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We introduce the problem of Dynamic Real-time Multimodal Routing (DREAMR),which requires planning and executing routes under uncertainty for anautonomous agent. The agent has access to a time-varying transit vehiclenetwork in which it can use multiple modes of transportation. For instance, adrone can either fly or ride on terrain vehicles for segments of their routes.DREAMR is a difficult problem of sequential decision making under uncertaintywith both discrete and continuous variables. We design a novel hierarchicalhybrid planning framework to solve the DREAMR problem that exploits itsstructural decomposability. Our framework consists of a global open-loopplanning layer that invokes and monitors a local closed-loop execution layer.Additional abstractions allow efficient and seamless interleaving of planningand execution. We create a large-scale simulation for DREAMR problems, witheach scenario having hundreds of transportation routes and thousands ofconnection points. Our algorithmic framework significantly outperforms areceding horizon control baseline, in terms of elapsed time to reach thedestination and energy expended by the agent.

Dynamic Real-time Multimodal Routing with Hierarchical Hybrid Planning