GRAPH NEURAL NETWORK (GNN) BASED TOPOLOGY CONTROL IN SELF-ORGANIZING WHEELED ROBOT SWARMS

This study examines a Graph Neural Network (GNN)-based approach for controlling communication topology and coordinated motion in self-organizing wheeled robot swarms under dynamically changing spatial and network conditions. The proposed framework represents robots as graph nodes and wireless communication links as graph edges, enabling the neural model to evaluate connectivity patterns and generate topology-aware motion decisions that preserve network coherence during collective navigation.