Design and Estimation of an AUV Portable Intelligent Rescue System Based on Attitude Recognition Algorithm

Abstract

This research is based on the attitude sensing algorithm to design a portable intelligent rescue system for autonomous underwater vehicles (AUVs). To lower the possibility of losing the underwater vehicle and reduce the difficulty of rescuing, when an AUV intelligent rescue system (AIRS) detects the fault of AUVs and they could not be reclaimed, AIRS can pump carbon dioxide into the airbag immediately to make the vehicle resurface. AIRS consists of attitude sensing module, double-trigger inflator mechanism, and activity recognition algorithm. The sensing module is an eleven-DOF sensor that is made up of a six-axis inertial sensor, a three-axis magnetometer, a barometer, and a thermometer. Furthermore, the signal calibration and extended Kalman filter (SC-EKF) is proposed to be used subsequently to calibrate and fuse the data from the sensing module. Then, the attitude data are classified with the principle of feature extraction (FE) and backpropagation network (BPN) classifier. Finally, the designed double-trigger inflator can be triggered not only by electricity but also by water damage when the waterproof cabin is severely broken. With the AIRS technology, the safety of detecting and investigating the use AUVs can be increased since there is no need to send divers to engage in the rescue mission under water.

Submersible location prediction using SST turbulence and dynamic analysis

“…The Submersible Position Prediction Model established in this paper can be extended to other sea areas. By changing the parameters of ocean currents, density, and terrain in the model, the positions of submersibles in other sea areas can be predicted [7] . Taking the Caribbean Sea and the Maldives (Indian Ocean) as examples, the results of visualizing the density, ocean currents, and terrain of these sea areas illustrate the potential changes in submersible positions in the Caribbean Sea and the Maldives.…”