Active Inference-Driven World Modeling for Adaptive UAV Swarm Trajectory Design

• URL: http://arxiv.org/abs/2601.12939v1
• Date: Mon, 19 Jan 2026 10:47:26 GMT
• Title: Active Inference-Driven World Modeling for Adaptive UAV Swarm Trajectory Design
• Authors: Kaleem Arshid, Ali Krayani, Lucio Marcenaro, David Martin Gomez, Carlo Regazzoni,
• Abstract summary: This paper proposes an Active Inference-based framework for autonomous trajectory design in UAV swarms.<n>The method integrates probabilistic reasoning and self-learning to enable distributed mission allocation, route ordering, and motion planning.
• Score: 5.238520207250123
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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