Information-Driven Path Planning
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 ener...
| Main Authors: | , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
Springer International Publishing
2021
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| Online Access: | https://hdl.handle.net/1721.1/136804 |
| _version_ | 1826193313728823296 |
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| author | Bai, Shi Shan, Tixiao Chen, Fanfei Liu, Lantao Englot, Brendan |
| author2 | Senseable City Laboratory |
| author_facet | Senseable City Laboratory Bai, Shi Shan, Tixiao Chen, Fanfei Liu, Lantao Englot, Brendan |
| author_sort | Bai, Shi |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T09:37:00Z |
| format | Article |
| id | mit-1721.1/136804 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T09:37:00Z |
| publishDate | 2021 |
| publisher | Springer International Publishing |
| record_format | dspace |
| spelling | mit-1721.1/1368042023-02-23T15:19:53Z Information-Driven Path Planning Bai, Shi Shan, Tixiao Chen, Fanfei Liu, Lantao Englot, Brendan Senseable City Laboratory 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. 2021-11-01T14:33:29Z 2021-11-01T14:33:29Z 2021-04-30 2021-05-17T06:29:06Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/136804 en https://doi.org/10.1007/s43154-021-00045-6 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. The Author(s), under exclusive licence to Springer Nature Switzerland AG application/pdf Springer International Publishing Springer International Publishing |
| spellingShingle | Bai, Shi Shan, Tixiao Chen, Fanfei Liu, Lantao Englot, Brendan Information-Driven Path Planning |
| title | Information-Driven Path Planning |
| title_full | Information-Driven Path Planning |
| title_fullStr | Information-Driven Path Planning |
| title_full_unstemmed | Information-Driven Path Planning |
| title_short | Information-Driven Path Planning |
| title_sort | information driven path planning |
| url | https://hdl.handle.net/1721.1/136804 |
| work_keys_str_mv | AT baishi informationdrivenpathplanning AT shantixiao informationdrivenpathplanning AT chenfanfei informationdrivenpathplanning AT liulantao informationdrivenpathplanning AT englotbrendan informationdrivenpathplanning |