  Real-time planning for a combined problem of target assignment and path
planning for multiple agents, also known as the unlabeled version of
Multi-Agent Path Finding (MAPF), is crucial for high-level coordination in
multi-agent systems, e.g., pattern formation by robot swarms. This paper
studies two aspects of unlabeled-MAPF: (1) offline scenario: solving large
instances by centralized approaches with small computation time, and (2) online
scenario: executing unlabeled-MAPF despite timing uncertainties of real robots.
  For this purpose, we propose TSWAP, a novel sub-optimal complete algorithm,
which takes an arbitrary initial target assignment then repeats one-timestep
path planning with target swapping. TSWAP can adapt to both offline and online
scenarios. We empirically demonstrate that Offline TSWAP is highly scalable;
providing near-optimal solutions while reducing runtime by orders of magnitude
compared to existing approaches. In addition, we present the benefits of Online
TSWAP, such as delay tolerance, through real-robot demos.
