Search Results (7,227)

Multi-unmanned aerial vehicle (UAV) platforms integrate radio-frequency (RF) sensing, datalinks, and onboard embedded compute; adversarial electronic warfare (EW) degrades these subsystems through jamming and forces decentralized control policies to act on fragmented observations—a setting aligned with intelligent electronic systems and autonomous robotics in contested spectrum. Cooperative swarms then face two compounding failure modes: loss of coherent situational awareness, and reward-driven passive survival that suppresses mission completion. Memory-based multi-agent reinforcement learning (MARL) partially addresses the first but tends to reinforce the second; dense intent shaping addresses the second but becomes unreliable when observations are incomplete. We propose CIDA (Cognitive–Intent Dual-Stream Architecture), a reinforcement learning framework that decouples belief reconstruction from tactical intent at the representation level while coupling them through a unified actor–critic update. The cognitive stream encodes a 64-step observation history with a pre-normalized Transformer to reconstruct threat belief; the intent stream supplies a hierarchical potential field (reconnaissance, threat-weighted engagement, and approach incentives). A steady-state training mechanism (dynamic reward scaling and adaptive gradient clipping) stabilizes Transformer-based on-policy learning under non-stationary multi-agent dynamics. In a complex terrain scenario with SAM, AAA, and jammer assets, CIDA reaches 96.15% task success versus 12.21% (memoryless PPO) and 25.28% (MAPPO+RNN), with ablations showing nonlinear coupling and emergent tactics such as jammer bypass and weak-sector traversal. Results are robust to a four-fold sweep of the intent-shaping weight (above 90% success). Full article

Electric vehicles are an important carrier for achieving energy savings and emission reductions in the transportation sector. As the decision-making core of the powertrain, the energy management strategy is responsible for power allocation and energy scheduling and directly determines vehicle economy, power-source lifetime, and overall performance. Model predictive control can handle multiple constraints and objectives within a prediction horizon and realize online closed-loop decision-making via receding-horizon optimization and has become an important research direction for energy management of electric vehicles. This paper presents the basic principles and typical modeling framework of model predictive control and reviews its research progress in hybrid electric vehicle energy management. The related studies are categorized and comparatively analyzed from three perspectives—prediction methods, solution strategies, and optimization objectives—and the characteristics of different approaches are summarized. The review shows that model predictive control has advantages in multi-objective trade-offs and adaptation to time-varying operating conditions. However, practical implementation still faces significant barriers, including prediction uncertainty and computational complexity. Finally, the challenges and future directions of model-predictive-control-based energy management strategies are discussed. Full article

This paper proposes a finite-time target tracking control for multi-rotor unmanned aerial vehicles (UAVs) based on a vision-inertial-stabilized platform. To address the challenge of stable and accurate moving target tracking, the sliding mode control (SMC) technique is used to overcome limitations of conventional control algorithms, such as poor robustness and slow convergence speed. First, by computing the pixel deviation between the target and the image center, a kinematic model of the tracking target is established. Then, by introducing homogeneous system theory into the sliding mode surface design, a non-singular fast integral terminal sliding mode control (NFITSMC) is designed for target tracking via regulating the rotational angular acceleration of dual actuators in the vision inertial stabilized platform, thereby driving the pixel deviation to converge to zero in a finite time. Strict theoretical analysis is given to prove the finite-time stability and robustness of the closed-loop control system. Furthermore, simulation results demonstrate that the proposed method maintains higher tracking accuracy than SMC, ISMC, and TSMC. Full article

With the advancement of the “dual carbon” goals, the electric vehicle market has experienced explosive growth, and user review mining has become key data support for industrial quality improvement and low-carbon transportation transition. Addressing the limitations of existing sentiment classification methods in long-distance feature capture, cross-sentence semantic association, and emotional feature focus, this study proposes a BERT-Bi-xLSTM-Attention fusion model: BERT pre-trained semantic representation extracts deep contextual information, Bi-xLSTM models long-range dependency relationships, and the Attention mechanism locates sentiment-critical markers. Based on multi-platform review data from Chinese Autohome, Yiche, and China Quality Inspection Network, experiments show that the model achieves Accuracy, Recall, Precision, and F1 values of 0.9323, 0.9326, 0.9321, and 0.9328, significantly outperforming baseline models. A “sentiment-topic” fusion analysis framework is constructed, identifying five positive themes and four negative themes, revealing the dual emotional characteristics of range, driving experience, and smart features. Temporal analysis finds that negative attention to intelligent system reliability has continued to rise from 2021 to 2024, becoming an emerging user pain point. Combined with the above findings, it is recommended that consumers comprehensively evaluate multi-attribute experiences when purchasing; manufacturers prioritize optimizing user-concerned attributes; and policymakers improve industrial standards and regulatory mechanisms. This promotes high-quality development of electric vehicles, contributes to the realization of carbon neutrality goals in the transportation sector, and facilitates sustainable transportation development. Full article

Indoor localization for unmanned aerial vehicles (UAVs) remains challenging in GNSS-denied environments due to the difficulty of position calibration of multiple ultra-wideband (UWB) anchors and the asynchronous fusion of heterogeneous sensors. This paper proposes a multi-sensor fusion localization framework that integrates multi-anchor one-shot calibration with factor graph optimization (FGO). First, Landmark Multidimensional Scaling (LMDS) is used to reconstruct the relative geometry of the anchors and the onboard tag from ranging measurements. Then, rigid Procrustes alignment is performed using a small number of anchors with known coordinates in the East–North–Up (ENU) frame to recover the transformation to the ENU frame, thereby enabling efficient position calibration of multiple UWB anchors and UAV pose initialization. Subsequently, a tightly coupled factor graph is constructed by incorporating inertial measurement unit (IMU) pre-integration, UWB ranging, laser rangefinder height measurements, and visual–inertial odometry (VIO) pose constraints. The resulting nonlinear optimization problem is solved using incremental smoothing, which improves robustness against non-line-of-sight (NLOS) errors and long-term drift. Experimental results on anchor calibration, public datasets, and real-world indoor UAV flights demonstrate that the proposed method improves the accuracy and robustness of indoor UAV localization. In particular, on the real-world rectangle trajectory, FGO-TC reduces the RMSE by approximately 38.8% compared with FGO-LC. Full article

This study develops a multi-criteria and AI-assisted optimization framework that integrates the Analytic Hierarchy Process (AHP), K-means clustering, and Genetic Algorithm (GA) optimization within a Geographic Information System (GIS) environment to optimize electric vehicle (EV) charging station deployment across Abu Dhabi’s urban–rural gradient. The model generates a community-level Spatial Suitability Index (mean = 0.47) based on residential, commercial, and accessibility factors, which inform clustering into five deployment typologies reflecting distinct socio-spatial characteristics. GA-based spatial optimization under two policy pathways, Progressive and Thriving, balances accessibility, grid proximity, and utilization efficiency. Results show that the Thriving scenario achieves approximately 15–20% higher network coverage and equity compared to the Progressive case, demonstrating the value of adaptive, data-driven optimization for mixed urban–rural contexts. The integrated AHP–Clustering–GA approach provides a transferable and scalable blueprint for equitable, low-carbon mobility infrastructure planning in rapidly developing regions. Full article

The study of aircraft gust behaviour is essential in aerodynamic and structural design and analysis, as well as in airworthiness certification. The particularities of fixed-wing Remotely Piloted Aircraft (RPA) demand a specific study of gust effects on these vehicles and their implications in RPA design and operation. The research presented here addresses the investigation of gust behaviour of RPA within the frame of conceptual design through three complementary approaches, which are respectively based on the assessment of gust and manoeuvring envelopes of RPA, the modelisation of multi-mission flight profiles of RPA towards the evaluation of the variations in gust load factor along the mission, and the analysis of the interaction of RPA conceptual design parameters with gust behaviour. These approaches are applied to various case studies, providing several key insights into the gust behaviour characteristics of RPA. These include the assessment of the operational conditions in which gust-induced stall may occur and the way in which they interact with typical mission conditions of RPA, the evaluation of the impact of mission parameters in RPA gust response along with the capability of identifying the most critical gust load factor condition for the set of considered design missions, and the ways in which undesirable gust effects may be mitigated in the conceptual design stage through the change in overall RPA design parameters. Full article

Conventional cooperative control methods for multi-AUV systems typically rely on quasi-steady hydrodynamic assumptions and do not explicitly account for time-varying uncertainties in ocean dynamics. In addition, controller parameters are often tuned empirically. As a result, under complex disturbed flow fields and communication constraints, AUV swarms are prone to group fragmentation and reduced polarization, which undermines stable cooperative navigation. To address these limitations, we propose a double-gyre-flow-optimized autonomous underwater vehicle swarm (DGF-OAS) model for coordinated operations in time-varying flow fields. The proposed model incorporates a heading-aware graph attention mechanism to adaptively adjust adjacency weights among agents with different roles. It further integrates the Lennard–Jones potential to preserve safe inter-vehicle spacing and embeds a periodically varying double-gyre flow field to characterize ocean disturbances. Bayesian optimization is then employed to automatically identify suitable weights for the alignment and attraction–repulsion terms, thereby improving swarm cohesion and environmental adaptability. Simulation results demonstrate that, under flow-field disturbances, DGF-OAS achieves group polarization of up to 96%, reduces the average task completion time by 15.84% compared with the baseline model, and attains a task completion rate of 97%, significantly outperforming the compared methods. These findings indicate that the proposed approach exhibits strong adaptability and stability in complex environments and offers an effective solution for AUV swarm control. Full article

This paper offers a critical review of cutting-edge research on multi-energy microgrids (MEMs), with a novel exploration of their potential role in supporting urban air mobility (UAM), specifically electric vertical takeoff and landing (eVTOL) aircraft. While extensive research has focused on improving the economic performance and emission reductions of MEMs, particularly in the context of electric vehicle (EV) charging, there remains a significant gap in understanding how microgrids can support the decarbonization of UAM. The paper examines the opportunities and challenges of integrating microgrids with UAM operations, highlighting the need for more research to optimize energy management systems that balance renewable energy use with the growing demand for aerial transport. Thermal energy storage systems are emphasized as a critical component for addressing transportation energy needs, offering a promising solution to reduce carbon emissions while enhancing system efficiency. This review aims to provide new insights into how the coupling of microgrids and UAM can contribute to the development of economically and environmentally sustainable smart cities. Full article

The densely distributed, scale-varying objects in unmanned aerial vehicle (UAV) images, together with their dynamic, diverse, and unconstrained backgrounds, make conventional detection methods prone to missed detections, false alarms, and localization biases. To improve UAV vision tasks, we propose AD-YOLO, a unified method tailored for small object detection in traffic-dense settings. First, a module combining an adaptive rotation convolution unit and grouped directional attention with mixed-kernel features is introduced to enhance the model’s orientation invariance and multi-scale discrimination. Then, a dual-path collaborative feature pyramid network is proposed to jointly refine the model’s semantic and spatial details via a multi-directional context aggregation path and a hierarchical semantic progressive fusion path. Last, a hierarchically dense reparameterized large-kernel module is designed to produce broader receptive fields with reduced computational complexity. Extensive experiments on the VisDrone2019 and UAVDT datasets demonstrate that AD-YOLO outperforms state-of-the-art methods in detection accuracy while maintaining favorable computational efficiency. Full article

Flood disasters pose significant threats to human life and infrastructure, creating an urgent need for reliable vision-based environmental sensing technologies for rapid floodwater identification. Vision-based platforms such as unmanned surface vehicles (USVs) provide an effective solution for monitoring inland water environments; however, accurate floodwater segmentation remains challenging due to complex water boundaries, reflections, and background interference. To address these issues, we propose RFD-BiSeNet V2, a lightweight semantic segmentation network. Building upon BiSeNet V2, our model integrates an edge-aware learning strategy to track dynamic contours, a feature refinement module to suppress reflection noise, and a multi-scale feature fusion module to accommodate varying morphological scales. Evaluated on a comprehensive dataset incorporating USV data, UAV imagery, and diverse real-world scenes, RFD-BiSeNet V2 achieves an mIoU of 97.10%, outperforming the baseline by 6.68%. Crucially, the results demonstrate the practical implications of our architectural advancements: the edge-aware and feature refinement modules successfully sharpen ambiguous water boundaries and effectively filter out severe surface reflections, directly driving the segmentation accuracy. With a compact size of 5.95M parameters and real-time inference capabilities, the model offers a robust and highly efficient solution suitable for resource-constrained deployments across diverse intelligent environmental sensing systems. Full article

Unmanned Aerial Vehicle (UAV) orthophoto generation in complex environments remains challenging because weak textures, reflective surfaces, occlusions, and large scene extents can cause incomplete reconstruction, ghosting, and seam artifacts. Although 3D Gaussian Splatting (3DGS) offers an efficient explicit scene representation, its use in large-scale UAV orthophoto generation is limited by high memory consumption, unstable densification, and insufficient support for mapping-oriented orthographic rendering. This paper proposes a single-GPU 3DGS framework for UAV orthophoto generation by integrating adaptive spatial block partitioning, deterministic structure-sensitive adaptive density control, and core–buffer tiled orthographic rendering with weighted blending. The proposed framework decomposes large scenes into resource-bounded subregions, guides Gaussian densification using fixed multi-view neighborhoods and edge-enhanced dynamic consistency, and generates large-format orthophotos with reduced boundary and seam artifacts. Experiments on MatrixCity-S and multiple UAV photogrammetric datasets show that the method achieves competitive reconstruction quality and improved resource efficiency. On MatrixCity-S, it reaches 29.01 dB PSNR and 0.901 SSIM, while completing training in 1 h 49 min on a single NVIDIA RTX 3090 GPU. Compared with BlockGS, peak VRAM consumption is reduced by more than 38% across datasets. Under geo-aligned comparison conditions, line-measurement comparisons with MetaShape and Pix4DMapper yield RMSE values of 0.099 m and 0.087 m, respectively. These results demonstrate the potential of the proposed framework for memory-efficient 3DGS-based UAV orthophoto generation under constrained hardware resources, while further control-point-based validation is still needed for rigorous surveying-grade applications. Full article

The increasing adoption of electric vehicles in residential buildings creates challenges for charging infrastructure management, particularly in pricing services to balance revenue, user satisfaction, and grid stability. Traditional pricing methods, such as fixed rates and time-of-use tariffs, cannot adapt to the dynamic nature of charging demand. We propose a reinforcement learning framework for dynamic pricing of residential EV charging stations. The framework formulates the pricing problem as a Markov decision process and employs proximal policy optimization to learn a pricing policy based on real-time conditions. The state representation includes ten features covering temporal indicators, charging loads, grid status, traffic, and weather. A multi-objective reward function balances revenue, station utilization, grid stability, and user satisfaction. The system is trained on 6878 charging sessions from a residential complex in Trondheim, Norway. Compared with fixed pricing and time-of-use pricing, the proposed method achieves an overall score of 0.569, representing improvements of 32.9% and 48.9%, respectively. Sensitivity analysis confirms that the model remains robust across different demand response assumptions. The main contributions include a custom reinforcement learning environment for residential EV charging and empirical evidence that learned policies outperform traditional pricing approaches. Full article

In vehicle-mounted thermoelectric refrigerators, limited installation space and fluctuating ambient conditions make it difficult to achieve both sufficient cooling capacity and low power consumption. However, most previous studies have focused on thermoelectric materials or standalone devices rather than system-level optimization under realistic vehicle constraints. To address this issue, a three-dimensional multiphysics-coupled finite element model combined with a parametric optimization approach was developed for a vehicle-mounted thermoelectric refrigerator used in one of Dongfeng Motor’s new energy vehicle models. Based on this model, the effects of key geometric parameters, including thermoelectric leg height (l), leg width (w), and leg number (p_d), as well as operating conditions, namely input voltage (U) and ambient temperature (T_a), on the overall performance of the refrigerator, including cooling capacity (Q_c), coefficient of performance (COP), and interior center temperature (T), were systematically investigated. The results show that under nominal operating conditions (U = 13.5 V, T_a = 25 °C), increasing p_d from low to moderate values significantly improves cooling capacity, reduces the interior temperature, and decreases power consumption. However, further increases in p_d lead to diminishing improvements in cooling performance because of the heat dissipation limitation on the hot side. By comprehensively evaluating cooling performance and energy consumption, the optimal design was determined to have 322 legs, a leg width of 1.4 mm, and a leg height of 1.8 mm. Under these conditions, the refrigerator achieved a cooling capacity of 13.95 W, a power consumption of 38.4 W, a COP of 0.36, and a compartment center temperature of 10.71 °C. Compared with the conventional 254-leg module (w = 1.4 mm, l = 1.6 mm), the optimized design improved the COP by more than 45.1% and reduced power consumption by 28.8%. In addition, the results indicate that under high ambient temperature conditions, the overall system performance is mainly limited by the hot-side heat rejection capacity. Overall, this study provides an effective structural optimization approach for improving the energy efficiency of compact thermoelectric refrigerators in confined spaces and offers a useful reference for the low-power design of vehicle-mounted cooling devices. Full article

With the rapid development of new energy vehicles, fast and reliable prediction of power battery collision damage has become increasingly important. Traditional finite-element analysis is computationally expensive and difficult to deploy for rapid prediction under varying conditions. Although learning-based methods are faster, they usually rely on single-fidelity data: high-fidelity data is accurate but scarce and costly, while low-fidelity data is abundant but less reliable. Existing multi-fidelity methods alleviate this issue, yet often suffer from imbalanced sample allocation and weak cross-fidelity modeling. Moreover, current adaptive sampling strategies cannot dynamically determine the appropriate fidelity for different regions of the design space. To address these challenges, we propose HNGP-LCA, a multi-fidelity active learning framework for battery pack collision damage prediction. Our method consists of two components: (1) an Ensemble Nested Gaussian Process module that integrates single-layer and double-layer nested Gaussian process regression to better capture high–low fidelity correlations; and (2) a Location Information Cost-aware Active Learning strategy that leverages positional information to reconstruct expected improvement under different fidelities, enabling dynamic fidelity selection during sampling. Experiments on multiple synthetic benchmarks and a real battery pack engineering case demonstrate that HNGP-LCA achieves a better trade-off among accuracy, efficiency, and cost than strong baselines such as NARCO and MFBO. In the engineering case, it improves prediction accuracy by 0.6% over NARCO and 1.29% over MFBO, while reducing dependence on expensive high-fidelity data. These results show that HNGP-LCA provides an effective and practical solution for battery collision damage prediction. Full article