  We present a hierarchical framework for motion planning of a large collection
of agents. The proposed framework starts from low level motion primitives over
a gridded workspace and provides a set of rules for constructing higher level
motion primitives. Our hierarchical approach is highly scalable and robust
making it an ideal tool for planning for multi-agent systems. Results are
demonstrated experimentally on a collection of quadrotors that must navigate a
cluttered environment while maintaining a formation.
