Integrating sensors for robots operating on offshore oil and gas platforms

This thesis presents a solution to integrate sensors and instruments on a robot to be used instead of operators on unmanned oil and gas offshore platforms. Operators have various tasks from inspection to maintenance in the platforms. Because of high costs of having operators in offshore platforms, there has been always an ambitious to design a fully unmanned automated platform to decrease the costs and increase human safety in oil and gas industry. These days Robotics is quite mature to be utilized in different industries. There are few manufacturers that produce robots in order that robots perform some activities in industrial environment. But the Robot usage in offshore platforms has higher risks and they have not been used before as a rigid solution, because of inaccessibility to platforms at all conditions (such as bad weather). In this thesis, I have collected the operator tasks which are possible to be done by robots, provided main requirements to use the robots in oil and gas offshore platforms and found the sensors and instruments to be suitable to mount on the robot to measure, collect and analyze required data. Finally, the proper way for data processing and analysis was done in MATLAB Simulink to present the result of measurements and data collection. The topic of this thesis was inspired from oil and gas offshore industry and robots are going to be used in one of the largest oil and gas offshore projects in North Sea (Yggdrasil) which will be started to operate from 2027. This EPC project (Engineering Procurement Construction) has been started from 2021 and currently is ongoing in detail engineering. The information regarding operators’ tasks and required specifications for sensors and instruments were provided based on this project requirements. The report of this thesis can be used in future for the sensors and their integration on robots. It was not possible to test or prototype on existing robots within master thesis schedule because of different schedule of the master thesis and this oil and gas project. Only simulation was carried on showing the results of this thesis

A Neural Network Classifier for Notch Filter Classification of Sound-Source Elevation in a Mobile Robot

An important aspect of all robotic systems is sensing and there are many sensing modalities used including vision, tactile, olfactory and acoustics to name a few. This paper presents a robotic system for sensing in acoustics, specifically in elevation localization. The model presented is a two-stage model incorporating spectral analysis using artificial pinna and an artificial neural network for classification and elevation estimation. The spectral classifier uses notch filters to analyze changes in attenuation of certain frequencies with elevation. This paper shows how using the spectral output of a signal generated by an artificial pinna can be classified by a feed-forward backpropagation neural network to estimate the elevation of a sound-source

A neural network classifier for notch filter classification of sound-source elevation in a mobile robot

An important aspect of all robotic systems is sensing and there are many sensing modalities used including vision, tactile, olfactory and acoustics to name a few. This paper presents a robotic system for sensing in acoustics, specifically in elevation localization. The model presented is a two-stage model incorporating spectral analysis using artificial pinna and an artificial neural network for classification and elevation estimation. The spectral classifier uses notch filters to analyze changes in attenuation of certain frequencies with elevation. This paper shows how using the spectral output of a signal generated by an artificial pinna can be classified by a feed-forward backpropagation neural network to estimate the elevation of a sound-source.</p