A Survey of Decision-Theoretic Approaches for Robotic Environmental Monitoring

TL;DR

This survey reviews decision-theoretic methods in robotic environmental monitoring, highlighting how these approaches optimize sampling and task execution for efficient environmental data collection.

Contribution

It provides the first comprehensive taxonomy of decision-theoretic approaches, detailing representations, algorithms, and challenges in robotic environmental monitoring.

Findings

Decision-theoretic methods improve sampling efficiency.

Various algorithms optimize measurement and task assignment.

The survey identifies key challenges and future opportunities.

Abstract

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.