階層型不確実性対応協調マルチエージェント計画システムの実環境展開
Real-World Deployment of a Hierarchical Uncertainty-Aware Collaborative Multiagent Planning System
不確実な実環境で複数ロボットが長距離協調ナビゲーションを行うため、階層的な計画システムを構築し、各階層の失敗に頑健な設計で実機チームに展開した。
著者: Martina Stadler Kurtz, Samuel Prentice, Yasmin Veys, Long Quang, Carlos Nieto-Granda, Michael Novitzky, Ethan Stump, Nicholas Roy
分類: cs.RO, cs.AI, cs.MA
原文アブストラクト
We would like to enable a collaborative multiagent team to navigate at long length scales and under uncertainty in real-world environments. In practice, planning complexity scales with the number of agents in the team, with the length scale of the environment, and with environmental uncertainty. Enabling tractable planning requires developing abstract models that can represent complex, high-quality plans. However, such models often abstract away information needed to generate directly-executable plans for real-world agents in real-world environments, as planning in such detail, especially in the presence of real-world uncertainty, would be computationally intractable. In this paper, we describe the deployment of a planning system that used a hierarchy of planners to execute collaborative multiagent navigation tasks in real-world, unknown environments. By developing a planning system that was robust to failures at every level of the planning hierarchy, we enabled the team to complete collaborative navigation tasks, even in the presence of imperfect planning abstractions and real-world uncertainty. We deployed our approach on a Clearpath Husky-Jackal team navigating in a structured outdoor environment, and demonstrated that the system enabled the agents to successfully execute collaborative plans.
