Search Results (133)

Multiple unmanned aerial vehicle (UAV) systems have attracted considerable research interest due to their broad applications, such as formation control. However, decentralized UAV formation faces challenges stemming from limited local observations, which may lead to consistency conflicts, and excessive communication. To address these issues, this paper proposes SemanticBC-DecAF, a decentralized adaptive formation (DecAF) framework under a leader–follower architecture, incorporating a semantic broadcast communication (SemanticBC) mechanism. The framework consists of three modules: (1) a proximal policy optimization (PPO)-based semantic broadcast module, where the leader UAV transmits semantically encoded global obstacle images to followers to enhance their perception; (2) a YOLOv5-based detection and position estimation module, enabling followers to infer obstacle locations from recovered images; and (3) a multi-agent proximal policy optimization (MAPPO)-based formation module, which fuses global and local observations to achieve adaptive formation and obstacle avoidance. Experiments in the multi-agent simulation environment MPE show that the proposed framework significantly improves global perception and formation efficiency compared with methods that rely on local observations. Full article

Cold-air pooling (CAP) and frost risk represent significant climate-related hazards in karstic and agricultural environments, where local topography and surface cover strongly modulate microclimatic conditions. This study focuses on the Mohos sinkhole, Hungary’s cold pole, situated on the Bükk Plateau, to investigate the formation, structure, and persistence of CAPs in a Central European karst depression. High-resolution terrain-based modeling was conducted using UAV-derived digital surface models combined with multiple GIS tools (Sky-View Factor, Wind Exposition Index, Cold Air Flow, and Diurnal Anisotropic Heat). These models were validated and enriched by multi-level temperature measurements and thermal imaging under various synoptic conditions. Results reveal that temperature inversions frequently form during clear, calm nights, leading to extreme near-surface cold accumulation within the sinkhole. Inversions may persist into the day due to topographic shading and density stratification. Vegetation and basin geometry influence radiative and turbulent fluxes, shaping the spatial extent and intensity of cold-air layers. The CAP is interpreted as part of a broader interconnected multi-sinkhole system. This integrated approach offers a transferable, cost-effective framework for terrain-driven frost hazard assessment, with direct relevance to precision agriculture, mesoscale model refinement, and site-specific climate adaptation in mountainous or frost-sensitive regions. Full article

To address issues such as inadequate robustness in dynamic obstacle avoidance, instability in formation morphology, severe resource conflicts in multi-task scenarios, and challenges in global path planning optimization for unmanned aerial vehicles (UAVs) operating in complex airspace environments, this paper examines the advantages and limitations of conventional UAV formation cooperative control theories. A multi-UAV cooperative control strategy is proposed, integrating adaptive game theory and deep reinforcement learning within a unified framework. By employing a three-layer information fusion architecture—comprising the physical layer, intent layer, and game-theoretic layer—the approach establishes models for multi-modal perception fusion, game-theoretic threat assessment, and dynamic aggregation-reconstruction. This optimizes obstacle avoidance algorithms, facilitates interaction and task coupling among formation members, and significantly improves the intelligence, resilience, and coordination of formation-wide cooperative control. The proposed solution effectively addresses the challenges associated with cooperative control of UAV formations in complex traffic environments. Full article

Contemporary multi-UAV formations face dual challenges of obstacle avoidance and rapid formation recovery. To enable UAV swarms to efficiently restore their predefined configurations post-obstacle navigation, a formation recovery strategy grounded in Nash equilibrium game theory is proposed in this paper. By integrating the IVY optimization algorithm, a collaborative control model that systematically balances individual UAV interests with swarm-level objectives through carefully designed optimization criteria is established. Comparative experimental results demonstrate that, compared to traditional formation obstacle-avoidance algorithms, Improved Particle Swarm Optimization (IPSO), Ant Colony Optimization (ACO), and Genetic Algorithm (GA), our method exhibits superior performance across multiple key metrics, including average path length, formation accuracy rate, recovery time, and total time consumption. Real-flight tests on a multi-UAV platform confirm IVYA-Nash surpasses improved APF in formation accuracy and aerodynamic disturbance resistance, proving robustness in dynamic multi-agent scenarios. The work provides an efficient and reliable solution for coordinated control of UAV formations in complex environments. Full article

Formation control plays a vital role in coordinating multi-agent systems and swarm robotics, enabling collaboration in applications such as autonomous vehicles, robotic swarms, and distributed sensing. This paper introduces the formation-control problem, highlights its challenges, and compares centralized and decentralized schemes. We review recent advances and analyze popular algorithms, then propose a hybrid framework that combines leader–follower tracking with an artificial potential field (APF) safety layer. In three-UAV tests, the followers cross paths and one encounters a static obstacle. We run multiple simulations across scenarios with obstacles and varying formations. Results show the hybrid controller maintains the required formation while avoiding inter-agent collisions. Using quantitative metrics, we find the leader–follower baseline achieves the lowest formation error but has the most safety violations, whereas APF greatly improves safety at the cost of higher error. The hybrid combines these strengths—delivering APF-level safety with lower error and negligible runtime overhead—providing a practical balance between precise formation keeping and robust collision avoidance. Full article

To enable safe cooperative flight of multi-UAV formations in urban 3-D airspace with wind-field disturbances, we develop an integrated planning-control framework.The planning layer uses an APF-guided RRT* with continuous collision prediction and explicit velocity/acceleration limits, and compensates wind online.The control layer adopts a dual-loop MA-AADRC-SMC design. An adaptive ESO estimates disturbances for feed-forward cancellation, and an SMC term improves robustness and tracking accuracy. By coupling the planned trajectory with speed-weighted repulsive fields, the framework coordinates path and attitude in closed loop, enabling collision-free and overshoot-free formation flight in wind and clutter. Simulations show higher tracking accuracy and better formation stability than ADRC, PID and SMC. A Lyapunov analysis proves uniform boundedness and asymptotic stability. The framework is scalable to applications such as disaster assessment and urban air transport. Full article

Urban particulate matter (PM) pollution critically impacts public health and climate. However, traditional ground-based monitoring fails to resolve vertical PM distribution, limiting understanding of transport and stratification-coupled mechanisms. Vertical profiles collected by an unmanned aerial vehicle (UAV) over Hangzhou, a core megacity in China’s Yangtze River Delta, reveal the spatiotemporal heterogeneity and multi-scale drivers of regional PM pollution during two intensive ten-day campaigns capturing peak pollution scenarios (winter: 17–26 January 2019; summer: 21–30 August 2019). Results show stark seasonal differences: winter PM₁ and PM_2.5 averages were 2.6- and 2.7-fold higher (p < 0.0001) than summer. Diurnal patterns were bimodal in winter and unimodal (single valley) in summer. Vertically consistent PM₁ and PM_2.5 distributions featured sharp morning (08:00) concentration increases within specific layers (winter: 250–325 m; summer: 350–425 m). Analysis demonstrates multi-scale coupling of synoptic systems, boundary layer processes, and vertical wind structure governing pollution. Key mechanisms include a winter “Transport-Accumulation-Reactivation” cycle driven by cold air, and summer typhoon circulation influences. We identify hygroscopic growth triggered by inversion-high humidity coupling and sea-breeze-driven secondary aerosol formation. Leveraging UAV-based vertical profiling over Hangzhou, this study pioneers a three-dimensional dissection of layer-coupled PM dynamics in the Yangtze River Delta, offering a scalable paradigm for aerial–ground networks to achieve precision stratified control strategies in megacities. Full article

In disaster relief operations, integrating disaster reconnaissance, material delivery, and effect evaluation into a temporal task chain can significantly reduce emergency response cycles and improve rescue efficiency. However, since multiple types of heterogeneous UAVs need to be coordinated during the rescue temporal task chains assignment process, this places higher demands on the real-time dynamic decision-making and system fault tolerance of its task assignment algorithm. This study addresses the sequential dependencies among disaster reconnaissance, material delivery, and effect evaluation stages. A task allocation model for heterogeneous UAV swarm targeting temporal task chains is formulated, with objectives to minimize task completion time and energy consumption. A dynamic coalition formation algorithm based on temporary leader election and multi-round negotiation mechanisms is proposed to enhance continuous decision-making capabilities in complex disaster environments. A simulation scenario involving twenty heterogeneous UAVs and seven temporal rescue task chains is constructed. The results show that the proposed algorithm reduces average task completion time by 15.2–23.7% and average fuel consumption by 18.3–26.4% compared with cooperative network protocols and distributed auctions, with up to a 43% reduction in fuel consumption fluctuations. Full article

The vertical distribution of volatile organic compounds (VOCs) within the planetary boundary layer (PBL) is critical for understanding ozone (O₃) formation, yet knowledge remains limited in complex urban environments. In this study, vertical measurements of 117 VOC species were conducted using an unmanned aerial vehicle (UAV) equipped with a VOC multi-channel sampling system, up to a height of 500 m in Shenzhen, China. Results showed that total VOC (TVOC) concentrations decreased with altitude in the morning, reflecting the influence of surface-level local emissions, but increased with height at midday, likely driven by regional transport and potentially stronger photochemical processes. Source apportionment revealed substantial industrial emissions across all altitudes, vehicular emissions concentrated near the surface, and biomass burning primarily impacting higher layers. Clear evidence of enhanced secondary formation of oxygenated VOCs (OVOCs) was observed along the vertical gradient, particularly at midday, indicating intensified photochemical processes at higher altitudes. These findings underscore the importance of considering vertical heterogeneity in VOC distributions when modeling O₃ formation or developing measures to reduce emissions at different altitudes, and also demonstrate the potential of UAV platforms to provide high-resolution atmospheric chemical data in complex urban environments. Full article

This work formulates and solves the actual problem of studying the logistics of unmanned aerial vehicle (UAV) operations in facility security planning. The study is related to security tasks, including perimeter control, infrastructure condition monitoring, prevention of unauthorized access, and analysis of potential threats. Thus, the topic of the proposed publication is relevant as it examines the sequence of logistical actions in the large-scale application of a swarm of drones for facility protection. The purpose of the research is to create a set of mathematical and simulation models that can be used to analyze the capabilities of a drone swarm when organizing security measures. The article analyzes modern problems of using a drone swarm: formation of the swarm, assessment of its potential capabilities, organization of patrols, development of monitoring scenarios, planning of drone routes and assessment of the effectiveness of the security system. Special attention is paid to the possibilities of wave patrols to provide continuous surveillance of the object. In order to form a drone swarm and possibly divide it into groups sent to different surveillance zones, the necessary UAV capacity to effectively perform security tasks is assessed. Possible security scenarios using drone waves are developed as follows: single patrolling with limited resources; two-wave patrolling; and multi-stage patrolling for complete coverage of the protected area with the required number of UAVs. To select priority monitoring areas, the functional potential of drones and current risks are taken into account. An optimization model of rational distribution of drones into groups to ensure effective control of the protected area is created. Possible variants of drone group formation are analyzed as follows: allocation of one priority surveillance zone, formation of a set of key zones, or even distribution of swarm resources along the entire perimeter. Possible scenarios for dividing the drone swarm in flight are developed as follows: dividing the swarm into groups at the launch stage, dividing the swarm at a given navigation point on the route, and repeatedly dividing the swarm at different patrol points. An original algorithm for the formation of drone flight routes for object surveillance based on the simulation modeling of the movement of virtual objects simulating drones has been developed. An agent-based model on the AnyLogic platform was created to study the logistics of security operations. The scientific novelty of the study is related to the actual task of forming possible strategies for using a swarm of drones to provide integrated security of objects, which contributes to improving the efficiency of security and monitoring systems. The results of the study can be used by specialists in security, logistics, infrastructure monitoring and other areas related to the use of drone swarms for effective control and protection of facilities. Full article

Unmanned aerial vehicles (UAVs) are widely used in surveillance and combat for their efficiency and autonomy, whilst complex, dynamic environments challenge the modeling of inter-agent relations and information transmission. This research proposes a novel UAV tactical choice-making algorithm utilizing graph neural networks to tackle these challenges. The proposed algorithm employs a graph neural network to process the observed state information, the convolved output of which is then fed into a reconstructed critic network incorporating a Laplacian convolution kernel. This research first enhances the accuracy of obtaining unstable state information in hostile environments. The proposed algorithm uses this information to train a more precise critic network. In turn, this improved critic network guides the actor network to make decisions that better meet the needs of the battlefield. Coupled with a policy transfer mechanism, this architecture significantly enhances the decision-making efficiency and environmental adaptability within the multi-agent system. Results from the experiments show that the average effectiveness of the proposed algorithm across the six planned scenarios is 97.4%, surpassing the baseline by 23.4%. In addition, the integration of transfer learning makes the network convergence speed three times faster than that of the baseline algorithm. This algorithm effectively improves the information transmission efficiency between the environment and the UAV and provides strong support for UAV formation combat. Full article

The present paper deals with an inhabited, creeping mountainous landmass with profound surface deformation that affects the local community. The scope of the paper is to gather surficial and subsurface information in order to understand the parameters of this creeping mass, which is usually affected by several parameters, such as its geometry, subsurface water, and shear zone. Therefore, a combined aerial and surface investigation has been conducted. The aerial investigation involves UAV’s LiDAR acquisition for the terrain model and a comparison of historical aerial photographs for land use changes. The multi-technique surface investigation included resistivity (ERT) and seismic (SRT, MASW) measurements and density determination of geological formations. This combination of methods proved to be fruitful since several aspects of the landslide were clarified, such as water flow paths, the internal geological structure of the creeping mass, and its geometrical extent. The depth of the shear zone of the creeping mass is delineated at the first five to ten meters from the surface, especially from the difference in diachronic resistivity change. Full article

This paper resolves the predefined-time control problem for multi-agent systems under predefined performance metrics and state constraints, addressing critical limitations of traditional methods—notably their inability to enforce strict user-specified deadlines for mission-critical operations, coupled with difficulties in simultaneously guaranteeing transient performance bounds and state constraints while suffering prohibitive stability proof complexity. To overcome these challenges, we propose a predefined performance control methodology that integrates Barrier Lyapunov Functions command-filtered backstepping. The framework rigorously ensures exact convergence within user-defined time independent of initial conditions while enforcing strict state constraints through time-varying BLF boundaries and further delivers quantifiable performance such as overshoot below 5% and convergence within 10 s. By eliminating high-order derivative continuity proofs via command-filter design, stability analysis complexity is reduced by 40% versus conventional backstepping. Stability proofs and dual-case simulations (UAV formation/smart grid) demonstrate over 95% tracking accuracy under disturbances and constraints, validating broad applicability in safety-critical multi-agent systems. Full article

In time-sensitive aerial missions such as urban surveillance, emergency response, and adversarial airspace operations, achieving rapid and reliable formation control of multi-UAV systems is crucial. This paper addresses the challenge of ensuring robust and efficient formation control under stringent time constraints. The proposed singularity-free prescribed-time formation (PTF) control scheme guarantees task completion within a user-defined time, independent of initial conditions and control parameters. Unlike existing scaling-based prescribed-time methods plagued by unbounded gains and fixed-time strategies with non-tunable convergence bounds, the proposed scheme uses fixed-time stability theory and systematic parameter tuning to avoid singularity issues while ensuring robustness and predictable convergence. The method also accommodates directed communication topologies and unknown external disturbances, allowing follower UAVs to track a dynamic leader and maintain the desired geometric formation. Finally, some simulation results demonstrate the effectiveness of the proposed control strategy, showcasing its superiority over existing methods and validating its potential for practical applications. Full article

UAV formation navigation in complex environments such as narrow tunnels faces multiple challenges, including obstacle avoidance, formation maintenance, and communication constraints. This paper proposes a cooperative obstacle avoidance strategy for UAV formation based on adaptive event-triggered impulse control, achieving efficient navigation under limited resources. The strategy comprises four key modules: an adaptive event-triggering mechanism, optical flow-based obstacle detection, leader–follower formation structure, and dynamic communication topology management. The adaptive event-triggering mechanism dynamically adjusts triggering thresholds, ensuring control accuracy while reducing control update frequency; the enhanced optical flow perception model improves obstacle recognition ability through a sector-based approach, incorporating tunnel-specific avoidance strategies; the leader–follower formation structure employs dynamic weight allocation to balance obstacle avoidance needs with formation maintenance; and communication topology optimization enhances system robustness under limited communication conditions. Simulation experiments were conducted in an arc-shaped tunnel environment with 15 randomly distributed obstacles, and the results demonstrate that the proposed method significantly improves collision rates, formation errors, and communication overhead compared to traditional methods. Lyapunov stability analysis proves the convergence of the proposed control strategy. This research provides new theoretical and practical references for multi-UAV cooperative control in complex narrow environments. Full article