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