Cooperative Encirclement and Obstacle Avoidance of Fixed-Wing UAVs via MADDPG with Curriculum Learning

Multi-UAV cooperative encirclement tasks have attracted considerable attention in areas such as military defense and target interception. Fixed-wing UAVs face substantial challenges due to intrinsic dynamic limits, including their minimum velocity and turning radius, particularly when engaging evasive target and navigating in obstacle environments. This paper presents a hybrid deep reinforcement learning approach, in which a cooperative task environment is developed for fixed-wing UAVs that jointly integrates encirclement and obstacle avoidance. A composite MADDPG framework enhanced with curriculum learning is designed, employing progressive task staging and reward optimization to accelerate convergence and improve policy stability. Simulation results demonstrate that the proposed method achieves single-step encirclement success rates exceeding 80% in complex environments, while maintaining 10-step success rates around 70%, thereby strengthening both encirclement capability and obstacle avoidance safety in fixed-wing UAV swarm. This study provides new insights into the intelligent cooperative control of fixed-wing UAVs in high-risk missions.