大規模海底地形マッピングのための効率的な非近視眼的階層ベイズ最適化情報経路計画
Efficient Non-Myopic Layered Bayesian Optimization For Large-Scale Bathymetric Informative Path Planning
AUVによる海底地形マッピングで、大規模な確率的変分ガウス過程マップ上で非近視眼的なリアルタイム経路計画を階層ベイズ最適化で実現し、従来手法より高効率なマッピングを可能にした。
著者: Alexander Kiessling, Ignacio Torroba, Chelsea Rose Sidrane, Ivan Stenius, Jana Tumova, John Folkesson
分類: cs.RO
原文アブストラクト
Informative path planning (IPP) applied to bathymetric mapping allows AUVs to focus on feature-rich areas to quickly reduce uncertainty and increase mapping efficiency. Existing methods based on Bayesian optimization (BO) over Gaussian Process (GP) maps work well on small scenarios but they are short-sighted and computationally heavy when mapping larger areas, hindering deployment in real applications. To overcome this, we present a 2-layered BO IPP method that performs non-myopic, real-time planning in a tree search fashion over large Stochastic Variational GP maps, while respecting the AUV motion constraints and accounting for localization uncertainty. Our framework outperforms the standard industrial lawn-mowing pattern and a myopic baseline in a set of hardware in the loop (HIL) experiments in an embedded platform over real bathymetry.
