  Purpose of Review
  Planning collision-free paths for multiple robots is important for real-world
multi-robot systems and has been studied as an optimization problem on graphs,
called Multi-Agent Path Finding (MAPF). This review surveys different
categories of classic and state-of-the-art MAPF algorithms and different
research attempts to tackle the challenges of generalizing MAPF techniques to
real-world scenarios.
  Recent Findings
  Solving MAPF problems optimally is computationally challenging. Recent
advances have resulted in MAPF algorithms that can compute collision-free paths
for hundreds of robots and thousands of navigation tasks in seconds of runtime.
Many variants of MAPF have been formalized to adapt MAPF techniques to
different real-world requirements, such as considerations of robot kinematics,
online optimization for real-time systems, and the integration of task
assignment and path planning.
  Summary
  Algorithmic techniques for MAPF problems have addressed important aspects of
several multi-robot applications, including automated warehouse fulfillment and
sortation, automated train scheduling, and navigation of non-holonomic robots
and quadcopters. This showcases their potential for real-world applications of
large-scale multi-robot systems.
