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The Limpet: A ROS-Enabled Multi-Sensing Platform for the ORCA Hub.

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  • 1School of Engineering, Institute for Integrated Micro and Nano Systems, The University of Edinburgh, Scottish Microelectronics Centre, Alexander Crum Brown Road, King's Buildings, Edinburgh EH9 3FF, UK. m.mohammed@ed.ac.uk.

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Summary

This study introduces the Limpet, a low-cost robotic sensor for offshore infrastructure monitoring. It enables real-time condition assessment of structures like wind turbines using on-board signal processing and online classification.

Keywords:
LimpetORCA HubROS Interfacecommunication fail-overfault diagnosison-board processingreal-time condition monitoringremote sensingrobot sensing systemsrobots

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Area of Science:

  • Robotics and Automation
  • Structural Health Monitoring
  • Sensor Technology

Background:

  • Increasing pressure to enhance safety in offshore operations by reducing human presence.
  • Need for cost-effective and reliable solutions for inspection, repair, and maintenance of offshore infrastructure.
  • Development of integrated robotic systems for comprehensive asset management.

Purpose of the Study:

  • To introduce the Limpet, a novel, low-cost, and manufacturable multi-sensing platform for offshore monitoring.
  • To demonstrate the Limpet's capability for real-time condition monitoring of offshore structures using integrated sensing and signal processing.
  • To integrate the Limpet into the Offshore Robotics for Certification of Assets (ORCA) Hub System for enhanced robotic interaction.

Main Methods:

  • Development and integration of the Limpet multi-sensing platform with the Robot Operating System (ROS).
  • Utilizing a Time-of-Flight distance sensor for condition monitoring, demonstrated on a simulated wind turbine setup.
  • Implementing both offline and online classification approaches for fault detection, with online classification performed on-board the microcontroller.

Main Results:

  • Successful real-time condition monitoring and classification of four simulated wind turbine faults using the Limpet.
  • Demonstrated the efficacy of the online classification approach with on-board data processing and analysis.
  • Validation of reduced information density transmissions through online classification, enabling the use of long-range, low-bandwidth communication.

Conclusions:

  • Robots can perform on-board signal processing and analysis for multi-functional sensing and improved communication in offshore environments.
  • The Limpet platform offers a scalable and cost-effective solution for enhancing the structural health monitoring of offshore assets.
  • The ORCA Hub System, with integrated sensing platforms like the Limpet, represents a significant advancement in robotic solutions for the offshore industry.