Dynamic Real-time Multimodal Routing with Hierarchical Hybrid Planning
This addresses routing challenges for autonomous agents like drones in dynamic environments, though it appears incremental as a novel method for a specific bottleneck.
The paper tackles the Dynamic Real-time Multimodal Routing (DREAMR) problem for autonomous agents planning routes under uncertainty using multiple transportation modes, and introduces a hierarchical hybrid planning framework that significantly outperforms a receding horizon control baseline in elapsed time and energy expended.
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.