Multi-agent path planning (MAPP) is the problem of planning collision-free trajectories from start to goal locations for a team of agents. This work explores a relatively unexplored setting of MAPP where streams of agents have to go through the starts and goals with high throughput. We tackle this problem by formulating a new variant of MAPP called periodic MAPP in which the timing of agent appearances is periodic. The objective with periodic MAPP is to find a periodic plan, a set of collision-free trajectories that the agent streams can use repeatedly over periods, with periods that are as small as possible. To meet this objective, we propose a solution method that is based on constraint relaxation and optimization. We show that the periodic plans once found can be used for a more practical case in which agents in a stream can appear at random times. We confirm the effectiveness of our method compared with baseline methods in terms of throughput in several scenarios that abstract autonomous intersection management tasks.

翻译：多智能体路径规划（MAPP）是为一组智能体规划从起点到目标位置的无碰撞轨迹的问题。本研究探索了MAPP中一个相对未被充分研究的场景，即智能体流需要以高吞吐量通过起点和终点。我们通过提出MAPP的新变体——周期性MAPP来解决该问题，其中智能体出现的时间具有周期性。周期性MAPP的目标是寻找周期性规划，即一组智能体流可在周期内重复使用的无碰撞轨迹集合，且周期尽可能短。为实现该目标，我们提出了一种基于约束松弛与优化的求解方法。研究表明，一旦求得周期性规划，可将其应用于更实际的情况，即智能体流中个体可在随机时间出现。通过模拟自主交叉口管理任务的多个场景，我们以吞吐量为指标，验证了所提方法相较基线方法的有效性。