Hierarchical probabilistic graphical models for multi-UAV cooperative pursuit in dynamic environments

Pursuing a non-cooperative moving target through multiple unmanned aerial vehicles (multi-UAV) is still challenging, especially in complex environments with dynamic obstacles. This article proposes a self-organizing multi-UAV cooperative pursuit approach based on hierarchical probabilistic graphical models. Firstly, we establish the UAV double-integrator kinematic models and provide a mathematical description of the pursuit task. Subsequently, a task-specific hierarchical probabilistic graphical model is designed for autonomous decision-making of UAVs. In the model, local perception states and individual motion capabilities are integrated to estimate the probability distribution parameters for each node. To enhance pursuit efficiency, the pursuit task is segmented into multiple phases and a "dispersed encirclement" strategy is devised inspired by wolf pack hunting behavior. Finally, numerical simulations and real-world experiments are conducted to validate the scalability, adaptability, and robustness of the proposed approach.