Game-theoretic motion planners are a powerful tool for the control of interactive multi-agent robot systems. Indeed, contrary to predict-then-plan paradigms, game-theoretic planners do not ignore the interactive nature of the problem, and simultaneously predict the behaviour of other agents while considering change in one's policy. This, however, comes at the expense of computational complexity, especially as the number of agents considered grows. In fact, planning with more than a handful of agents can quickly become intractable, disqualifying game-theoretic planners as possible candidates for large scale planning. In this paper, we propose a planning algorithm enabling the use of game-theoretic planners in robot systems with a large number of agents. Our planner is based on the reality of locality of information and thus deploys local games with a selected subset of agents in a receding horizon fashion to plan collision avoiding trajectories. We propose five different principled schemes for selecting game participants and compare their collision avoidance performance. We observe that the use of Control Barrier Functions for priority ranking is a potent solution to the player selection problem for motion planning.

翻译：博弈论运动规划器是控制交互式多智能体机器人系统的强大工具。与"先预测后规划"范式不同，博弈论规划器不忽视问题的交互特性，在考虑自身策略变化的同时同步预测其他智能体的行为。然而，这种优势伴随着计算复杂度的代价，尤其是当智能体数量增加时。事实上，对超过少数智能体的规划会迅速变得难以处理，这使得博弈论规划器难以成为大规模规划的可选方案。本文提出一种规划算法，使博弈论规划器能够在包含大量智能体的机器人系统中应用。该算法基于信息局部化的现实原则，通过滚动时域方式选择部分智能体构建局部博弈，从而规划无碰撞轨迹。我们提出了五种基于原则的参与者选择方案，并比较了它们的避障性能。实验表明，采用控制障碍函数进行优先级排序是解决运动规划中参与者选择问题的有效方案。