DyPNIPP: 環境動態を予測するRLベースのロバストな情報経路計画
DyPNIPP: Predicting Environment Dynamics for RL-based Robust Informative Path Planning
時空間的に変化する環境での情報経路計画(IPP)のため、ドメインランダム化と動態予測モデルを組み合わせた強化学習フレームワークを提案し、山火事環境での実験でロバスト性と性能の向上を示した。
著者: Srujan Deolasee, Siva Kailas, Wenhao Luo, Katia Sycara, Woojun Kim
分類: cs.RO, cs.AI
原文アブストラクト
Informative path planning (IPP) is an important planning paradigm for various real-world robotic applications such as environment monitoring. IPP involves planning a path that can learn an accurate belief of the quantity of interest, while adhering to planning constraints. Traditional IPP methods typically require high computation time during execution, giving rise to reinforcement learning (RL) based IPP methods. However, the existing RL-based methods do not consider spatio-temporal environments which involve their own challenges due to variations in environment characteristics. In this paper, we propose DyPNIPP, a robust RL-based IPP framework, designed to operate effectively across spatio-temporal environments with varying dynamics. To achieve this, DyPNIPP incorporates domain randomization to train the agent across diverse environments and introduces a dynamics prediction model to capture and adapt the agent actions to specific environment dynamics. Our extensive experiments in a wildfire environment demonstrate that DyPNIPP outperforms existing RL-based IPP algorithms by significantly improving robustness and performing across diverse environment conditions.
