# Dynamic Real-time Multimodal Routing with Hierarchical Hybrid Planning

## TL;DR

This paper presents a hierarchical hybrid planning framework for dynamic, real-time multimodal routing that efficiently handles uncertainty and complex decision-making for autonomous agents using multiple transportation modes.

## Contribution

It introduces a novel hierarchical hybrid planning approach that exploits problem structure for efficient real-time multimodal routing under uncertainty.

## Key findings

- Framework outperforms baseline in time to reach destination.
- Significantly reduces energy expenditure.
- Handles large-scale, complex routing scenarios.

## Abstract

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.