Search Results (675)

The utilization of Unmanned Aerial Vehicles (UAVs) in missions such as reconnaissance and surveillance has grown rapidly, underscoring the need for efficient path planning algorithms that ensure both optimality and collision avoidance. The A-star algorithm is widely used for global path planning due to its ability to generate optimal routes; however, its high computational cost makes it unsuitable for real-time applications, particularly in unknown or dynamic environments. For local path planning, the Artificial Potential Field (APF) algorithm enables real-time navigation by attracting the UAV toward the target while repelling it from obstacles. Despite its efficiency, APF suffers from local minima and limited performance in dynamic settings. To address these challenges, this paper proposes the A-star-Guided Potential Field (AGPF) algorithm, which integrates the strengths of A-star and APF to achieve robust performance in both global and local path planning. The AGPF algorithm was validated through simulations conducted in the Robot Operating System (ROS) environment. Simulation results demonstrate that AGPF produces smoother and more optimal paths than A-star, while avoiding the local minima issues inherent in APF. Furthermore, AGPF effectively handles moving and previously unknown obstacles by generating real-time avoidance trajectories, demonstrating strong adaptability in dynamic and uncertain environments. Full article

To address the unclear coupling mechanism between thermal elastohydrodynamic lubrication (TEHL) and dynamic behaviors in planetary gear systems, a novel tribo-dynamic model for dual-star planetary gears considering TEHL effects is proposed. In this model, a TEHL surrogate model is first established to determine the oil film thickness and sliding friction force along the tooth meshing line. Subsequently, the dynamic model of the dual-star planetary gear transmission system is developed through coordinate transformations of the dual-star gear train. Finally, by integrating lubrication effects into both time-varying mesh stiffness and time-varying backlash, a tribo-dynamic model for the dual-star planetary gear transmission system is established. The study reveals that the lubricant film thickness is positively correlated with relative sliding velocity but negatively correlated with unit line load. Under high-speed conditions, a thickened oil film induces premature meshing contact, leading to meshing impacts. In contrast, under high-torque conditions, tooth deformation dominates meshing force fluctuations while lubrication influence diminishes. By establishing a test bench for the planetary gear transmission system, the obtained simulation conclusions are verified. This research provides theoretical and experimental support for the design of high-reliability planetary gear systems. Full article

Autonomous Unmanned Surface Vehicle (USV) operations in complex ocean engineering scenarios necessitate robust navigation, guidance, and control technologies. These systems require reliable sensor-based object detection and efficient, safe, and energy-aware path planning. To address these multifaceted challenges, this paper proposes a novel synergistic AI framework. The framework integrates (1) a novel adaptation of the Swin-Transformer to generate a dense, semantic risk map from raw visual data, enabling the system to interpret ambiguous marine conditions like sun glare and choppy water, enabling real-time environmental understanding crucial for guidance; (2) a Transformer-enhanced A-star (T-ASTAR) algorithm with spatio-temporal attentional guidance to generate globally near-optimal and energy-aware static paths; (3) a domain-adapted TD3 agent featuring a novel energy-aware reward function that optimizes for USV hydrodynamic constraints, making it suitable for long-endurance missions tailored for USVs to perform dynamic local path optimization and real-time obstacle avoidance, forming a key control element; and (4) CUDA acceleration to meet the computational demands of real-time ocean engineering applications. Simulations and real-world data verify the framework’s superiority over benchmarks like A* and RRT, achieving 30% shorter routes, 70% fewer turns, 64.7% fewer dynamic collisions, and a 215-fold speed improvement in map generation via CUDA acceleration. This research underscores the importance of integrating powerful AI components within a hierarchical synergy, encompassing AI-based perception, hierarchical decision planning for guidance, and multi-stage optimal search algorithms for control. The proposed solution significantly advances USV autonomy, addressing critical ocean engineering challenges such as navigation in dynamic environments, object avoidance, and energy-constrained operations for unmanned maritime systems. Full article

This paper investigated the issues of unstable data collection links and low efficiency in IoT data collection for smart cities by combining active STAR-RIS with UAVs to enhance channel quality, achieving efficient data collection in complex environments. To this end, we propose an active simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-assisted UAV-enabled NOMA data collection system that jointly optimizes active STAR-RIS beamforming, SN power allocation, and UAV trajectory to maximize the system energy efficiency (EE) and the data complete collection rate (DCCR). We apply block coordinate ascent (BCA) to decompose the non-convex problem into three alternating subproblems: combined beamforming optimization of phase shift and amplification gain matrices, power allocation, and trajectory optimization, which are iteratively processed through successive convex approximation (SCA) and fractional programming (FP) methods, respectively. Simulation results demonstrate the proposed algorithm’s rapid convergence and significant advantages over conventional NOMA and OMA schemes in both throughput rate and DCCR. Full article

To achieve efficient and sustainable marine aquaculture, STAR-CCM+ was used to simulate the internal and external field characteristics of a semi-submersible aquaculture platform based on a porous media model, focusing on the influence of incoming flow velocity and net solidity ratio. The results indicate that the flow field distribution around the platform exhibits no significant regularity and that low-velocity vortex regions are primarily concentrated near the pillars and nets. After velocity attenuation, the velocity reduction coefficients at the centers of the three cages are 90.26%, 63.65%, and 52.56%, respectively. Furthermore, the velocity attenuation inside the cages is minimally influenced by incoming flow velocity, with a maximum difference of 3.10%. In contrast, differences in net solidity ratio significantly affect velocity attenuation, particularly in downstream regions. The velocity reduction coefficient in the third cage varies by up to 43.25% depending on the net solidity ratio. These findings provide practical insights for the engineering design and application of aquaculture platforms. Full article

Currently, the dismantling of aluminum stranded conductors remains predominantly manual due to their structural complexity. To enhance the efficiency and reduce the labor intensity for dismantling aluminum stranded conductors, this study presents an innovative torque-driven dismantling method validated through dynamic simulation analysis. To demonstrate the proposed method, a modular prototype machine that includes four main functional modules (transmission, untwisting, separation, and shearing) was developed. Experimental results from the prototype dismantling machine demonstrated that when processing JL/G3A-500/65 conductors (Sichuan Star Cable Co., Ltd., Leshan, China) under the following operational parameters—0.5 rad/s rotational speed, 10 cm extension length, 2400 N clamping force, and 40 N·m torque application—the system achieved a single-layer dismantling efficiency exceeding 98%. This represents a significant improvement in operational speed compared to traditional manual methods. The developed machine achieved collaborative control of axial feed, reverse untwisting, and automatic shearing, elevating the untwisting qualification rate to 95%. This solution provides an efficient and safe approach to conductor inspection, demonstrating substantial engineering application value. Full article

Star sensors, as the most precise attitude measurement devices currently available, play a crucial role in spacecraft attitude estimation. However, traditional frame-based cameras tend to suffer from target blur and loss under high-dynamic maneuvers, which severely limit the applicability of conventional star sensors in complex space environments. In contrast, event cameras—drawing inspiration from biological vision—can capture brightness changes at ultrahigh speeds and output a series of asynchronous events, thereby demonstrating enormous potential for space detection applications. Based on this, this paper proposes an event data extraction method for weak, high-dynamic space targets to enhance the performance of event cameras in detecting space targets under high-dynamic maneuvers. In the target denoising phase, we fully consider the characteristics of space targets’ motion trajectories and optimize a classical spatiotemporal correlation filter, thereby significantly improving the signal-to-noise ratio for weak targets. During the target extraction stage, we introduce the DBSCAN clustering algorithm to achieve the subpixel-level extraction of target centroids. Moreover, to address issues of target trajectory distortion and data discontinuity in certain ultrahigh-dynamic scenarios, we construct a camera motion model based on real-time motion data from an inertial measurement unit (IMU) and utilize it to effectively compensate for and correct the target’s trajectory. Finally, a ground-based simulation system is established to validate the applicability and superior performance of the proposed method in real-world scenarios. Full article

Multi-agent reinforcement learning has demonstrated excellent performance in complex decision-making tasks such as electronic games, power grid management, and autonomous driving. However, its vulnerability to adversarial attacks may impede its widespread application. Currently, research on adversarial attacks in reinforcement learning primarily focuses on single-agent scenarios, while studies in multi-agent settings are relatively limited, especially regarding how to achieve optimized attacks with fewer steps. This paper aims to bridge the gap by proposing a heuristic exploration-based attack method named the Search for Key steps and Key agents Attack (SKKA). Unlike previous studies that train a reinforcement learning model to explore attack strategies, our approach relies on a constructed predictive model and a T-value function to search for the optimal attack strategy. The predictive model predicts the environment and agent states after executing the current attack for a certain period, based on simulated environment feedback. The T-value function is then used to evaluate the effectiveness of the current attack. We select the strategy with the highest attack effectiveness from all possible attacks and execute it in the real environment. Experimental results demonstrate that our attack method ensures maximum attack effectiveness while greatly reducing the number of attack steps, thereby improving attack efficiency. In the StarCraft Multi-Agent Challenge (SMAC) scenario, by attacking 5–15% of the time steps, we can reduce the win rate from 99% to nearly 0%. By attacking approximately 20% of the agents and 24% of the time steps, we can reduce the win rate to around 3%. Full article

Stellar refraction autonomous navigation provides a promising alternative for cross-domain vehicles, particularly in near-space environments where traditional inertial and satellite navigation methods face limitations. This study develops a stellar refraction navigation system that utilizes stellar refraction angle observations and the Implicit Unscented Kalman Filter (IUKF) for state estimation. A representative orbit with altitudes ranging from 60 km to 200 km is designed to simulate cross-domain flight conditions. The navigation performance is analyzed under varying conditions, including orbital altitude, as well as star sensor design parameters, such as limiting magnitude, field of view (FOV) value, and measurement error, along with different sampling intervals. The simulation results show that increasing the limiting magnitude from 5 to 8 reduced the position error from 705.19 m to below 1 m, with optimal accuracy reaching 0.89 m when using a 20° × 20° field of view and a 3 s sampling interval. In addition, shorter sampling intervals improved accuracy and filter stability, while longer intervals introduced greater integration drift. When the sampling interval reached 100 s, position error grew to the kilometer level. These findings validate the feasibility of using stellar refraction for autonomous navigation in cross-domain scenarios and provide design guidance for optimizing star sensor configurations and sampling strategies in future near-space navigation systems. Full article

This study investigates the behavior of spark discharges under various environmental conditions to simulate aspects of early-stage lightning dynamics, with a focus on their spectral characteristics, propagation, and ionization behavior. In a laboratory setting, spark discharges generated by a Tesla coil operating with high-frequency alternating current (AC) were analyzed under varying air humidity and water surface conductivity. Spectral analysis revealed that the discharges are dominated by the second positive system of molecular nitrogen N₂ (2P) and also exhibit the first negative system of molecular nitrogen ions N₂⁺ (1N). Notably, the N₂ (2P) emissions show strong peaks in the 350–450 nm range, closely matching spectral features typically associated with corona and streamer discharges in natural lightning. Environmental factors significantly influenced discharge morphology: in dry air, sparks exhibited longer and more branched paths, while in moist air, the discharges were shorter and more confined. Over water surfaces, the sparks spread radially, forming star-shaped patterns. Deionized (DI) water, with low conductivity, supported wider lateral propagation, whereas higher conductivity in tap water and saltwater suppressed discharge spread. The gap between the electrode tip and the surface also affected discharge extent and brightness. These findings demonstrate that Tesla coil discharges reproduce key features of early lightning processes and offer insights into how environmental factors influence discharge development. Full article

Be stars are characterized by the presence of a circumstellar Keplerian disk formed from material ejected from the rapidly rotating stellar surface. This article presents recent observational and theoretical progress on two central aspects of this phenomenon: the mechanisms driving mass loss, and the fate of the ejected material. Using simultaneous TESS photometry and ground-based spectroscopy, we examine the short-term variability associated with discrete mass ejection events, or “flickers”, and review strong evidence linking them to pulsational activity near the stellar surface. Complementary 3D hydrodynamic simulations reproduce key observational signatures and establish that disk formation requires compact and asymmetric ejection sites with sufficient angular momentum to overcome re-accretion. In systems with binary companions, new high-resolution simulations resolve the outer disk for the first time and identify five dynamically distinct regions, including a circumsecondary disk and a circumbinary spiral outflow. Together, these results provide a coherent framework that traces the full life cycle of disk material from pulsation-driven ejection near the stellar surface to its final destination, whether re-accreted by the companion or lost from the system entirely. Full article

Galaxies moving through the gas of the intracluster medium (ICM) experience ram pressure stripping, which can leave behind a gas tail. When a disk galaxy receives the wind edge-on, however, the characteristic signature is not a typical jellyfish tail, but rather an unwinding of the spiral arms. We aim to quantify such asymmetries both in the gas and in the stellar component of a simulated galaxy. To this end, we simulate a gas-rich star-forming spiral galaxy moving through a self-consistent ICM gas. The amplitude and location of the asymmetries were measured via Fourier decomposition. We found that the asymmetry is much more evident in the gas component, but it is also measurable in the stars. The amplitude tends to increase with time and the asymmetry radius migrates inwards. We found that, when considering the gas, the spiral arms extend much further and are more unwound than the corresponding stellar arms. Characterizing the unwinding via simulations should help inform the observational criteria used to classify ram pressure stripped galaxies, as opposed to asymmetries induced by other mechanisms. Full article

To endow a star sensor with strong robustness, low algorithm complexity, and a small database, this paper proposes an all-sky star identification algorithm based on spherical polar pattern matching. The proposed algorithm consists of three main steps. First, the guide star is rotated to be a polar star, and the polar and azimuth angles of neighboring stars are used as polar pattern elements of the guide star. Then, the relative azimuth histogram is applied to the spherical polar pattern matching, and a star pair after spherical polar pattern matching is identified through angular distance cross-verification. Finally, a reference star image is generated from the identified star pair to complete the matching process of all guide stars in the field of view. The proposed algorithm is verified by simulation experiments. The simulation results show that for a star sensor with a medium field of view (15° × 15°, 1024 × 1024 pixel) and a limiting magnitude of 6.0 Mv, the required database size is 161 KB. When false and missing star spots account for 50% of the guide stars and the star spot extraction error is 1.0 pixel, the average star identification time is 0.35 ms (@i7-4790), and the identification probability is 99.9%. However, when false and missing star spots account for 100% of the guide stars and the star spot extraction error is 5.0 pixel, the average star identification time is less than 2.0 ms, and the identification probability is 97.1%. Full article

The increasing deployment of unmanned aerial vehicles (UAVs) in complex urban environments necessitates efficient and reliable path planning algorithms. While traditional sampling-based methods such as Rapidly exploring Random Tree Star (RRT*) are widely adopted, their computational inefficiency and suboptimal path quality in intricate 3D spaces remain significant challenges. This study proposes a novel framework (MPN-RRT*) that integrates Motion Planning Networks (MPNet) with RRT* to enhance UAV navigation in 3D urban maps. A key innovation lies in reducing computational complexity through dimensionality reduction, where 3D urban terrains are sliced into 2D maze representations while preserving critical obstacle information. Transfer learning is applied to adapt a pre-trained MPNet model to the simplified maps, enabling intelligent sampling that guides RRT* toward promising regions and reduces redundant exploration. Extensive MATLAB simulations validate the framework’s efficacy across two distinct 3D environments: a sparse 200 × 200 × 200 map and a dense 800 × 800 × 200 map with no-fly zones. Compared to conventional RRT*, the MPN-RRT* achieves a 47.8% reduction in planning time (from 89.58 s to 46.77 s) and a 19.8% shorter path length (from 476.23 m to 381.76 m) in simpler environments, alongside smoother trajectories quantified by a 91.2% reduction in average acceleration (from 14.67 m/s² to 1.29 m/s²). In complex scenarios, the hybrid method maintains superior performance, reducing flight time by 14.2% and path length by 13.9% compared to RRT*. These results demonstrate that the integration of deep learning with sampling-based planning significantly enhances computational efficiency, path optimality, and smoothness, addressing critical limitations in UAV navigation for urban applications. The study underscores the potential of data-driven approaches to augment classical algorithms, providing a scalable solution for real-time autonomous systems operating in high-dimensional dynamic environments. Full article

Galactic cosmic rays (GCRs), composed of protons and atomic nuclei, are accelerated in sources such as supernova remnants and pulsar wind nebulae, reaching energies up to the PeV range. As they propagate through the interstellar medium, their interactions with dense regions like molecular clouds produce secondary particles, including gamma-rays and neutrinos. In this study, we use the Geant4 Monte Carlo toolkit to simulate secondary particle production from GCR interactions within the Perseus molecular cloud, a nearby star-forming region. Our model incorporates realistic cloud composition, a wide range of incidence angles, and both hadronic and electromagnetic processes across a broad energy spectrum. The results highlight molecular clouds as significant sites of multi-messenger emissions and contribute to understanding the propagation of GCRs and the origin of diffuse gamma-ray and neutrino backgrounds in the Galaxy. Full article