Hierarchical planning and control for multi-UAV cooperative encirclement in complex environments

In complex environments, multi-UAV cooperative encirclement tasks often face critical challenges such as low intruder trajectory prediction accuracy, excessive system energy consumption during task allocation, and insufficient obstacle avoidance capability under dynamic conditions. These issues severely limit the efficiency and reliability of encirclement operations. To address these problems, a two-stage cooperative encirclement framework consisting of a gathering phase and a guidance phase is proposed in this paper. In the gathering phase, an interpolation and error-correction polynomial fitting(IEC-PF) method is designed to achieve early interception and significantly improve the accuracy of intruder trajectory prediction. For the task allocation of capture points, an iterative convex optimization-based allocation strategy is developed to minimize total system energy consumption while strictly satisfying the final capture formation constraints. In the guidance phase, UAVs construct an optimal encircling formation based on the assigned capture points. Within the generated obstacle-free convex regions, a nonlinear optimization method is used to compute optimal global paths and formation configuration parameters. An affine formation control strategy based on stress matrices is further introduced to ensure formation stability and reconfiguration during path tracking. Additionally, a deep reinforcement learning(DRL)-based local obstacle avoidance mechanism is integrated to enhance the system's real-time responsiveness to dynamic obstacles. Simulation results demonstrate that the proposed framework substantially improves obstacle avoidance performance in complex environments and enables reliable multi-UAV cooperative capture of intruding targets, as well as guiding them to designated areas.