Drones

Addressing prevalent challenges in current cooperative task assignment methods for cross-domain unmanned swarm, such as the disconnection between decision-making and execution processes, and the inadequate incorporation of platform kinematic constraints, this study introduces an integrated decision-control cooperative task assignment approach based on a bi-level optimization framework. The proposed framework formulates a bi-level programming model that tightly couples upper-level task assignment with lower-level optimal control. The upper-level model aims to minimize the maximum task completion time by optimizing the assignment and visitation sequences of diverse target types across heterogeneous unmanned platforms. The lower-level model, given the task sequences from the upper level, addresses a minimum-time optimal control problem based on a comprehensive nonlinear kinematic model. This approach enables precise computation of task execution times, which are subsequently fed back to the decision-making layer, thereby establishing a closed-loop optimization mechanism. To solve this complex model efficiently, the lower-level employs differential flatness transformation to eliminate trigonometric functions in the kinematic equations and discretizes the continuous-time optimal control problem into a nonlinear programming problem via the Radau pseudospectral method. For the upper-level combinatorial optimization, an improved genetic algorithm is developed, integrating hybrid encoding, dual-archive elitism preservation, adaptive crossover and mutation strategies, and periodic local search. Simulation results demonstrate that, compared with traditional Euclidean-distance-based assignment methods, the proposed approach generates kinematically feasible and smooth trajectories while thoroughly accounting for the kinematic constraints of heterogeneous platforms, thereby demonstrating its effectiveness and superiority in improving the comprehensive mission performance of cross-domain unmanned swarms.

To meet the energy-saving requirements of user equipment (UE) operating in Radio Resource Control idle/inactive states (RRC_IDLE/RRC_INACTIVE) in the 3rd-Generation Partnership Project (3GPP) 5G-Advanced (5G-A) networks, the New Radio (NR) downlink paging procedure relies on periodic monitoring and frequent synchronization signal block (SSB) measurements, which wastes energy when no paging arrivals occur. Meanwhile, heterogeneous Quality of Service (QoS) constraints make it difficult for fixed-parameter Idle Discontinuous Reception and Paging Early Indication mechanisms (IDRX/PEI) to balance energy, delay, and reliability. This paper develops a UAV-assisted 5G-A paging control framework that maps services into multiple QoS classes and models QoS violation risk and system energy consumption under unified accounting, including UE monitoring/reception energy and unmanned aerial vehicle (UAV) forwarding energy. We then propose a QoS-aware risk-driven paging strategy: an offline Long Short-Term Memory (LSTM) predictor is trained to estimate the time-to-next-arrival (TTNA) of paging events and produce a bounded urgency/risk signal to initialize class-dependent thresholds, while online triggering and QoS-feedback-based threshold adaptation regulate the empirical violation rate toward target constraints under varying loads, enabling a controllable energy–delay trade-off. A simulation-based evaluation is conducted to compare the proposed method with representative baselines (Enhanced Paging Monitoring (EPM), Split Paging Occasion (SPOP), and Predicted Paging Early Indication (PPEI)) and to examine the impact of SSB overhead and UAV relaying on the energy–delay–reliability trade-offs.

To address the escalating security challenges posed by unauthorized Unmanned Aerial Vehicles, this paper presents a Sim2real physics-informed audio–visual fusion simulation platform designed to enhance Counter-Unmanned Aerial Vehicle detection and tracking performance. The proposed method integrates two complementary sensing pipelines: a physics-based acoustic localization system utilizing Time Difference of Arrival principles and a deep learning-driven visual detection framework. To ensure robust surveillance against non-cooperative targets, these pipelines are not only fused through strict spatiotemporal synchronization but also mutually reinforce each other—acoustic data guides visual attention in low-visibility scenarios typical of adversarial intrusions, while visual detections refine acoustic parameter estimation. Building upon prior work in multi-modal perception, we extend the framework to dynamic environments characterized by configurable visual obstructions, including smoke and fog, which frequently compromise conventional optical anti-drone systems. Experiments demonstrate that the fusion system progressively adapts to degraded visual conditions, extending tracking continuity from approximately 50% coverage under vision-only operation to near-continuous target awareness, with a moderate trade-off in average angular precision when acoustic-only segments are included. Physical validation with quadrotor Unmanned Aerial Vehicles confirms the platform’s capability to bridge simulation-to-reality gaps. Our results highlight the system’s robustness against sensor degradation and its potential to accelerate the development of resilient multisensor Counter-Unmanned Aerial Vehicle systems while reducing dependency on costly field testing.