  The MAPF problem is the fundamental problem of planning paths for multiple
agents, where the key constraint is that the agents will be able to follow
these paths concurrently without colliding with each other. Applications of
MAPF include automated warehouses and autonomous vehicles. Research on MAPF has
been flourishing in the past couple of years. Different MAPF research papers
make different assumptions, e.g., whether agents can traverse the same road at
the same time, and have different objective functions, e.g., minimize makespan
or sum of agents' actions costs. These assumptions and objectives are sometimes
implicitly assumed or described informally. This makes it difficult to
establish appropriate baselines for comparison in research papers, as well as
making it difficult for practitioners to find the papers relevant to their
concrete application. This paper aims to fill this gap and support researchers
and practitioners by providing a unifying terminology for describing common
MAPF assumptions and objectives. In addition, we also provide pointers to two
MAPF benchmarks. In particular, we introduce a new grid-based benchmark for
MAPF, and demonstrate experimentally that it poses a challenge to contemporary
MAPF algorithms.
