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 can use multiple modes of transportation in a dynamic transit vehicle network. 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.
Dynamic Real-time Multimodal Routing with Hierarchical Hybrid Planning
2019 IEEE Intelligent Vehicles Symposium (IV) ; 2397-2404
2019-06-01
2223321 byte
Conference paper
Electronic Resource
English
DYNAMIC REAL-TIME MULTIMODAL ROUTING WITH HIERARCHICAL HYBRID PLANNING
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