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arXiv · 2601.12939

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

Kaleem Arshid·Ali Krayani·Lucio Marcenaro·David Martin Gomez·Carlo Regazzoni
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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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Kaleem Arshid, Ali Krayani, Lucio Marcenaro, David Martin Gomez, Carlo Regazzoni. 2026-01-19. Active Inference-Driven World Modeling for Adaptive UAV Swarm Trajectory Design. https://arxiv.org/abs/2601.12939

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