Resource Allocation and Pricing Strategies for Swarm Robots in Complex Networks ：A Game Theory Perspective

To optimize the resource allocation problem in swarm robot systems under complex dynamic environments, this paper proposes a pricing strategy method based on complex networks and game theory. First, cross-shaped and nine-square grid network models are constructed to describe the interaction structure among robots. Next, differentiated and uniform pricing strategies are analyzed by establishing leader-follower models based on Stackelberg games, solved using Python for simulation. Simulation experiments evaluate the performance of different pricing strategies under various network structures. The results show that uniform pricing strategies are suitable for simple networks with low resource competition, while differentiated pricing strategies perform better in complex networks with intense resource competition. A reasonable choice of pricing strategy can significantly enhance the overall efficiency of swarm robot systems, adapting to the needs of different dynamic environments.