Abstract
Autonomous underwater vehicles (AUVs) have changed the way marine environment is surveyed, monitored and mapped. Autonomous underwater vehicles have a wide range of applications in research, military, and commercial settings. AUVs not only perform a given task but also adapt to changes in the environment, e.g., sudden side currents, downdrafts, and other effects which are extremely unpredictable. To navigate properly and allow simultaneous localisation and mapping (SLAM) algorithms to be used, these effects need to be detected. With current navigation systems, these disturbances in the water flow are not measured directly. Only the indirect effects are observed. It is proposed to detect the disturbances directly by placing pressure sensors on the surface of the AUV and processing the pressure data obtained. Within this study, the applicability of different learning methods for determining flow parameters of a surrounding fluid from pressure on an AUV body are tested. This is based on CFD simulations using pressure data from specified points on the surface of the AUV. It is shown that support vector machines are most suitable for the given task and yield excellent results.
Highlights
Autonomous underwater vehicles (AUVs) are a sub-group of unmanned undersea vehicles (UUVs), which have changed the way marine environment is surveyed, monitored, and mapped.Autonomous underwater vehicles have a wide range of applications in research, military, and commercial settings
The root mean square errors for forward/backward motion were slightly higher while the root mean square errors for upward/downward motion were slightly lower than the values given in the table
For multiple linear regression (MLR), the root mean square error was around 1.5 kn except with singular value decomposition, where it was above 2 kn
Summary
Autonomous underwater vehicles (AUVs) are a sub-group of unmanned undersea vehicles (UUVs), which have changed the way marine environment is surveyed, monitored, and mapped.Autonomous underwater vehicles have a wide range of applications in research, military, and commercial settings. The oil and gas industry shows great interest in using autonomous underwater vehicles for finding new underwater oil fields and for pipeline inspection [1]. They are utilised when the use of manned undersea vehicles is too dangerous, impossible, or too expensive [2]. Typical influences are sudden side currents, downdrafts, and other effects, which are extremely unpredictable. To navigate properly, these effects need to be detected
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