  This paper addresses the increasing significance of UAVs (Unmanned Aerial
Vehicles) and the emergence of UAV swarms for collaborative operations in
various domains. However, the effectiveness of UAV swarms can be severely
compromised by jamming technology, necessitating robust antijamming strategies.
While existing methods such as frequency hopping and physical path planning
have been explored, there remains a gap in research on path planning for UAV
swarms when the jammer's location is unknown. To address this, a novel
approach, where UAV swarms leverage collective intelligence to predict jamming
areas, evade them, and efficiently reach target destinations, is proposed. This
approach utilizes Graph Convolutional Networks (GCN) to predict the location
and intensity of jamming areas based on information gathered from each UAV. A
multi-agent control algorithm is then employed to disperse the UAV swarm, avoid
jamming, and regroup upon reaching the target. Through simulations, the
effectiveness of the proposed method is demonstrated, showcasing accurate
prediction of jamming areas and successful evasion through obstacle avoidance
algorithms, ultimately achieving the mission objective. Proposed method offers
robustness, scalability, and computational efficiency, making it applicable
across various scenarios where UAV swarms operate in potentially hostile
environments.
