ロボット環境モニタリングのための意思決定論的アプローチに関するサーベイ
A Survey of Decision-Theoretic Approaches for Robotic Environmental Monitoring
ロボットによる環境モニタリングにおいて、効率的なサンプリングを実現する意思決定論的手法を初めて包括的に調査し、環境表現やタスク、最適化アルゴリズムを整理した。
著者: Yoonchang Sung, Zhiang Chen, Jnaneshwar Das, Pratap Tokekar
分類: cs.RO
原文アブストラクト
Robotics has dramatically increased our ability to gather data about our environments, creating an opportunity for the robotics and algorithms communities to collaborate on novel solutions to environmental monitoring problems. To understand a taxonomy of problems and methods in this realm, we present the first comprehensive survey of decision-theoretic approaches that enable efficient sampling of various environmental processes. We investigate representations for different environments, followed by a discussion of using these presentations to solve tasks of interest, such as learning, localization, and monitoring. To efficiently implement the tasks, decision-theoretic optimization algorithms consider: (1) where to take measurements from, (2) which tasks to be assigned, (3) what samples to collect, (4) when to collect samples, (5) how to learn environment; and (6) who to communicate. Finally, we summarize our study and present the challenges and opportunities in robotic environmental monitoring.
