A framework leverages concepts from game theory to design control strategies for multiple robots operating collaboratively or competitively. This approach considers each robot as an agent within a game, where the agent’s actions influence the outcomes and payoffs for all other agents involved. For example, in a cooperative task like collaborative object transport, each robot’s control inputs are determined by considering the actions of its teammates and the collective objective, leading to a coordinated and efficient solution.
This control methodology provides a structured approach to handling complex interactions and decision-making in multi-robot systems. Its advantages include the ability to handle uncertainty, adapt to changing environments, and provide guarantees on system performance. Historically, traditional control methods struggled with the inherent complexity of coordinating multiple agents, especially when dealing with conflicting objectives or limited communication. The advent of this framework offered a more principled and robust solution, leading to improved efficiency and safety in robotic applications. This method’s capacity to ensure optimal behavior and achieve stability across interconnected systems has solidified its critical role.
