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 estimate 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.

翻译：多机器人团队应该能够以完全分散的方式运作,使机器人团队的个体成员能够在没有邻居之间明确沟通的情况下采取行动。在本文中,我们提出了一个新的游戏理论模型,使分散和无通信导航能够达到目标位置。机器人估算了本地团队伙伴的行为,以便确定他们朝着目标方向前进的行为,同时避免障碍并保持团队凝聚力,避免碰撞。我们从理论上证明,产生的行动接近纳什平衡,这也符合为每个机器人确定的最佳战略。我们通过广泛的模拟显示,我们的方法能够通过多机器人系统实现分散和无通信的导航,从而达到目标位置,并且能够避免障碍和碰撞,保持连通性,并对传感器的噪音做出有力反应。