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This paper introduces an Active Inference framework for decentralized UAV swarm trajectory design, aiming to improve adaptability and safety in dynamic environments. A hierarchical World Model is trained on expert trajectories generated by a Genetic Algorithm with Repulsion Forces (GA-RF) to represent swarm behavior at different levels of abstraction. The UAVs then use active inference to select actions that minimize the divergence between their beliefs and the World Model's predictions.
Active Inference offers a scalable and cognitively grounded alternative to reinforcement learning for UAV swarm control, showing faster convergence and improved stability in dynamic environments.
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.
