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Ocean Systems Engineering
  Volume 7, Number 2, June 2017 , pages 157-177

Design and estimation of a sensing attitude algorithm for AUV self-rescue system
Yi-Ting Yang and Sheng-Chih Shen

    This research is based on the concept of safety airbag to design a self-rescue system for the autonomous underwater vehicle (AUV) using micro inertial sensing module. To reduce the possibility of losing the underwater vehicle and the difficulty of searching and rescuing, when the AUV self-rescue system (ASRS) detects that the AUV is crashing or encountering a serious collision, it can pump carbon dioxide into the airbag immediately to make the vehicle surface. ASRS consists of 10-DOF sensing module, sensing attitude algorithm and air-pumping mechanism. The attitude sensing modules are a nine-axis micro-inertial sensor and a barometer. The sensing attitude algorithm is designed to estimate failure attitude of AUV properly using sensor calibration and extended Kalman filter (SCEKF), feature extraction and backpropagation network (BPN) classify. SCEKF is proposed to be used subsequently to calibrate and fuse the data from the micro-inertial sensors. Feature extraction and BPN training algorithms for classification are used to determine the activity malfunction of AUV. When the accident of AUV occurred, the ASRS will immediately be initiated; the airbag is soon filled, and the AUV will surface due to the buoyancy. In the future, ASRS will be developed successfully to solve the problems such as the high losing rate and the high difficulty of the rescuing mission of AUV.
Key Words
    autonomous underwater vehicle; extended Kalman filtering; BPN classifier; AUV airbag
Yi-Ting Yang and Sheng-Chih Shen: Department of Systems and Naval Mechatronic Engineering, National Cheng Kung University, No.1, Daxue Rd., East Dist., Tainan City 701, Taiwan (R.O.C.)

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