An Embedded Tactical Decision Aid Framework for Environmentally Adaptive Autonomous Underwater Vehicle Communication and Navigation
Name
An_Embedded_Tactical_Decision_Aid_Framework_for_Environmentally_Adaptive_Autonomous_Underwater_Vehicle_Communication_and_Navigation.pdf
Size
7.99 MB
Format
Adobe PDF
Checksum (MD5)
310b5a4e0fc1a389e1e02f53278236aa
Author(s)
Bhatt, EeShan C.
Howard, Bradli
Schmidt, Henrik
Date Issued
October 2022
Journal
IEEE Journal of Oceanic Engineering
Publisher
Institute of Electrical and Electronics Engineers
Citation
Bhatt, EeShan C., Howard, Bradli and Schmidt, Henrik. 2022. "An Embedded Tactical Decision Aid Framework for Environmentally Adaptive Autonomous Underwater Vehicle Communication and Navigation." IEEE Journal of Oceanic Engineering, 47 (4).
Version
Final published version
Abstract
Motivated by a changing acoustic environment in the Arctic Beaufort Sea, in this article, we present a tactical decision aid framework for a human decision-maker collaborating with an autonomous underwater vehicle (AUV) to integrate the vertical sound-speed profile for underwater navigation and communication. In a predeployment phase, using modeled and real oceanographic data, we load basis function representations of the sound-speed perturbations onto one or more AUVs on deck, where a handful of weights can estimate a sound-speed profile. During deployment, these weights are updated on an AUV through a digital acoustic message to improve navigation and reciprocal communication throughout the duration of an under-ice mission. Field work applying this framework in the Beaufort Sea is presented, highlighting key decisions regarding predeployment oceanographic data assimilation, CTD cast collection, and in situ weight choice. Selected examples evaluate the framework’s ability to adapt to a depth-limited CTD cast and the appearance of an anomalous microlens feature in the profile. We show that the framework effectively balances the need to adapt in a changing acoustic environment in real time while maintaining operator trust in an AUV’s embedded intelligence.
Subjects
Electrical and Electronic Engineering
Mechanical Engineering
Ocean Engineering
MIT Department
Massachusetts Institute of Technology. Center for Ocean Engineering
Massachusetts Institute of Technology. Department of Mechanical Engineering
Terms of Use
Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/joe.2022.3159234
