# Dynamic Real-time Multimodal Routing with Hierarchical Hybrid Planning

arXiv: https://arxiv.org/abs/1902.01560 · HTML: https://mlsanity.com/paper/1902.01560/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Feb 2019.

## Built on by (most cited first, 3 of 3)

- [Coordinated Multi-Agent Pathfinding for Drones and Trucks over Road Networks](https://mlsanity.com/paper/2110.08802/) (2021)
- [Scalable Anytime Planning for Multi-Agent MDPs](https://mlsanity.com/paper/2101.04788/) (2021)
- [Dynamic Resource Management for Providing QoS in Drone Delivery Systems](https://mlsanity.com/paper/2103.04015/) (2021)

## Abstract (arXiv)

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.