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.

翻译：本文提出了一种面向多机器人群体在拥挤环境中协同导航的控制框架。该框架包含每个机器人的局部模型预测控制（MPC）模块，以及用于预测行人轨迹的社交长短期记忆模型。我们为每个机器人构建了包含个体目标与共享目标的局部MPC公式，其中共享目标旨在促进机器人间的协调行为。随后，我们将多机器人导航问题视为势博弈，将人机交互视为两人博弈，并采用迭代最优响应方法以集中式和分布式方式求解相应的优化问题。最后，通过仿真人群导航实验验证了机器人间协调机制的有效性。