時空間モデルと不完全観測下でのグリッドマップ探索計画
Estimation and Planning of Exploration Over Grid Map Using A Spatiotemporal Model with Incomplete State Observations
相互相関演算子を用いた時空間モデルと適応的状態推定器を組み合わせ、探索と活用のトレードオフを高レベル意思決定で扱う経路計画フレームワークを提案し、山火事探索シミュレーションでランダムウォークや単純な探索/活用戦略を上回ることを示した。
著者: Hyung-Jin Yoon, Hunmin Kim, Kripash Shrestha, Naira Hovakimyan, Petros Voulgaris
分類: cs.RO
原文アブストラクト
Path planning over spatiotemporal models can be applied to a variety of applications such as UAVs searching for spreading wildfire in mountains or network of balloons in time-varying atmosphere deployed for inexpensive internet service. A notable aspect in such applications is the dynamically changing environment. However, path planning algorithms often assume static environments and only consider the vehicle's dynamics exploring the environment. We present a spatiotemporal model that uses a cross-correlation operator to consider spatiotemporal dependence. Also, we present an adaptive state estimator for path planning. Since the state estimation depends on the vehicle's path, the path planning needs to consider the trade-off between exploration and exploitation. We use a high-level decision-maker to choose an explorative path or an exploitative path. The overall proposed framework consists of an adaptive state estimator, a short-term path planner, and a high-level decision-maker. We tested the framework with a spatiotemporal model simulation where the state of each grid transits from normal, latent, and fire state. For the mission objective of visiting the grids with fire, the proposed framework outperformed the random walk (baseline) and the single-minded exploitation (or exploration) path.
