適応的情報経路計画のための学習ベース手法に関するサーベイ
Learning-based Methods for Adaptive Informative Path Planning
未知環境で効率的にデータ収集する適応的情報経路計画(AIPP)に学習手法を適用した研究を、統一的な数理枠組みと2つの分類軸で整理したサーベイ。
著者: Marija Popovic, Joshua Ott, Julius Rückin, Mykel J. Kochenderfer
分類: cs.RO
原文アブストラクト
Adaptive informative path planning (AIPP) is important to many robotics applications, enabling mobile robots to efficiently collect useful data about initially unknown environments. In addition, learning-based methods are increasingly used in robotics to enhance adaptability, versatility, and robustness across diverse and complex tasks. Our survey explores research on applying robotic learning to AIPP, bridging the gap between these two research fields. We begin by providing a unified mathematical framework for general AIPP problems. Next, we establish two complementary taxonomies of current work from the perspectives of (i) learning algorithms and (ii) robotic applications. We explore synergies, recent trends, and highlight the benefits of learning-based methods in AIPP frameworks. Finally, we discuss key challenges and promising future directions to enable more generally applicable and robust robotic data-gathering systems through learning. We provide a comprehensive catalogue of papers reviewed in our survey, including publicly available repositories, to facilitate future studies in the field.
