Periodic Multi-Agent Path Planning

TL;DR

This paper introduces a novel periodic multi-agent path planning framework that optimizes collision-free trajectories for high-throughput agent streams, applicable to autonomous intersection management.

Contribution

It formulates the periodic MAPP problem and proposes a constraint relaxation and optimization method to generate efficient, reusable collision-free plans.

Findings

Periodic plans improve throughput over baseline methods.

The approach is effective for autonomous intersection scenarios.

Plans are adaptable to random agent appearance times.

Abstract

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.