Information-Driven Path Planning
Name
43154_2021_45_ReferencePDF.pdf
Size
1.97 MB
Format
Adobe PDF
Checksum (MD5)
10c473e3a14bac6024eb6448fc786cc7
Author(s)
Bai, Shi
Shan, Tixiao
Chen, Fanfei
Liu, Lantao
Englot, Brendan
Date Issued
April 30, 2021
Publisher
Springer International Publishing
Version
Author's final manuscript
Abstract
Abstract
Purpose of Review
The era of robotics-based environmental monitoring has given rise to many interesting areas of research. A key challenge is that robotic platforms and their operations are typically constrained in ways that limit their energy, time, or travel distance, which in turn limits the number of measurements that can be collected. Therefore, paths need to be planned to maximize the information gathered about an unknown environment while satisfying the given budget constraint, which is known as the informative planning problem. This review discusses the literature dedicated to information-driven path planning, introducing the key algorithmic building blocks as well as the outstanding challenges.
Recent Findings
Machine learning approaches have been introduced to solve the information-driven path planning problem, improving both efficiency and robustness.
Summary
This review started with the fundamental building blocks of informative planning for environment modeling and monitoring, followed by integration with machine learning, emphasizing how machine learning can be used to improve the robustness and efficiency of informative path planning in robotics.
MIT Department
Senseable City Laboratory
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1007/s43154-021-00045-6
