This study presents a novel cooperative bird-driving strategy utilizing unmanned aerial vehicles (UAV) swarms, specifically designed for airport environments, to mitigate the risks posed by bird interference with aircraft operations. Our approach introduces a target trajectory prediction framework that integrates Long Short-Term Memory (LSTM) networks with Kalman Filter algorithms (KF), improves the response speed of UAV swarms in bird-driving tasks, optimizes task allocation, and improves the accuracy and precision of trajectory prediction, making the entire bird-driving process more efficient and accurate. Within this framework, UAV swarms collaborate to drive birds that encroach upon designated protected areas, thereby optimizing bird-driving operations. We present a distributed collaborative bird-driving strategy to ensure effective coordination among UAV swarm members. Simulation experiments demonstrate that our strategy effectively drives dynamically changing targets, preventing them from remaining within the protected area. The proposed solution integrates dynamic target trajectory prediction using LSTM and Kalman Filter, task assignment optimization through the Hungarian algorithm, and 3D Dubins path planning. This innovative approach not only improves the operational efficiency of bird-driving in airport environments but also highlights the potential of UAV swarms to perform airborne missions in complex scenarios. Our work makes a significant contribution to the field of UAV swarm collaboration and provides practical insights for real-world applications.

To enhance the efficiency of locating dynamic missing persons in complex mountain terrain, this study introduces an innovative Slope Probability Search (SPS) algorithm based on a modified A* framework. The algorithm’s core is a dynamic global probability map, constructed by linking terrain slope to the behavioral tendencies of missing persons. This fundamentally shifts the unmanned aerial vehicle (UAV) search paradigm from conventional coverage patterns to intelligent, guided exploration. To ensure a realistic evaluation, we designed three representative dynamic models for the missing persons: Terrain Constrained, Path Following, and Random Walk. The SPS algorithm, through its unique heuristic function, achieves an optimal balance between exploiting high probability areas and exploring new regions to maximize search efficiency. Simulation experiments using real-world geographic data demonstrated that even under severe constraints of limited search duration and sensor range, the algorithm achieved a success rate of 88.9% achieving an average search time substantially lower than that of conventional methods. This research provides a solid theoretical basis and a practical algorithmic framework for developing next generation intelligent search and rescue systems.

Low altitude logistics and aerial transport increasingly rely on multirotor unmanned aerial vehicles (UAVs) carrying slung payloads, where cable flexibility and load swing can degrade safety and delivery accuracy. This paper studies payload trajectory tracking for a dual-quadrotor cooperative slung-load system, targeting accurate tracking with swing suppression under thrust, attitude, and cable-tension limits. First, a payload-layer dynamic model is derived from d’Alembert’s principle with geometric cable constraints, and explicit tension reconstruction formulas are provided to enable direct enforcement of tension bounds. Building on this model, a payload-layer DEA nominal tracking controller is designed by applying dynamic extension to the tension-scalar channels and enforcing output-level linear error dynamics. To ensure real-time feasibility, a convex quadratic-programming (QP) projection layer minimally corrects the nominal command to satisfy thrust saturation, attitude-cone constraints, and cable-tension bounds. Moreover, an adaptive tuning control layer updates the DEA feedback gain and the projection weighting matrix within preset constraint limits based on energy residual and constraint-activation information, improving robustness and reducing manual tuning. Input-to-state stability is established under bounded disturbances and constraint-activation switching via a composite Lyapunov analysis. ROS–PX4–Gazebo simulations show low tracking error, suppressed swing, and sustained tension-limit compliance, validating the fusion controller.