Active Inference-Driven World Modeling for Adaptive UAV Swarm Trajectory Design
This addresses adaptive control for UAV swarms in dynamic environments, though it appears incremental as it builds on existing Active Inference and expert trajectory methods.
The paper tackles autonomous trajectory design for UAV swarms by proposing an Active Inference-based framework that integrates probabilistic reasoning and self-learning, with simulation results showing faster convergence, higher stability, and safer navigation compared to Q-Learning.
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.