Adaptive neuro-fuzzy network enhanced automatic visual servoing algorithm for ROV manipulators

Satja Sivcev, Petar Trslic, David Adley, Luke Robinson, Gerard Dooly, Edin Omerdic, Daniel Toal

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

This paper presents research and development for achieving advanced ROV manipulation systems with vision based servo control capable of being operated by pilots with auto assist in the dynamic subsea conditions. Underwater inspection and intervention operations are performed by work-class ROVs equipped with robotic manipulators. A standard offshore oil and gas setup includes a human pilot utilising telemanipulation technology to operate both vehicle and manipulators based on the work-site visual feedback provided by camera and sonar systems. For challenging applications in waves or currents where target devices are in motion a new approach is required. A position based visual servoing (PBVS) algorithm designed to follow a moving target with an underwater manipulator is proposed. The developed algorithm integrates Adaptive Neuro-Fuzzy Inference System (ANFIS) network framework for target motion prediction. The effectiveness of the developed software is verified through a series of experiments carried out with an off-the-shelf industrial hydraulic subsea manipulator in the laboratory conditions.

Original languageEnglish
Title of host publicationOCEANS 2019 MTS/IEEE Seattle, OCEANS 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9780578576183
DOIs
Publication statusPublished - Oct 2019
Event2019 OCEANS MTS/IEEE Seattle, OCEANS 2019 - Seattle, United States
Duration: 27 Oct 201931 Oct 2019

Publication series

NameOCEANS 2019 MTS/IEEE Seattle, OCEANS 2019

Conference

Conference2019 OCEANS MTS/IEEE Seattle, OCEANS 2019
Country/TerritoryUnited States
CitySeattle
Period27/10/1931/10/19

Keywords

  • ANFIS
  • Subsea manipulation
  • Visual servoing

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