Behavior-based planning and prosecution architecture for Autonomous Underwater Vehicles in Ocean Observatories
This paper discusses the autonomy framework proposed for the mobile instruments such as Autonomous Underwater Vehicles (AUVs) and gliders. Paper focuses on the challenges faced by these clusters of mobile platform in executive tasks such as adaptive sampling in the hostile underwater environment. Co...
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| Format: | Article |
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Institute of Electrical and Electronics Engineers (IEEE)
2019
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| Online Access: | http://hdl.handle.net/1721.1/120741 https://orcid.org/0000-0002-2883-7027 https://orcid.org/0000-0003-3422-8700 |
| _version_ | 1826198300795076608 |
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| author | Balasuriya, Arjuna Prabhath Petillo, Stephanie Marie Schmidt, Henrik Benjamin, Michael |
| author2 | Massachusetts Institute of Technology. Department of Mechanical Engineering |
| author_facet | Massachusetts Institute of Technology. Department of Mechanical Engineering Balasuriya, Arjuna Prabhath Petillo, Stephanie Marie Schmidt, Henrik Benjamin, Michael |
| author_sort | Balasuriya, Arjuna Prabhath |
| collection | MIT |
| description | This paper discusses the autonomy framework proposed for the mobile instruments such as Autonomous Underwater Vehicles (AUVs) and gliders. Paper focuses on the challenges faced by these clusters of mobile platform in executive tasks such as adaptive sampling in the hostile underwater environment. Collaborations between these mobile instruments are essential to capture the environmental changes and track them for time-series analysis. This paper looks into the challenges imposed by the underwater communication infrastructure and presents the nested autonomy architecture as a solution to overcome these challenges. The autonomy architecture is separated from the low-level control architecture of these instruments, which is called the `backseat driver'. The back-seat driver paradigm is implemented on the Mission Oriented Object Suite (MOOS) developed at MIT. The autonomy is achieved by generating multiple behaviors (multiple objective functions) linked to the internal state of the platform as well as the environment. Optimization engine called the MOOS-IvP is used to pick the best action for the given instance based on the mission at hand. At sea operational scenarios and results are presented to demonstrate the proposed autonomy architecture for Ocean Observatory Initiative (OOI). Keywords: Autonomous Underwater Vehicles (AUVs), Underwater
Gliders, MOOS, MOOS DB, MOOS-IvP, OOI-CI, behavior-based
autonomy |
| first_indexed | 2024-09-23T11:02:43Z |
| format | Article |
| id | mit-1721.1/120741 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T11:02:43Z |
| publishDate | 2019 |
| publisher | Institute of Electrical and Electronics Engineers (IEEE) |
| record_format | dspace |
| spelling | mit-1721.1/1207412022-10-01T00:45:16Z Behavior-based planning and prosecution architecture for Autonomous Underwater Vehicles in Ocean Observatories Balasuriya, Arjuna Prabhath Petillo, Stephanie Marie Schmidt, Henrik Benjamin, Michael Massachusetts Institute of Technology. Department of Mechanical Engineering Balasuriya, Arjuna Prabhath Petillo, Stephanie Marie Schmidt, Henrik Benjamin, Michael This paper discusses the autonomy framework proposed for the mobile instruments such as Autonomous Underwater Vehicles (AUVs) and gliders. Paper focuses on the challenges faced by these clusters of mobile platform in executive tasks such as adaptive sampling in the hostile underwater environment. Collaborations between these mobile instruments are essential to capture the environmental changes and track them for time-series analysis. This paper looks into the challenges imposed by the underwater communication infrastructure and presents the nested autonomy architecture as a solution to overcome these challenges. The autonomy architecture is separated from the low-level control architecture of these instruments, which is called the `backseat driver'. The back-seat driver paradigm is implemented on the Mission Oriented Object Suite (MOOS) developed at MIT. The autonomy is achieved by generating multiple behaviors (multiple objective functions) linked to the internal state of the platform as well as the environment. Optimization engine called the MOOS-IvP is used to pick the best action for the given instance based on the mission at hand. At sea operational scenarios and results are presented to demonstrate the proposed autonomy architecture for Ocean Observatory Initiative (OOI). Keywords: Autonomous Underwater Vehicles (AUVs), Underwater Gliders, MOOS, MOOS DB, MOOS-IvP, OOI-CI, behavior-based autonomy 2019-03-05T18:52:28Z 2019-03-05T18:52:28Z 2010-10 2010-05 2018-12-19T19:11:12Z Article http://purl.org/eprint/type/JournalArticle 978-1-4244-5221-7 http://hdl.handle.net/1721.1/120741 Balasuriya, Arjuna, Stephanie Petillo, Henrik Schmidt, and Michael Benjamin. “Behavior-Based Planning and Prosecution Architecture for Autonomous Underwater Vehicles in Ocean Observatories.” OCEANS’10 IEEE SYDNEY, 24-27 October, 2010. Sydney, NSW, Australia, IEEE, 2010. https://orcid.org/0000-0002-2883-7027 https://orcid.org/0000-0003-3422-8700 http://dx.doi.org/10.1109/OCEANSSYD.2010.5603896 OCEANS'10 IEEE SYDNEY Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Institute of Electrical and Electronics Engineers (IEEE) Other repository |
| spellingShingle | Balasuriya, Arjuna Prabhath Petillo, Stephanie Marie Schmidt, Henrik Benjamin, Michael Behavior-based planning and prosecution architecture for Autonomous Underwater Vehicles in Ocean Observatories |
| title | Behavior-based planning and prosecution architecture for Autonomous Underwater Vehicles in Ocean Observatories |
| title_full | Behavior-based planning and prosecution architecture for Autonomous Underwater Vehicles in Ocean Observatories |
| title_fullStr | Behavior-based planning and prosecution architecture for Autonomous Underwater Vehicles in Ocean Observatories |
| title_full_unstemmed | Behavior-based planning and prosecution architecture for Autonomous Underwater Vehicles in Ocean Observatories |
| title_short | Behavior-based planning and prosecution architecture for Autonomous Underwater Vehicles in Ocean Observatories |
| title_sort | behavior based planning and prosecution architecture for autonomous underwater vehicles in ocean observatories |
| url | http://hdl.handle.net/1721.1/120741 https://orcid.org/0000-0002-2883-7027 https://orcid.org/0000-0003-3422-8700 |
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