Active Inference-Driven World Modeling for Adaptive UAV Swarm Trajectory Design
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| Acceso en línea: | |
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| _version_ | 1866909994586210304 |
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| author | Arshid, Kaleem Krayani, Ali Marcenaro, Lucio Gomez, David Martin Regazzoni, Carlo |
| author_facet | Arshid, Kaleem Krayani, Ali Marcenaro, Lucio Gomez, David Martin Regazzoni, Carlo |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_12939 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Active Inference-Driven World Modeling for Adaptive UAV Swarm Trajectory Design Arshid, Kaleem Krayani, Ali Marcenaro, Lucio Gomez, David Martin Regazzoni, Carlo Robotics Artificial Intelligence Signal Processing 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. |
| title | Active Inference-Driven World Modeling for Adaptive UAV Swarm Trajectory Design |
| topic | Robotics Artificial Intelligence Signal Processing |
| url | https://arxiv.org/abs/2601.12939 |