Multiple obstacles detection using fuzzy interface system for AUV navigation in natural water

The detection of small moving targets, such as hazardous objects, is a challenging problem for sonar surveillance used by maritime robots' navigation in shallow water. This phenomenon is due to the ambient sea noise and reverberation. This paper presents a fuzzy algorithm to automate detection of underwater multiple objects using Mechanical Scanned Imaging Sonar (MSIS). Returned sound waves were evaluated by a fuzzy interface system (FIS). Two parameters: the Pulse length and Maximum intensity are selected as linguistic variables for the fuzzy system. The data used in this paper were collected in a natural river which is about 3 meters depth. Results demonstrate good, reliable and fast performances of the proposed algorithm.

[1]  浅居 喜代治,et al.  Fuzzy systems for information processing , 1995 .

[2]  Stefan B. Williams,et al.  Autonomous underwater simultaneous localisation and map building , 2000, Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065).

[3]  Lotfi A. Zadeh,et al.  Outline of a New Approach to the Analysis of Complex Systems and Decision Processes , 1973, IEEE Trans. Syst. Man Cybern..

[4]  Pere Ridao,et al.  Underwater SLAM in man‐made structured environments , 2008, J. Field Robotics.

[5]  Ashley David Waite,et al.  Sonar for Practising Engineers , 1996 .

[6]  Tien-Fu Lu,et al.  Automatic object detection for AUV navigation using imaging sonar within confined environments , 2009, 2009 4th IEEE Conference on Industrial Electronics and Applications.

[7]  G. Jourdain,et al.  "Principal component inverse" algorithm for detection in the presence of reverberation , 2002 .

[8]  W. Pedrycz.  Why triangular membership functions , 1994 .

[9]  Pere Ridao,et al.  Underwater SLAM in man-made structured environments , 2008 .

[10]  Arthur B. Baggeroer,et al.  An overview of matched field methods in ocean acoustics , 1993 .

[11]  Tien-Fu Lu,et al.  Automatic object detection and diagnosis for AUV operation using underwater imaging sonar , 2008, 2008 3rd IEEE Conference on Industrial Electronics and Applications.

[12]  Ilona Jagielska,et al.  An investigation into the application of neural networks, fuzzy logic, genetic algorithms, and rough sets to automated knowledge acquisition for classification problems , 1999, Neurocomputing.

[13]  D. Schleher.  Mti and Pulsed Doppler Radar , 1999 .

[14]  S. Williams,et al.  SLAM using natural features in an underwater environment , 2004, ICARCV 2004 8th Control, Automation, Robotics and Vision Conference, 2004..

[15]  Chungyong Lee,et al.  Adaptive processing technique for enhanced CFAR detecting performance in active sonar systems , 2000, IEEE Trans. Aerosp. Electron. Syst..

[16]  Tien-Fu Lu,et al.  Automated mapping and localization for autonomous underwater vehicle's navigation using imaging sonar , 2009 .