動的なリアルタイムマルチモーダル経路計画の階層型ハイブリッドプランニング
Dynamic Real-time Multimodal Routing with Hierarchical Hybrid Planning
ドローンが飛行と地上車両を乗り継ぐような不確実な交通網で、階層型ハイブリッド計画により経路を計画・実行するフレームワークを提案し、大規模シミュレーションで既存手法を上回る性能を示した。
著者: Shushman Choudhury, Jacob P. Knickerbocker, Mykel J. Kochenderfer
分類: cs.AI, cs.RO
原文アブストラクト
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.
