In situ analysis for intelligent control

M. Fox, D. Long, F. Py, K. Rajan, J. Ryan

Research output: Contribution to conferencePaper

14 Citations (Scopus)

Abstract

We report a pilot study on in situ analysis of backscatter data for intelligent control of a scientific instrument on an Autonomous Underwater Vehicle (AUV) carried out at the Monterey Bay Aquarium Research Institute (MBARI). The objective of the study is to investigate techniques which use machine intelligence to enable event-response scenarios. Specifically we analyse a set of techniques for automated sample acquisition in the water-column using an electro-mechanical "Gulper", designed at MBARI. This is a syringe-like sampling device, carried onboard an AUV. The techniques we use in this study are clustering algorithms, intended to identify the important distinguishing characteristics of bodies of points within a data sample. We demonstrate that the complementary features of two clustering approaches can offer robust identification of interesting features in the water-column, which, in turn, can support automatic event-response control in the use of the Gulper.
LanguageEnglish
Pages1364-1369
Number of pages5
Publication statusPublished - 2007
EventMTS/IEEE Oceans 2007 - Vancouver, Canada
Duration: 2 Oct 20073 Oct 2007

Conference

ConferenceMTS/IEEE Oceans 2007
CityVancouver, Canada
Period2/10/073/10/07

Fingerprint

Autonomous underwater vehicles
Intelligent control
Syringes
Clustering algorithms
Water
Sampling

Keywords

  • machine intelligence
  • computer science
  • robotics
  • artificial intelligence

Cite this

Fox, M., Long, D., Py, F., Rajan, K., & Ryan, J. (2007). In situ analysis for intelligent control. 1364-1369. Paper presented at MTS/IEEE Oceans 2007, Vancouver, Canada, .
Fox, M. ; Long, D. ; Py, F. ; Rajan, K. ; Ryan, J. / In situ analysis for intelligent control. Paper presented at MTS/IEEE Oceans 2007, Vancouver, Canada, .5 p.
@conference{27fe98f7ff924b548cc4cb7245261202,
title = "In situ analysis for intelligent control",
abstract = "We report a pilot study on in situ analysis of backscatter data for intelligent control of a scientific instrument on an Autonomous Underwater Vehicle (AUV) carried out at the Monterey Bay Aquarium Research Institute (MBARI). The objective of the study is to investigate techniques which use machine intelligence to enable event-response scenarios. Specifically we analyse a set of techniques for automated sample acquisition in the water-column using an electro-mechanical {"}Gulper{"}, designed at MBARI. This is a syringe-like sampling device, carried onboard an AUV. The techniques we use in this study are clustering algorithms, intended to identify the important distinguishing characteristics of bodies of points within a data sample. We demonstrate that the complementary features of two clustering approaches can offer robust identification of interesting features in the water-column, which, in turn, can support automatic event-response control in the use of the Gulper.",
keywords = "machine intelligence, computer science, robotics, artificial intelligence",
author = "M. Fox and D. Long and F. Py and K. Rajan and J. Ryan",
year = "2007",
language = "English",
pages = "1364--1369",
note = "MTS/IEEE Oceans 2007 ; Conference date: 02-10-2007 Through 03-10-2007",

}

Fox, M, Long, D, Py, F, Rajan, K & Ryan, J 2007, 'In situ analysis for intelligent control' Paper presented at MTS/IEEE Oceans 2007, Vancouver, Canada, 2/10/07 - 3/10/07, pp. 1364-1369.

In situ analysis for intelligent control. / Fox, M.; Long, D.; Py, F.; Rajan, K.; Ryan, J.

2007. 1364-1369 Paper presented at MTS/IEEE Oceans 2007, Vancouver, Canada, .

Research output: Contribution to conferencePaper

TY - CONF

T1 - In situ analysis for intelligent control

AU - Fox, M.

AU - Long, D.

AU - Py, F.

AU - Rajan, K.

AU - Ryan, J.

PY - 2007

Y1 - 2007

N2 - We report a pilot study on in situ analysis of backscatter data for intelligent control of a scientific instrument on an Autonomous Underwater Vehicle (AUV) carried out at the Monterey Bay Aquarium Research Institute (MBARI). The objective of the study is to investigate techniques which use machine intelligence to enable event-response scenarios. Specifically we analyse a set of techniques for automated sample acquisition in the water-column using an electro-mechanical "Gulper", designed at MBARI. This is a syringe-like sampling device, carried onboard an AUV. The techniques we use in this study are clustering algorithms, intended to identify the important distinguishing characteristics of bodies of points within a data sample. We demonstrate that the complementary features of two clustering approaches can offer robust identification of interesting features in the water-column, which, in turn, can support automatic event-response control in the use of the Gulper.

AB - We report a pilot study on in situ analysis of backscatter data for intelligent control of a scientific instrument on an Autonomous Underwater Vehicle (AUV) carried out at the Monterey Bay Aquarium Research Institute (MBARI). The objective of the study is to investigate techniques which use machine intelligence to enable event-response scenarios. Specifically we analyse a set of techniques for automated sample acquisition in the water-column using an electro-mechanical "Gulper", designed at MBARI. This is a syringe-like sampling device, carried onboard an AUV. The techniques we use in this study are clustering algorithms, intended to identify the important distinguishing characteristics of bodies of points within a data sample. We demonstrate that the complementary features of two clustering approaches can offer robust identification of interesting features in the water-column, which, in turn, can support automatic event-response control in the use of the Gulper.

KW - machine intelligence

KW - computer science

KW - robotics

KW - artificial intelligence

UR - http://www.oceans07mtsieeevancouver.org/

UR - http://www.cis.strath.ac.uk/research/publications/papers/strath_cis_publication_1969.pdf

M3 - Paper

SP - 1364

EP - 1369

ER -

Fox M, Long D, Py F, Rajan K, Ryan J. In situ analysis for intelligent control. 2007. Paper presented at MTS/IEEE Oceans 2007, Vancouver, Canada, .