  Effective multi-robot teams require the ability to move to goals in complex
environments in order to address real-world applications such as search and
rescue. Multi-robot teams should be able to operate in a completely
decentralized manner, with individual robot team members being capable of
acting without explicit communication between neighbors. In this paper, we
propose a novel game theoretic model that enables decentralized and
communication-free navigation to a goal position. Robots each play their own
distributed game by estimating the behavior of their local teammates in order
to identify behaviors that move them in the direction of the goal, while also
avoiding obstacles and maintaining team cohesion without collisions. We prove
theoretically that generated actions approach a Nash equilibrium, which also
corresponds to an optimal strategy identified for each robot. We show through
extensive simulations that our approach enables decentralized and
communication-free navigation by a multi-robot system to a goal position, and
is able to avoid obstacles and collisions, maintain connectivity, and respond
robustly to sensor noise.
