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This paper addresses the challenge of ineffective coordination between terminal maneuvering and precision strike capabilities in hypersonic unmanned aerial vehicles (UAVs). To resolve this issue, an intelligent spiral maneuver trajectory generation method utilizing a virtual trajectory framework is proposed. Initially, a relative motion model between the UAV and the virtual center of mass (VCM) is established based on the geometric principles of the Archimedean spiral. Subsequently, the interaction dynamics between the VCM and the target are formulated as a Markov decision process (MDP). A deep reinforcement learning (DRL) approach, employing the proximal policy optimization (PPO) algorithm, is implemented to train a policy network capable of end-to-end virtual trajectory generation. Ultimately, the relative spiral motion is superimposed onto the generated virtual trajectory to synthesize a composite spiral maneuvering trajectory. The simulation results demonstrate that the proposed method achieves expansive spiral maneuvering ranges while ensuring precise target strikes.

In response to the issue of UAV escape guidance, this study proposed a unified intelligent control strategy synthesizing optimal guidance and meta deep reinforcement learning (DRL). Optimal control with minor energy consumption was introduced to meet terminal latitude, longitude, and altitude. Maneuvering escape was realized by adding longitudinal and lateral maneuver overloads. The Maneuver command decision model is calculated based on soft-actor–critic (SAC) networks. Meta-learning was introduced to enhance the autonomous escape capability, which improves the performance of applications in time-varying scenarios not encountered in the training process. In order to obtain training samples at a faster speed, this study used the prediction method to solve reward values, avoiding a large number of numerical integrations. The simulation results demonstrated that the proposed intelligent strategy can achieve highly precise guidance and effective escape.

The penetration of unmanned aerial vehicles (UAVs) is an important aspect of UAV games. In recent years, UAV penetration has generally been solved using artificial intelligence methods such as reinforcement learning. However, the high sample demand of the reinforcement learning method poses a significant challenge specifically in the context of UAV games. To improve the sample utilization in UAV penetration, this paper innovatively proposes an improved sampling mechanism called task completion division (TCD) and combines this method with the soft actor critic (SAC) algorithm to form the TCD-SAC algorithm. To compare the performance of the TCD-SAC algorithm with other related baseline algorithms, this study builds a dynamic environment, a UAV game, and conducts training and testing experiments in this environment. The results show that among all the algorithms, the TCD-SAC algorithm has the highest sample utilization rate and the best actual penetration results, and the algorithm has a good adaptability and robustness in dynamic environments.

The penetration of unmanned aerial vehicles (UAVs) is an essential and important link in modern warfare. Enhancing UAV’s ability of autonomous penetration through machine learning has become a research hotspot. However, the current generation of autonomous penetration strategies for UAVs faces the problem of excessive sample demand. To reduce the sample demand, this paper proposes a combination policy learning (CPL) algorithm that combines distributed reinforcement learning and demonstrations. Innovatively, the action of the CPL algorithm is jointly determined by the initial policy obtained from demonstrations and the target policy in the asynchronous advantage actor-critic network, thus retaining the guiding role of demonstrations in the initial training. In a complex and unknown dynamic environment, 1000 training experiments and 500 test experiments were conducted for the CPL algorithm and related baseline algorithms. The results show that the CPL algorithm has the smallest sample demand, the highest convergence efficiency, and the highest success rate of penetration among all the algorithms, and has strong robustness in dynamic environments.

Knowledge of the soil water content (SWC) is important for many aspects of agriculture and must be monitored to maximize crop yield, efficiently use limited supplies of irrigation water, and ensure optimal nutrient management with minimal environmental impact. Single-location sensors are often used to monitor SWC, but a limited number of point measurements is insufficient to measure SWC across most fields since SWC is typically very heterogeneous. To overcome this difficulty, several researchers have used data acquired from unmanned aerial vehicles (UAVs) to predict the SWC by using machine learning on a limited number of point measurements acquired across a field. While useful, these methods are limited by the relatively small number of SWC measurements that can be acquired with conventional measurement techniques. This study uses UAV-based data and thousands of SWC measurements acquired using geophysical methods at two different depths and before and after precipitation to predict the SWC using the random forest method across a vineyard in the central United States. Both multispectral data (five reflectance bands and eleven vegetation indices calculated from these bands) and thermal UAV-based data were acquired, and the importance of different reflectance data and vegetation indices in the prediction of SWC was analyzed. Results showed that when both thermal and multispectral data were used to estimate SWC, the thermal data contributed the most to prediction accuracy, although multispectral data were also important. Reflectance data contributed as much or more to prediction accuracy than most vegetation indices. SWC measurements that had a larger sample size and greater penetration depth (~30 cm sampling depth) were more accurately predicted than smaller and shallower SWC estimates (~18 cm sampling depth). The timing of SWC estimation was also important; higher accuracy predictions were achieved in wetter soils than in drier soils, and a light precipitation event also improved prediction accuracy.

The multi-UAV system has stronger robustness and better stability in combat. Therefore, the collaborative penetration of UAVs has been extensively studied in recent years. Compared with general static combat scenes, the dynamic tracking and interception of equipment penetration are more difficult to achieve. To realize the coordinated penetration of the dynamic-tracking interceptor by the multi-UAV system, the intelligent UAV model is established by using the deep deterministic policy-gradient algorithm, and the reward function is constructed using the cooperative parameters of multiple UAVs to guide the UAV to proceed with collaborative penetration. The simulation experiment proved that the UAV finally evaded the dynamic-tracking interceptor, and multiple UAVs reached the target at the same time, realizing the time coordination of the multi-UAV system.

Smart construction technology integrates artificial intelligence, Internet of Things, UAVs, and building information modeling to improve productivity and quality in construction. In road embankment earthworks, ground compaction quality is critical for structural stability and maintenance. This study proposes a methodology combining UAV photogrammetry with intelligent compaction quality management systems to evaluate surface flatness and compaction homogeneity in real-time. High-resolution UAV images were used to generate digital elevation models, from which surface roughness was extracted using terrain element analysis and fast Fourier transform. Local terrain changes were interpreted through contour gradient, outline gradient, and tangential gradient curvature analysis. Field tests were conducted at a pilot site using a vibratory roller, followed by four compaction quality assessments: plate load test, dynamic cone penetration test, light falling weight deflectometer, and compaction meter value. UAV-based flatness analysis revealed that, when surface flatness met the standard, a strong correlation was observed, with results from conventional field tests and intelligent compaction data. The proposed method effectively identified poorly compacted zones and spatial inhomogeneity without interrupting construction. These findings demonstrate that UAV-based terrain analysis can serve as a nondestructive real-time monitoring tool and contribute to automated quality control in smart construction environments.

To address the increasing vulnerability of unmanned aerial vehicle (UAV) lightweight airframe structures to high-velocity fragment impacts in complex operational environments, this study combines high-velocity impact penetration tests, quasi-static strength tests, fracture-surface microanalysis, and finite-element simulation to systematically reveal the formation mechanism of typical penetration damage and its influence on residual strength. Results show that such penetration induces damage such as adiabatic-shear local melting zones, spall cracks, and grain-boundary separation, significantly weakening static strength and shifting the fracture mode from ductility- to brittleness-dominated. A modified fracture-mechanics criterion with higher prediction accuracy than the traditional net-section criterion is proposed, and a high-precision simulation model based on explicit–quasi-static coupling is established, which well reproduces damage morphology and tensile-failure processes. Compared with conventional manned aircraft structures, UAV airframes characterized by thinner skins and higher lightweighting ratios exhibit more pronounced sensitivity to penetration-induced micro-defects, making rapid residual-strength assessment essential for operational recovery and field-level repair decision-making. The research reveals the damage mechanism and provides an engineering-applicable residual-strength assessment method, offering a reliable theoretical basis and simulation tool for rapid UAV damage evaluation and fast-turnaround repair planning for civil and industrial UAV platforms.

Yearly, the rates of Internet penetration are on the rise, surpassing 80% in developed nations. Despite this progress, over two billion individuals in rural and low-income regions face a complete absence of Internet access. This lack of connectivity hinders the implementation of vital services like remote healthcare, emergency assistance, distance learning, and personal communications. To bridge this gap and bring essential services to rural populations, this paper leverages Unmanned Aerial Vehicles (UAVs). The proposal introduces a UAV-based network architecture and an energy-efficient algorithm to deploy Internet of Things (IoT) applications. These applications are broken down into microservices, strategically distributed among a subset of UAVs. This approach addresses the limitations associated with running an entire IoT application on a single UAV, which could lead to suboptimal outcomes due to battery and computational constraints. Simulation results conducted in a realistic scenario underscore the effectiveness of the proposed solution. The evaluation includes assessing the percentage of IoT requests successfully served to users in the designated area and reducing the energy consumption required by UAVs during the handling of such requests.

Fixed-wing unmanned aerial vehicles (UAVs) will play a vital role in forthcoming military conflicts. Effectively avoiding threats and improving the survivability of fixed-wing UAV in dynamic hostile environments are the keys to the success of combat missions. Hence, endowing fixed-wing UAVs with the ability to autonomously generate evasive maneuver is the primary problem that should be solved. With considering the threat of air-to-air missile attacks, this paper designs a novel hierarchical goal-guided learning (HGGL) method, which combines with traditional off-policy deep reinforcement learning (DRL) algorithms and endows the agent with the ability to evade a series of air-to-air missiles. The pivotal idea of the proposed algorithm is to use the hierarchical features of the goal, it improves the availability of training data to eliminate the limitation of the convergence rate of traditional DRL algorithms owing to sparse rewards. We demonstrate the performance of our algorithm in several simulation experiments. All experiments are applied on the XSimStudio platform. The results demonstrate that the proposed algorithm improves the convergence speed and outperforms the state-of-the-art traditional algorithms.