale and large-scale scenarios.

关键词 ：

Two-stage greedy auction algorithm UAV swarm Reassignment Cooperative target allocation

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