The oil and gas industry faces increasing pressure to remove people from dangerous offshore environments. There have been several reported accidents and explosions of offshore rigs, with the most widely reported tragedy being the Deep Horizon oil spill in the Gulf of Mexico, which triggered the biggest debate from governments, academia, environmentalists and major companies of the oil and gas industry to look for safer ways to inspect, repair and maintain offshore platforms. Robots present a cost-effective and safe method for the inspection, repair and maintenance of topside and marine offshore infrastructure. They can increase health, safety and environment (HSE), while increasing the production and efficiency. In our recent publication, we introduce a new multisensing platform, the Limpet, which is designed to be low-cost and manufacturable, and thus can be deployed in huge collectives for monitoring offshore platforms. The Limpet consists of a single two-layer Printed Circuit Board (PCB) and a detachable Li-Ion coin cell battery. We equipped the Limpet with nine exteroceptive sensing modalities: temperature, pressure, humidity, optical, distance, sound, magnetic field, accelerometer and gyroscope. 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 ORCA Hub is a 3.5 year EPSRC funded, multi-site project with a vision to use teams of robots and autonomous intelligent systems (AIS) on remote energy platforms to enable cheaper, safer and more efficient working practices. The ORCA Hub System aims at achieving tasks that are not possible with a single robot due to the complex nature of offshore platforms. 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 our publication, 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 use two different approaches for the condition monitoring process: offline and online classification. We tested the offline classification approach using two different communication techniques: serial and WiFi, and the online classification approach using LoRa and optical communication. We train our classifier offline and transfer its parameters to the Limpet for online classification. We tailored a data processing procedure for the gathered data and trained the Limpet to distinguish among different functioning states. By using online classification, where the Limpet processes and analyses data on-board using the microcontroller, 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.
According to the industrial partners in the ORCA Hub project, one of the major problems they face is dealing with the vast amounts of data they get from their monitoring systems. As such, our publication shines light on how a robot can help with such problems by performing on-board signal processing and analysis to gain multi-functional sensing capabilities, improve their communication requirements, and monitor the structural health of equipment.
If you are interested in more details about the Limpet system, please have a look at our publication (link below) or contact our research group.
The Limpet: A ROS-Enabled Multi-Sensing Platform for the ORCA Hub
Recent Comments