Preprint Article Version 1 This version is not peer-reviewed

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

Version 1 : Received: 20 August 2018 / Approved: 20 August 2018 / Online: 20 August 2018 (19:24:46 CEST)

A peer-reviewed article of this Preprint also exists.

Sayed, M.E.; Nemitz, M.P.; Aracri, S.; McConnell, A.C.; McKenzie, R.M.; Stokes, A.A. The Limpet: A ROS-Enabled Multi-Sensing Platform for the ORCA Hub. Sensors 2018, 18, 3487. Sayed, M.E.; Nemitz, M.P.; Aracri, S.; McConnell, A.C.; McKenzie, R.M.; Stokes, A.A. The Limpet: A ROS-Enabled Multi-Sensing Platform for the ORCA Hub. Sensors 2018, 18, 3487.

Journal reference: Sensors 2018, 18, 3487
DOI: 10.3390/s18103487

Abstract

The oil and gas industry faces increasing pressure to remove people from dangerous offshore environments. Robots present a cost-effective and safe method for inspection, repair and maintenance of topside and marine offshore infrastructure. In this work, we introduce a new immobile multi-sensing robot, the Limpet, which is designed to be low-cost and highly manufacturable, and thus can be deployed in huge collectives for monitoring offshore platforms. The Limpet can be considered an instrument, where in abstract terms, an instrument is a device that transforms a physical variable of interest (measurand) into a form that is suitable for recording (measurement). The Limpet is designed to be part of the ORCA (Offshore Robotics for Certification of Assets) Hub System, which consists of the offshore assets and all the robots (UAVs, drones, mobile legged robots etc.) interacting with them. The Limpet comprises the sensing aspect of the ORCA Hub System. We integrated the Limpet with Robot Operating System (ROS), which allows it to interact with other robots in the ORCA Hub System. In this work, we demonstrate how the Limpet can be used to achieve real-time condition monitoring for offshore structures, by combining remote sensing with signal processing techniques. We show an example of this approach for monitoring offshore wind turbines. We demonstrate the use of four different communication systems (WiFi, serial, LoRa and optical communication) for the condition monitoring process. By processing the sensor data on-board, we reduce the information density of our transmissions, which allows us to substitute short-range high-bandwidth communication systems with low-bandwidth long-range communication systems. We train our classifier offline and transfer its parameters to the Limpet for online classification, where it makes an autonomous decision based on the condition of the monitored structure.

Subject Areas

Communication Fail-Over, Fault Diagnosis, Limpet, On-board Processing, ORCA Hub, Real-time Condition Monitoring, Remote Sensing, Robots, Robot Sensing Systems, ROS Interface.

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