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Agents can individually devise plans and coordinate to achieve common goals. Methods exist to factor planning problems into separate tasks and distribute the plan synthesis process, while reducing the overall planning complexity. Merging distributedly generated plans becomes computationally costly when task plans are tightly coupled, and conflicts arise due to dependencies between plan actions. New plan merging algorithms allow factoring and solving large problems with a growing number of agents and tasks, but are yet to be demonstrated in physical real-world systems. This Demo presents an architecture that deploys plan merging algorithms in a physical multi-robot setting and emulates a First Response Domain.

Scalable Multiple Robot Task Planning with Plan Merging and Conflict Resolution