Analyzing the Impact of Information Asymmetry on Strategy Adaptation in Swarm Robotics: A Game-Theoretic Approach

In dynamic environments characterized by information asymmetry, swarm robots encounter significant challenges in efficiently collaborating to complete tasks. This study investigates the effects of factors such as resource information, shared costs, transmission efficiency, and strategy-switching probabilities arising from uneven information sharing among robots from the perspective of information disparity. A payoff matrix is developed to model the selection between search and exploration strategies under conditions of information asymmetry. Utilizing evolutionary game theory and replicator dynamics, the study analyzes how robots adapt their strategies in response to variations in resource information and shared costs. The findings reveal that the system ultimately evolves toward one of two dominant strategies: search or exploration. Numerical simulations demonstrate that information disparity, shared costs, transmission efficiency, and strategy-switching probabilities collectively drive the transition of robots from a search strategy to an exploration strategy, enabling them to acquire unknown environmental information more effectively and expedite task completion. The results suggest that in environments with balanced information, the system predominantly favors the search strategy to optimize resource utilization. Conversely, in environments with pronounced information asymmetry, the system is more inclined to adopt the exploration strategy, enhancing adaptability to environmental changes and accelerating task completion.