  In this paper, we develop a control framework for the coordination of
multiple robots as they navigate through crowded environments. Our framework
comprises of a local model predictive control (MPC) for each robot and a social
long short-term memory model that forecasts pedestrians' trajectories. We
formulate the local MPC formulation for each individual robot that includes
both individual and shared objectives, in which the latter encourages the
emergence of coordination among robots. Next, we consider the multi-robot
navigation and human-robot interaction, respectively, as a potential game and a
two-player game, then employ an iterative best response approach to solve the
resulting optimization problems in a centralized and distributed fashion.
Finally, we demonstrate the effectiveness of coordination among robots in
simulated crowd navigation.
