Encoding Reusable Multi-Robot Planning Strategies as Abstract Hypergraphs
This work addresses the challenge of efficient online planning for multi-robot systems, though it appears incremental as it extends existing techniques to a new context.
The paper tackles the exponential complexity of Multi-Robot Task Planning (MR-TP) by combining a hypergraph-based framework (DaSH) with learning-by-abstraction to extend strategy-learning from single-robot to multi-robot planning, aiming to accelerate planning over a system's lifetime.
Multi-Robot Task Planning (MR-TP) is the search for a discrete-action plan a team of robots should take to complete a task. The complexity of such problems scales exponentially with the number of robots and task complexity, making them challenging for online solution. To accelerate MR-TP over a system's lifetime, this work looks at combining two recent advances: (i) Decomposable State Space Hypergraph (DaSH), a novel hypergraph-based framework to efficiently model and solve MR-TP problems; and \mbox{(ii) learning-by-abstraction,} a technique that enables automatic extraction of generalizable planning strategies from individual planning experiences for later reuse. Specifically, we wish to extend this strategy-learning technique, originally designed for single-robot planning, to benefit multi-robot planning using hypergraph-based MR-TP.