  In this work, we present a multi-robot planning framework that leverages
guidance about the problem to efficiently search the planning space. This
guidance captures when coordination between robots is necessary, allowing us to
decompose the intractably large multi-robot search space while limiting risk of
inter-robot conflicts by composing relevant robot groups together while
planning. Our framework additionally supports planning with kinodynamic
constraints through our conflict resolution structure. This structure also
improves the scalability of our approach by eliminating unnecessary work during
the construction of motion solutions. We also provide an application of this
framework to multiple mobile robot motion planning in congested environments
using topological guidance. Our previous work has explored using topological
guidance, which utilizes information about the robots' environment, in these
multi-robot settings where a high degree of coordination is required of the
full robot group. In real-world scenarios, this high level of coordination is
not always necessary and results in excessive computational overhead. Here, we
leverage our novel framework to achieve a significant improvement in
scalability and show that our method efficiently finds paths for robot teams up
to an order of magnitude larger than existing state-of-the-art methods in
congested settings with narrow passages in the environment.
