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This work presents a framework for multi-robot tour guidance in a partiallyknown environment with uncertainty, such as a museum. A simultaneous matchingand routing problem (SMRP) is formulated to match the humans with robot guidesaccording to their requested places of interest (POIs) and generate the routesfor the robots according to uncertain time estimation. A large neighborhoodsearch algorithm is developed to efficiently find sub-optimal low-costsolutions for the SMRP. The scalability and optimality of the multi-robotplanner are evaluated computationally. The largest case tested involves 50robots, 250 humans, and 50 POIs. A photo-realistic multi-robot simulation wasdeveloped to verify the tour guiding performance in an uncertain indoorenvironment.

Human-robot Matching and Routing for Multi-robot Tour Guiding under Time Uncertainty