Search Results (158)

Cooperative path planning for unmanned aerial vehicle (UAV) swarms has attracted considerable research attention, yet it remains challenging in complex, uncertain environments. To tackle this problem, we model the cooperative path planning task as a heterogeneous decentralized Markov Decision Process (MDP), emphasizing the symmetry-inspired role assignment between leader and wingmen UAVs, which ensures balanced and coordinated behaviors in dynamic settings. We address the problem using a Multi-Agent Soft Actor-Critic (MASAC) framework enhanced with a symmetry-aware reward mechanism designed to optimize multiple cooperative objectives: simultaneous arrival, formation topology preservation, dynamic obstacle avoidance, trajectory smoothness, and inter-agent collision avoidance. This design promotes behavioral symmetry among agents, enhancing both coordination efficiency and system robustness. Simulation results demonstrate that our method achieves efficient swarm coordination and reliable obstacle avoidance. Quantitative evaluations show that our MASAC-CA algorithm achieves a Mission Success Rate (MSR) of 99.0% with 2–5 wingmen, representing approximately 13% improvement over baseline MASAC, while maintaining Formation Keeping Rates (FKR) of 59.68–26.29% across different swarm sizes. The method also reduces collisions to near zero in cluttered environments while keeping flight duration, path length, and energy consumption at levels comparable to baseline algorithms. Finally, the proposed model’s robustness and effectiveness are validated in complex uncertain environments, underscoring the value of symmetry principles in multi-agent system design. Full article

With the development of unmanned aerial vehicle (UAV) technology, multimachine collaborative operations have become the core model for increasing mission effectiveness. However, large-scale UAV clusters face challenges such as dynamic security threats, heterogeneous data fusion difficulties, and resource-constrained decision-making delays. Traditional single-machine intelligent architectures have limitations when addressing new threats, such as insufficient real-time response capabilities. To address these issues, this paper presnts an LLM-layered collaborative security architecture (LLM-LCSA) for multimachine collaborative security. This architecture optimizes the spatiotemporal fusion efficiency of multisource asynchronous data through cloud–edge–end collaborative deployment, combining an end lightweight LLM, an edge medium LLM, and a cloud-based foundation LLM. Additionally, a Mixture of Experts (MoEs) intelligent algorithm that dynamically activates the most relevant expert models by leveraging a threat–expert association matrix is introduced, thereby increasing the accuracy of complex threat identification and dynamic adaptability. Moreover, a resource-aware multi-objective optimization model is constructed to generate optimal decisions under resource constraints. Simulation results indicate that compared with traditional methods, LLM-LCSA achieves an average 7.92% improvement in the threat detection accuracy, reduces the system’s total response time by 44.52%, and enables resource scheduling during off-peak periods. This architecture provides an efficient, intelligent, and scalable solution for secure collaboration among UAV swarms. Future research should further explore its application potential in 6G network integration and large-scale swarm environments. Full article

This paper addresses the cooperative formation control problem for a heterogeneous unmanned system composed of Unmanned Surface Vehicles (USVs) and Autonomous Underwater Vehicles (AUVs) under coexisting constraints of actuator faults, time-varying communication topology, and communication delay. First, a unified dynamic model is established under the Euler–Lagrange framework. Building on this, a novel distributed adaptive fault-tolerant control (DAFTC) framework is proposed. This framework integrates a Dynamic Event-Triggered Mechanism (DETM) to address communication bandwidth limitations, alongside an adaptive fault-tolerant strategy to enhance system robustness. The novelty lies in the cohesive integration of DETM for communication efficiency and adaptive laws for online fault compensation (both loss of effectiveness and bias), while rigorously handling communication delays via Lyapunov–Krasovskii analysis. It is proven via Lyapunov stability analysis that the proposed control protocol ensures all signals in the closed-loop system remain semi-globally uniformly ultimately bounded, with the formation tracking error converging to an adjustable compact set. Simulation results demonstrate the framework’s effectiveness. Compared to periodic communication (0.1 s interval), the proposed DETM reduces the communication load by over 99.6%. Even when subjected to a 25% effectiveness fault and a 5 Nm bias fault, the root-mean-square (RMS) tracking error is maintained below 0.15 m, validating the system’s high performance and robustness. Full article

The coordination of heterogeneous Unmanned Aerial Vehicles (UAVs) for complex, multi-stage tasks presents a significant challenge in robotics and autonomous systems. Traditional linear models often fail to capture the emergent synergistic effects and dynamic nature of multi-agent collaboration. To address these limitations, this paper proposes a novel hierarchical framework based on a Mission Chain (MC) concept. We systematically define and model key elements of multi-agent collaboration, including Mission Chains (MCs), Execution Paths (EPs), Task Networks (TNs), and Solution Spaces (SSs), creating an integrated theoretical structure. Based on this framework, we formulate the problem as a Sensor–Effector–Target Assignment challenge and propose a Marginal Return-Based Heuristic Algorithm (MRBHA) for efficient dynamic task allocation. Simulations demonstrate that our proposed MRBHA achieves a substantially higher total expected mission value—outperforming standard greedy and random assignment strategies by 14% and 77%, respectively. This validates the framework’s ability to effectively capitalize on synergistic opportunities within the UAV network. The proposed system provides a robust and scalable solution for managing complex missions in dynamic environments, with potential applications in search-and-rescue, environmental monitoring, and intelligent logistics. Full article

Early warning systems on edge devices such as satellites and unmanned aerial vehicles (UAVs) are essential for effective forest fire prevention. Edge Intelligence (EI) enables deploying deep learning models on edge devices; however, traditional convolutional neural networks (CNNs)/Transformer-based models struggle to balance local-global context integration and computational efficiency in such constrained environments. To address these challenges, this paper proposes HybriDet, a novel hybrid-architecture neural network for wildfire detection. This architecture integrates the strengths of both CNNs and Transformers to effectively capture both local and global contextual information. Furthermore, we introduce efficient attention mechanisms—Windowed Attention and Coordinate-Spatial (CS) Attention—to simultaneously enhance channel-wise and spatial-wise features in high-resolution imagery, enabling long-range dependency modeling and discriminative feature extraction. Additionally, to optimize deployment efficiency, we also apply model pruning techniques to improve generalization performance and inference speed. Extensive experimental evaluations demonstrate that HybriDet achieves superior feature extraction capabilities while maintaining high computational efficiency. The optimized lightweight variant of HybriDet has a compact model size of merely 6.45 M parameters, facilitating seamless deployment on resource-constrained edge devices. Comparative evaluations on the FASDD-UAV, FASDD-RS, and VOC datasets demonstrate that HybriDet achieves superior performance over state-of-the-art models, particularly in processing highly heterogeneous remote sensing (RS) imagery. When benchmarked against YOLOv8, HybriDet demonstrates a 6.4% enhancement in mAP50 on the FASDD-RS dataset while maintaining comparable computational complexity. Meanwhile, on the VOC dataset and the FASDD-UAV dataset, our model improved by 3.6% and 0.2%, respectively, compared to the baseline model YOLOv8. These advancements highlight HybriDet’s theoretical significance as a novel hybrid EI framework for wildfire detection, with practical implications for disaster emergency response, socioeconomic security, and ecological conservation. Full article

In order to enhance the wind-resistance capability and achieve a lightweight design of high-altitude unmanned airships, this study proposes a rapid optimization method for a heterogeneous propeller propulsion system. This system integrates contra-rotating and ducted propellers to exploit their respective aerodynamic advantages. First, surrogate models of the contra-rotating propeller, contra-rotating motor, ducted propeller, and ducted motor were constructed using an optimal Latin hypercube sampling method based on the max–min criterion. Then, within the optimization framework, propeller–motor matching principles and energy balance constraints were incorporated to minimize the total weight of the propulsion and energy systems. A case study on a conventional high-altitude unmanned airship demonstrates that, under the same wind-resistance capability, the adoption of the heterogeneous propeller electric propulsion system reduces the total propulsion-and-energy system weight by 24.94%. This method integrates the advantages of contra-rotating and ducted propellers, thereby overcoming the limitations of conventional propulsion architectures. It provides a new approach for designing lightweight, efficient, and long-endurance propulsion systems for near-space high-altitude platforms. Full article

Industrial environments are complex, making the monitoring of gauge meters challenging. This is especially true in confined spaces, underground, or at high altitudes. These difficulties underscore the need for intelligent solutions in the inspection and monitoring of plant assets, such as gauge meters. In this study, we plan to integrate unmanned ground vehicles and unmanned aerial vehicles to address the challenge, but the integration of these heterogeneous systems introduces additional complexities in terms of coordination, interoperability, and communication. Our goal is to develop a multi-domain robotic swarm system for industrial mapping and asset monitoring. We created an experimental setup to simulate industrial inspection tasks, involving the integration of a TurtleBot 2 and a QDrone 2. The TurtleBot 2 utilizes simultaneous localization and mapping (SLAM) technology, along with a LiDAR sensor, for mapping and navigation purposes. The QDrone 2 captures high-resolution images of meter gauges. We evaluated the system’s performance in both simulation and real-world environments. The system achieved accurate mapping, high localization, and landing precision, with 84% accuracy in detecting meter gauges. It also reached 87.5% accuracy in reading gauge indicators using the paddle OCR algorithm. The system navigated complex environments effectively, showcasing the potential for real-time collaboration between ground and aerial robotic platforms. Full article

Vertical greening (VG) delivers measurable urban ecosystem benefits, yet maintenance is constrained by at-height safety risks, heterogeneous facade geometries, and low labor efficiency. Although unmanned aerial vehicles show promise, most studies optimize isolated modules rather than providing a user-oriented, system-level pathway. This paper proposes a closed-loop, multi-method framework integrating the Decision-Making Trial and Evaluation Laboratory-Analytic Network Process, the Functional Analysis System Technique, and the Theory of Inventive Problem Solving. DEMATEL-ANP models causal interdependencies among requirements and derives prioritized weights,; FAST decomposes functions and localizes conflicts, and TRIZ converts those conflicts into principle-guided structural concepts—establishing a traceable requirements → functions → conflicts → structure pipeline. We illustrate the approach at the prototype level with Rhino–KeyShot visualizations under near-facade constraints, showing how prioritized requirements propagate into candidate UAV architectures. The framework structures the identification and resolution of tightly coupled technical conflicts, supports adaptability in facade-proximal scenarios, and provides a transparent mapping from user needs to structure-level concepts. Claims are restricted to methodological feasibility; comprehensive quantitative field validation remains for future work. The framework offers a reproducible methodological reference for the systematic design and decision-making of intelligent UAV maintenance systems for VG. Full article

This paper proposes an intelligent hierarchical fuzzy decision-making framework for threat detection and identification in Counter-Unmanned Aerial Vehicle (Counter-UAV) systems, based on the fusion of heterogeneous sensor data. To address the increasing complexity and ambiguity in modern UAV threats, this study introduces a novel three-stage fuzzy inference architecture that supports adaptive sensor evaluation and optimal pairing. The proposed methodology consists of three-layered Fuzzy Inference Systems (FIS): FIS-A quantifies sensor effectiveness based on UAV flight altitude and detection probability; FIS-B assesses operational suitability using sensor range and cost; and FIS-C synthesizes both outputs, along with sensor capability overlap, to determine the composite suitability of sensor pairs. This hierarchical structure enables detailed analysis and system-level optimization, reflecting real-world constraints and performance trade-offs. Simulation-based evaluation using diverse sensor modalities (EO/IR, Radar, Acoustic, RF), supported by empirical data and literature, demonstrates the framework’s ability to handle uncertainty, enhance detection reliability, and support cost-effective sensor deployment in Counter-UAV operations. The framework’s modularity, scalability, and interpretability represent significant advancements in intelligent Counter-UAV system design, offering a transferable methodology for dynamic threat environments. Full article

In accordance with the PRISMA 2020 guidelines, this systematic review analyzed 73 publications (1997–2025) to summarize advancements in robotic aircraft skin inspection, focusing on the integrated pipeline from data acquisition to defect analysis. The review included studies on Unmanned Aerial Vehicles (UAVs) and Unmanned Ground Vehicles (UGVs) for external skin inspection, which present clear technical contributions, while excluding internal inspections and non-technical reports. Literature was retrieved from IEEE conferences, journals, and other academic databases, and key findings were summarized via the categorical analysis of motion planning, perception modules, and defect detection algorithms. Key limitations identified include the fragmentation of core technical modules, unresolved bottlenecks in dynamic environments, challenges in weak-texture and all-weather perception, and a lack of mature integrated systems with practical validation. The study concludes by advocating for future research in multi-robot heterogeneous collaborative systems, intelligent dynamic task scheduling, large model-based airworthiness assessment, and the expansion of inspection scenarios, all aimed at achieving fully autonomous and reliable operations. Full article

In modern amphibious operations, the dynamic scheduling of shipborne unmanned helicopters faces challenges including highly uncertain operational environments, complex and variable mission requirements, and stringent resource constraints. To tackle this issue, this paper presents an integrated solution encompassing modeling, scheduling strategies, and optimization algorithms. First, a Dynamic Scheduling Model for Integrated Operation-Support Activities of Shipborne Unmanned Helicopters (SUH-DSMIOSA) is developed, which integrates mission temporal constraints, heterogeneous unmanned helicopter resources, and deck support resource constraints to achieve integrated modeling of operational tasks and support operations. Second, a Multi-Modal Disturbance-Aware Adaptive Rescheduling Strategy (MDAARS) is designed, which adaptively selects targeted rescheduling schemes by identifying disturbance types and establishes a differentiated evaluation system to quantify their effects. And then, an Improved Tabu Search algorithm (I-TS) is proposed, enhancing search efficiency and solution quality through adaptive tabu length adjustment, enhanced neighborhood operations, and an intelligent restart strategy. The results show that the I-TS algorithm achieved an average convergence speed improvement of 40.2% and a solution quality enhancement of 1.76%. The algorithm reaches a normalized efficiency of 0.98 within 10 iterations and maintains excellent stability throughout the entire convergence process. When facing disturbance events, the proposed algorithm reduces the mission change rate by an average of 20.1% and improves the rescheduling success rate by 2.8% compared to other algorithms. This research provides theoretical support and technical pathways for efficient dynamic scheduling of unmanned helicopters in amphibious operations. Full article

Aerial-assisted vehicular edge computing (AVEC) has emerged as a transformative approach to addressing the limitations of traditional vehicular edge computing (VEC) in dynamic vehicular environments. By integrating platforms such as unmanned aerial vehicles (UAVs), high-altitude platforms (HAPs), and satellites, AVEC systems offer enhanced scalability, flexibility, and responsiveness, enabling efficient resource allocation and adaptive decision-making. This paper presents a comprehensive survey of resource allocation techniques in AVEC, addressing both traditional and reinforcement learning-based approaches. These techniques aim to optimize the allocation of bandwidth, computation, and energy resources across heterogeneous platforms, ensuring reliable and efficient operations in diverse scenarios. Additionally, the study examines key challenges inherent in AVEC, including achieving seamless interoperability among diverse platforms, addressing scalability in large-scale systems, and adapting to real-time environmental dynamics. To address these challenges, the paper proposes future research directions, such as leveraging advanced technologies like quantum computing for solving complex optimization problems, employing tiny machine learning (TinyML) to enable resource-efficient intelligence on low-power devices, and predictive task offloading to enhance proactive resource management. By presenting a detailed analysis of existing techniques and identifying critical research opportunities, this paper seeks to guide researchers and practitioners in developing more efficient, secure, and adaptive AVEC systems. The insights from this study contribute to advancing the design and deployment of resilient intelligent transportation networks, paving the way for the next generation of vehicular connectivity. Full article

The strategic development of the low-altitude economy requires efficient urban logistics solutions. The existing Unmanned Aerial Vehicle (UAV) truck delivery system faces severe challenges in dealing with dynamic weather constraints and multi-agent coordination. This article proposes a neural network-based optimisation framework that integrates constrained K-means clustering and a three-stage neural architecture. In this work, a mathematical model for heterogeneous vehicle constraints considering time windows and UAV energy consumption is developed, and it is validated through reference to the Solomon benchmark’s arithmetic examples. Experimental results show that the Truck–Drone Cooperative Traveling Salesman Problem (TDCTSP) model reduces the cost by 21.3% and the delivery time variance by 18.7% compared to the truck-only solution (Truck Traveling Salesman Problem (TTSP)). Improved neural network (INN) algorithms are also superior to the traditional genetic algorithm (GA) and Adaptive Large Neighborhood Search (ALNS) methods in terms of the quality of computed solutions. This research provides an adaptive solution for intelligent low-altitude logistics, which provides a theoretical basis and practical tools for the development of urban air traffic under environmental uncertainty. Full article

Background: The increasing deployment of unmanned aerial vehicles (UAVs) for logistics in smart cities presents pressing challenges related to identity spoofing, unauthorized payload transport, and airspace security. Existing drone defense systems (DDSs) struggle to verify both drone identity and payload authenticity in real time, while blockchain-assisted solutions are often hindered by high latency and limited scalability. Methods: To address these challenges, we propose iBANDA, a blockchain- and AI-assisted DDS framework. The system integrates a lightweight You Only Look Once 5 small (YOLOv5s) object detection model with a Snowball-based Proof-of-Stake consensus mechanism to enable dual-layer authentication of drones and their attached payloads. Authentication processes are coordinated through an edge-deployable decentralized application (DApp). Results: The experimental evaluation demonstrates that iBANDA achieves a mean average precision of 99.5%, recall of 100%, and an F1-score of 99.8% at an inference time of 0.021 s, validating its suitability for edge devices. Blockchain integration achieved an average network latency of 97.7 ms and an end-to-end transaction latency of 1.6 s, outperforming Goerli, Sepolia, and Polygon Mumbai testnets in scalability and throughput. Adversarial testing further confirmed resilience to Sybil attacks and GPS spoofing, maintaining a false acceptance rate below 2.5% and continuity above 96%. Conclusions: iBANDA demonstrates that combining AI-based visual detection with blockchain consensus provides a secure, low-latency, and scalable authentication mechanism for UAV-based logistics. Future work will explore large-scale deployment in heterogeneous UAV networks and formal verification of smart contracts to strengthen resilience in safety-critical environments. Full article

This study proposes a novel vehicle speed prediction model for plateau transportation—CLS-DW Stacking (Constrained Least Squares Dynamic Weighting Model Stacking)—which holds significant implications for the sustainable development of transportation systems in high-altitude regions. Research on sharp-curved roads on mountainous plateaus remains scarce. Compared with plain areas, data acquisition in such regions is constrained by government confidentiality policies, while complex environmental and topographical conditions lead to substantial variations in road alignment and elevation. To address these challenges, this study presents a sustainable data acquisition and construction method: unmanned aerial vehicle (UAV) video data are processed through road image segmentation, trajectory tracking, and three-dimensional modeling to generate multi-source heterogeneous datasets for both single-curve and continuous-curve scenarios. Building upon these datasets, the proposed framework integrates constrained least squares with multiple deep learning methods to achieve accurate traffic flow prediction. Bi-LSTM (Bidirectional Long Short-Term Memory), Informer, and GRU (Gated Recurrent Unit) are employed as base learners, and the loss function is redefined with non-negativity and normalization constraints on the weights. This ensures optimal weight coefficients for each base learner, with the final prediction obtained via weighted summation. The experimental results show that, compared with single deep learning models such as Informer, the proposed model reduces the mean squared error (MSE) by 1.9% on the single curve dataset and by 7.7% on the continuous curve dataset. Furthermore, by combining vehicle speed predictions across different altitude gradients with decision tree-based interpretable analysis, this research provides scientific support for developing altitude-specific and precision-oriented speed limit policies. The outcomes contribute to accident risk reduction, traffic congestion mitigation, and carbon emission reduction, thereby improving road resource utilization efficiency. This work not only fills the research gap in traffic prediction for sharp-curved plateau roads but also supports the construction of green transportation systems and the broader objectives of sustainable development in high-altitude regions. Full article