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.

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.

Typhoon-eye sensing places unmanned aerial vehicle (UAV) swarms in a setting where the wind field that carries the target signal also displaces aircraft, drains energy, weakens links, and increases failure risk. A rule that improves only routing or only motion can therefore move the swarm into another failure mode. This paper proposes a risk-driven cross-layer coordination scheme for such missions. A bounded risk index, computed from isolation, connectivity loss, and wind intensity, acts as a supervisory variable for multi-hop reachability maintenance, isolated-node recovery, and layered altitude adaptation. For evaluation, graph reachability is separated from useful data return through a degraded multi-hop aggregation model that includes distance loss, wind-dependent reliability, rain-induced packet loss, relay forwarding loss, and mothership collection capacity. The simulator combines a bounded Holland-type storm field, stochastic turbulence, nonlinear propulsion energy consumption, and wind-dependent structural failure. Against three literature-inspired baselines, two AI-inspired comparators, and six ablation variants, the method keeps a balanced profile across connectivity, isolation, wind exposure, data collection, and survival. In 30-run steady-state robustness tests under heavy-rain attenuation, the full strategy showed clear gains over routing-only and multi-agent reinforcement learning (MARL)-routing comparators in connectivity and isolation, but did not uniformly dominate topology reconstruction or the multi-agent deep deterministic policy gradient–artificial potential field (MADDPG-APF) recovery comparator. The results indicate that, in storm-dominated swarm sensing, resilience comes mainly from coordinating exposure reduction with topology stabilization, rather than from optimizing a single layer.