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In this paper, we consider a planning problem for a large-scale systemmodelled as a hierarchical finite state machine (HFSM) and develop a controlalgorithm for computing optimal plans between any two states. The controlalgorithm consists of two steps: a preprocessing step computing optimal exitcosts for each machine in the HFSM, with time complexity scaling linearly withthe number of machines, and a query step that rapidly computes optimal plans,truncating irrelevant parts of the HFSM using the optimal exit costs, with timecomplexity scaling near-linearly with the depth of the HFSM. The controlalgorithm is reconfigurable in the sense that a change in the HFSM isefficiently handled, updating only needed parts in the preprocessing step toaccount for the change, with time complexity linear in the depth of the HFSM.We validate our algorithm on a robotic application, comparing it withDijkstra's algorithm and Contraction Hierarchies. Our algorithm outperformsboth.

Efficient and Reconfigurable Optimal Planning in Large-Scale Systems Using Hierarchical Finite State Machines