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.
@article{arxiv.2601.12939,
title = {Active Inference-Driven World Modeling for Adaptive UAV Swarm Trajectory Design},
author = {Kaleem Arshid and Ali Krayani and Lucio Marcenaro and David Martin Gomez and Carlo Regazzoni},
journal= {arXiv preprint arXiv:2601.12939},
year = {2026}
}
Comments
This paper has been accepted for presentation at the 2026 IEEE International Conference on Acoustics, Speech, and Signal Processing (IEEE ICASSP 2026) Workshop: 'Multi-Modal Signal Processing and AI for Communications and Sensing in 6G and Beyond (MuSiC-6GB)'