  We present a multi-scale forward search algorithm for distributed agents to
solve single-query shortest path planning problems. Each agent first builds a
representation of its own search space of the common environment as a
multi-resolution graph, it communicates with the other agents the result of its
local search, and it uses received information from other agents to refine its
own graph and update the local inconsistency conditions. As a result, all
agents attain a common subgraph that includes a provably optimal path in the
most informative graph available among all agents, if one exists, without
necessarily communicating the entire graph. We prove the completeness and
optimality of the proposed algorithm, and present numerical results supporting
the advantages of the proposed approach.
