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

TL;DR

This paper introduces an active inference-based framework for autonomous UAV swarm trajectory planning, combining probabilistic reasoning and self-learning to enhance adaptability, stability, and safety in dynamic environments.

Contribution

It presents a novel hierarchical world model trained with expert trajectories and an active inference approach for distributed, adaptive UAV swarm control.

Findings

Faster convergence compared to Q-Learning

Higher stability in navigation tasks

Safer navigation in dynamic environments

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.