Network Game Study of Swarm Robots in Meta-Structures

This paper investigates the interaction structure and decision-making process of swarm robots. It analyses the impact of communication links on the robots’ gains when they interact in local meta-structures and resolves the decision dynamic mechanisms of swarm robot systems. The collaboration efficiency of swarm robots is highly dependent on their local interactions. Complex networks are leveraged to portray the interaction structure between individuals, while classical games are employed to depict the decision-making paradigm of individuals. The study focuses on two typical local network meta-structures of distributed swarm robots: the cross-shaped grid world and the nine-lattice grid world. Within these meta-structures, the robots are categorized as resource-providing or resource-consuming. The information interaction and decision-making process between the central robot and its neighboring robots are mapped into resource-allocating group-interacting network games. The payoff of the central robot within the meta-structure is determined by the strategies of its neighbors and its neighbors’ neighbors, establishing the interaction strategies between robots, namely the yield game strategy and the price game strategy. A game model is constructed for meta-structures consisting of various classes and numbers of robots, which is subsequently solved. The results demonstrate that robots in network structures with simpler configurations and fewer communication links achieve higher gains in the group interaction network game. This economic perspective supports the notion that simpler network structures are more efficient and preferable to select.