時相タスクと安全帰還制約下における不確実MDPのオンライン計画
Online Planning of Uncertain MDPs under Temporal Tasks and Safe-Return Constraints
移動ロボットの運動を不確実MDPとしてモデル化し、LTLで表された複雑な時相タスクを満たしつつ、ベイズ的にモデルを更新しながら安全に帰還できるオンライン計画手法を提案した。
著者: Yuyang Zhang, Meng Guo
分類: cs.RO, cs.SY, eess.SY
原文アブストラクト
This paper addresses the online motion planning problem of mobile robots under complex high-level tasks. The robot motion is modeled as an uncertain Markov Decision Process (MDP) due to limited initial knowledge, while the task is specified as Linear Temporal Logic (LTL) formulas. The proposed framework enables the robot to explore and update the system model in a Bayesian way, while simultaneously optimizing the asymptotic costs of satisfying the complex temporal task. Theoretical guarantees are provided for the synthesized outgoing policy and safety policy. More importantly, instead of greedy exploration under the classic ergodicity assumption, a safe-return requirement is enforced such that the robot can always return to home states with a high probability. The overall methods are validated by numerical simulations.
