用主动推理建模无人机群，自适应调整飞行路径。
Active Inference-Driven World Modeling for Adaptive UAV Swarm Trajectory Design
- 基于主动推理构建分层世界模型，实现分布式任务分配与路径规划。
- 仿真中收敛更快、更稳定，导航更安全，优于Q-learning方法。
- 适合智能无人机集群控制研究者，具认知合理性与可扩展性。
本文提出一种基于主动推理的无人机群自主轨迹设计框架。该方法融合概率推理与自学习能力，实现分布式任务分配、路径排序与运动规划。利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，捕捉任务、路径和运动层面的群体行为。在线运行时，无人机通过最小化当前信念与模型预测状态之间的差异来推断动作，从而在动态环境中实现自适应响应。仿真结果表明，该方法相比Q-learning具有更快的收敛速度、更高的稳定性以及更安全的导航表现，验证了框架在智能无人机群控制中的可扩展性与认知基础。
原文摘要 · Abstract (English)
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.
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