Ocean Systems Engineering

  • 제7권2호
  • /
  • Pages.157-177
  • /
  • 2017
  • /
  • 2093-6702(pISSN)
  • /
  • 2093-677X(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Design and estimation of a sensing attitude algorithm for AUV self-rescue system

  • Yang, Yi-Ting (Department of Systems and Naval Mechatronic Engineering, National Cheng Kung University) ;
  • Shen, Sheng-Chih (Department of Systems and Naval Mechatronic Engineering, National Cheng Kung University)
  • 투고 : 2016.05.31
  • 심사 : 2017.05.10
  • 발행 : 2017.06.25
⟨ 이전 논문 다음 논문 ⟩

초록

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.

키워드

과제정보

연구 과제 주관 기관 : Ministry of Science and Technology (MOST), National Cheng Kung University

참고문헌

  1. Aggarwal, P., Syed, Z. and El-Sheimy, N. (2008), "Thermal calibration of low cost mems sensors for land vehicle navigation system", 2859-2863.
  2. Aha, D.W., Kibler, D. and Albert, M.K. (1991), "Instance-based learning algorithms", Machine Learning, 6(1), 37-66. https://doi.org/10.1007/BF00153759
  3. Asakawa, K., Kojima, J., Kato, Y., Matsumoto, S., Kato, N., Asai, T. and Iso, T. (2002), "Design concept and experimental results of the autonomous underwater vehicle AQUA EXPLORER 2 for the inspection of underwater cables", Adv. Robot., 16(1), 27-42. https://doi.org/10.1163/156855302317413727
  4. Bao, Z., Lu, G., Wang, Y. and Tian, D. (2013), "A calibration method for misalignment angle of vehicle-mounted IMU", Procedia - Social and Behavioral Sciences, 96, 1853-1860. https://doi.org/10.1016/j.sbspro.2013.08.210
  5. Biricik, G., Diri, B. and Sonmez, A.C. (2012), "Abstract feature extraction for text classification", Turkish J. Elec. Eng. Comput. Sci., 20, 1137-1159.
  6. Bonin-Font, F., Massot-Campos, M., Lluis Negre-Carrasco, P., Oliver-Codina, G. and Beltran, J.P. (2015), "Inertial sensor self-calibration in a visually-aided navigation approach for a micro-AUV", Sensors, 15(1), 1825-1860. https://doi.org/10.3390/s150101825
  7. Bouten, C.V.C., Koekkoek, K.T.M., Verduin, M., Kodde, R. and Janssen, J.D. (1997), "A triaxial accelerometer and portable data processing unit for the assessment of daily physical activity", IEEE T. Bio.- Med. Eng., 44(3), 136-147. https://doi.org/10.1109/10.554760
  8. Burgess, A.R., Dischinger, P.C., Oquinn, T.D. and Schmidhauser, C.B. (1995), "Lower-extremity injuries in drivers of airbag-equipped automobiles - clinical and crash reconstruction correlations", J. Trauma-Injury Infection Critical Care, 38(4), 509-516. https://doi.org/10.1097/00005373-199504000-00008
  9. Dragcevic, Z., Takeuchi, K., Vecaj, D. and Hursa, A. (2009), "Motorcycle jacket with integrated air bag", Tekstil, 58(7), 346-351.
  10. Guangyi, S., Cheung-Shing, C., Guanglie, Z., Li, W.J., Leong, P.H.W. and Leung, K.S. (2007), "Towards a mobile airbag system using MEMS sensors and embedded intelligence", 634-639.
  11. Hopfield, J.J. (1982), "Neural networks and physical systems with emergent collective computational abilities", Proceedings of the National Academy of Sciences of the United States of America-Biological Sciences, 79(8), 2554-2558.
  12. Hwangbo, M., Kim, J.S. and Kanade, T. (2013), "IMU self-calibration using factorization", IEEE T. Robot., 29(2), 493-507. https://doi.org/10.1109/TRO.2012.2230994
  13. Ishizaka, S., Moromugi, S., Kobayashi, M., Kajihara, H., Koga, K., Sugahara, H., Ishimatsu, T., Kurata, S., Kirkness, J.P., Oi, K. and Ayuse, T. (2014), "A remote-controlled airbag device can improve upper airway collapsibility by producing head elevation with jaw closure in normal subjects under propofol anesthesia", IEEE J. Translational Eng. Health Med., 2, 1-9.
  14. Karantonis, D.M., Narayanan, M.R., Mathie, M., Lovell, N.H. and Celler, B.G. (2006), "Implementation of a real-time human movement classifier using a triaxial accelerometer for ambulatory monitoring", IEEE T. Inform. Technol. Biomed., 10(1), 156-167. https://doi.org/10.1109/TITB.2005.856864
  15. Krzanowski, W.J. (1979), "Between-groups comparison of principal components", J. Am. Statist. Association, 74(367), 703-707. https://doi.org/10.1080/01621459.1979.10481674
  16. Leardini, A., Lullini, G., Giannini, S., Berti, L., Ortolani, M. and Caravaggi, P. (2014), "Validation of the angular measurements of a new inertial-measurement-unit based rehabilitation system: comparison with state-of-the-art gait analysis", J. Neuroeng. Rehabilit., 11, 7. https://doi.org/10.1186/1743-0003-11-7
  17. Li, W. and Wang, J.L. (2013), "Effective adaptive Kalman filter for MEMS-IMU/magnetometers integrated attitude and heading reference systems", J. Navigation, 66(1), 99-113. https://doi.org/10.1017/S0373463312000331
  18. Luinge, H.J. and Veltink, P.H. (2005), "Measuring orientation of human body segments using miniature gyroscopes and accelerometers", Med. Biol. Eng. Comput., 43(2), 273-282. https://doi.org/10.1007/BF02345966
  19. Lund, A.K. and Ferguson, S.A. (1995), "Driver fatalities in 1985-1993 cars with airbags", J. Trauma-Injury Infection Critical Care, 38(4), 469-475. https://doi.org/10.1097/00005373-199504000-00001
  20. Marins, J.L., Xiaoping, Y., Bachmann, E.R., McGhee, R.B. and Zyda, M.J. (2001), "An extended Kalman filter for quaternion-based orientation estimation using MARG sensors", 2003-2011 vol.2004.
  21. Mirzaei, F.M. and Roumeliotis, S.I. (2008), "A Kalman filter-based algorithm for IMU-camera calibration: observability analysis and performance evaluation", IEEE T. Robot., 24(5), 1143-1156. https://doi.org/10.1109/TRO.2008.2004486
  22. Nielsen, M.A. (2015), "Neural networks and deep learning", Determination Press, Canada
  23. Niu, X., Zhang, Q., Li, Y., Cheng, Y. and Shi, C. (2012), "Using inertial sensors of iPhone 4 for car navigation", 555-561.
  24. Preece, S.J., Goulermas, J.Y., Kenney, L.P.J. and Howard, D. (2009), "A comparison of feature extraction methods for the classification of dynamic activities from accelerometer data", IEEE T. Bio.- Med. Eng., 56(3), 871-879. https://doi.org/10.1109/TBME.2008.2006190
  25. Prestero, T.T.J. (2001), Verification of a six-degree of freedom simulation model for the REMUS autonomous underwater vehicle, Massachusetts institute of technology.
  26. Ranganathan, N., Patel, M.I. and Sathyamurthy, R. (2001), "An intelligent system for failure detection and control in an autonomous underwater vehicle", IEEE T. Syst. Man Cy. A, 31(6), 762-767. https://doi.org/10.1109/3468.983434
  27. Roman, C. and Mather, R. (2010), "Autonomous underwater vehicles as tools for deep-submergence archaeology", Proceedings of the Institution of Mechanical Engineers Part M-Journal of Engineering for the Maritime Environment, 224(4), 327-340. https://doi.org/10.1243/14750902JEME202
  28. Ruiz, A.R.J., Granja, F.S., Honorato, J.C.P. and Rosas, J.I.G. (2012), "Accurate pedestrian indoor navigation by tightly coupling foot-mounted IMU and RFID measurements", IEEE T. Instrum. Meas., 61(1), 178-189. https://doi.org/10.1109/TIM.2011.2159317
  29. Rumelhart, D.E., Hinton, G.E. and Williams, R.J. (1986), "Parallel distributed processing: explorations in the microstructure of cognition, vol. 1", (Eds., David, E.R., James, L.M. and Group, C.P.R.), 318-362, MIT Press.
  30. Sabatini, A.M. (2006), "Quaternion-based extended Kalman filter for determining orientation by inertial and magnetic sensing", IEEE T. Bio.- Med. Eng., 53(7), 1346-1356. https://doi.org/10.1109/TBME.2006.875664
  31. Saeedi, S. and El-Sheimy, N. (2015), "Activity recognition using fusion of low-cost sensors on a smartphone for mobile navigation application", Micromachines, 6(8), 1100-1134. https://doi.org/10.3390/mi6081100
  32. Sayyaadi, H. and Ura, T. (1999), "Multi input-multi output system identification of AUV systems by neural network", 201-208.
  33. Tamura, T., Yoshimura, T., Sekine, M., Uchida, M. and Tanaka, O. (2009), "A wearable airbag to prevent fall injuries", IEEE T. Bio.- Med. Eng., 13(6), 910-914.
  34. Titterton, D.H., Weston, J.L. and Institution of Electrical, E. (1997), "Strapdown inertial navigation technology".
  35. Toshiyo, T., Takumi, Y. and Masaki, S. (2008), "A study to demonstrate the use of an air bag device to prevent fall-related injuries", 1-3.
  36. Tuncel, O., Altun, K. and Barshan, B. (2009), "Classifying human leg motions with uniaxial piezoelectric gyroscopes", Sensors, 9(11), 8508-8546. https://doi.org/10.3390/s91108508
  37. Wang, L. (2005), "Air bags for motorcycles", Chem. Eng. News, 83(39), 80-80. https://doi.org/10.1021/cen-v083n039.p080
  38. Wang, Z., Jiang, M., Hu, Y. and Li, H. (2012), "An incremental learning method based on probabilistic neural networks and adjustable fuzzy clustering for human activity recognition by using wearable sensors", IEEE T. Inform. Technol. Biomed., 16(4), 691-699. https://doi.org/10.1109/TITB.2012.2196440
  39. Wenxi, C., Daming, W., Shuxue, D., Michael, C., Hui, W., Shigeru, T. and Naotoshi, T. (2005), "A scalable mobile phone-based system for multiple vital signs monitoring and healthcare", Int. Pervas. Comput., 1(2), 157-163.
  40. Wold, S., Esbensen, K. and Geladi, P. (1987), "Principal component analysis", Chemometr. Intell. Lab., 2(1-3), 37-52. https://doi.org/10.1016/0169-7439(87)80084-9
  41. Yang, J.Y., Wang, J.S. and Chen, Y.P. (2008), "Using acceleration measurements for activity recognition: An effective learning algorithm for constructing neural classifiers", Pattern Recogn. Lett., 29(16), 2213-2220. https://doi.org/10.1016/j.patrec.2008.08.002
  42. Yun, X., Bachmann, E.R., McGhee, R.B., Whalen, R.H., Roberts, R.L., Knapp, R.G., Healey, A.J. and Zyda, M.J. (1999), "Testing and evaluation of an integrated GPS/INS system for small AUV navigation", IEEE J. Oceanic Eng., 24(3), 396-404. https://doi.org/10.1109/48.775301
  43. Zhang, G.Q.P. (2000), "Neural networks for classification: A survey", IEEE T. Syst. Man C, 30(4), 451-462. https://doi.org/10.1109/5326.897072
  44. Zhou, J.W. and Mason, A. (2002), "Communication buses and protocols for sensor networks", Sensors, 2(7), 244-257. https://doi.org/10.3390/s20700244