  At modern warehouses, mobile robots transport packages and drop them into
collection bins/chutes based on shipping destinations grouped by, e.g., the ZIP
code. System throughput, measured as the number of packages sorted per unit of
time, determines the efficiency of the warehouse. This research develops a
scalable, high-throughput multi-robot parcel sorting solution, decomposing the
task into two related processes, bin assignment and offline/online multi-robot
path planning, and optimizing both. Bin assignment matches collection bins with
package types to minimize traveling costs. Subsequently, robots are assigned to
pick up and drop packages into assigned bins. Multiple highly effective bin
assignment algorithms are proposed that can work with an arbitrary planning
algorithm. We propose a decentralized path planning routine using only local
information to route the robots over a carefully constructed directed road
network for multi-robot path planning. Our decentralized planner, provably
probabilistically deadlock-free, consistently delivers near-optimal results on
par with some top-performing centralized planners while significantly reducing
computation times by orders of magnitude. Extensive simulations show that our
overall framework delivers promising performances.
