  The multi-agent path-finding (MAPF) problem has recently received a lot of
attention. However, it does not capture important characteristics of many
real-world domains, such as automated warehouses, where agents are constantly
engaged with new tasks. In this paper, we therefore study a lifelong version of
the MAPF problem, called the multi-agent pickup and delivery (MAPD) problem. In
the MAPD problem, agents have to attend to a stream of delivery tasks in an
online setting. One agent has to be assigned to each delivery task. This agent
has to first move to a given pickup location and then to a given delivery
location while avoiding collisions with other agents. We present two decoupled
MAPD algorithms, Token Passing (TP) and Token Passing with Task Swaps (TPTS).
Theoretically, we show that they solve all well-formed MAPD instances, a
realistic subclass of MAPD instances. Experimentally, we compare them against a
centralized strawman MAPD algorithm without this guarantee in a simulated
warehouse system. TP can easily be extended to a fully distributed MAPD
algorithm and is the best choice when real-time computation is of primary
concern since it remains efficient for MAPD instances with hundreds of agents
and tasks. TPTS requires limited communication among agents and balances well
between TP and the centralized MAPD algorithm.
