This paper proposes an Active Inference-based framework for autonomous trajectory design in UAV swarms. The method integrates probabilistic reasoning and self-learning to enable distributed mission allocation, route ordering, and motion planning. Expert trajectories generated using a Genetic Algorithm with Repulsion Forces (GA-RF) are employed to train a hierarchical World Model capturing swarm behavior across mission, route, and motion levels. During online operation, UAVs infer actions by minimizing divergence between current beliefs and model-predicted states, enabling adaptive responses to dynamic environments. Simulation results show faster convergence, higher stability, and safer navigation than Q-Learning, demonstrating the scalability and cognitive grounding of the proposed framework for intelligent UAV swarm control.

翻译：本文提出一种基于主动推理的自主轨迹设计框架，用于无人机集群控制。该方法融合概率推理与自学习机制，实现分布式任务分配、航路排序与运动规划。通过采用带排斥力的遗传算法生成专家轨迹，训练一个分层世界模型，该模型能够捕捉任务层、航路层与运动层的集群行为。在线运行期间，无人机通过最小化当前信念与模型预测状态之间的散度来推断行动，从而实现对动态环境的自适应响应。仿真结果表明，与Q学习相比，该方法具有更快的收敛速度、更高的稳定性和更安全的导航性能，证明了所提框架在智能无人机集群控制中的可扩展性与认知基础。