The exploration of oceans and sea beds is being made increasingly possible through the development of Autonomous Underwater Vehicles (AUVs). This is an activity that concerns the marine community and it must confront the existence of notable challenges. These include, for example, mining minerals, inspecting pipeline and mapping oceans, sampling in contaminated water. Also, there has been another growing interest for security forces in precluding submarines or intruders from a beach or harbour entrance as well as hunting shallow water mines. However, an automatic detecting and tracking system is the first and foremost element for an AUV or an aqueous surveillance network. Since accurate surrounding information is essential in order to manoeuvre the AUV efficiently and economically, while corrupt information can jeopardize an entire mission. By extracting the space information form sensors, an AUV can achieve the localisation and mapping which are currently two primary concerns in the robotics research. Meanwhile, such information will provide a fundament of protection for surface vessels or troops, harbour infrastructure and oil plant against the enemy and terrorism. Acoustic sensors are commonly used to detect and position underwater obstacles, suspicious objects or to map the surroundings because sound waves can propagate more appreciable distances than electromagnetic and optical energy in the water. The measurements from these sensors, however, are always bound up with noises and errors. Various underwater activities may further pollute sound signals and then threaten the AUV navigation process. To simplify the detection procedure, some researchers make use of acoustic beacons or apparent obstructions (such as rocks, concrete walls) because they have distinctive characteristics. Point or line features are extracted from the acoustic signals or images for localization and mapping purposes. The long propagation range of sound waves can present new problems when acoustic sensors operate in confined environments, such as water tanks, rivers and harbours. The multiple reflections will be recorded by the sensor and result in false alarms. Furthermore, with advances in manufacturing techniques, the downsizing in marine explosive ordnances is progressing significantly, making it more difficult to discriminate between surface reflections and explosive ordnances. Finally, under the consideration of cost effectiveness, a mechanically scanned sonar has been introduced for the AUV in this research. However, the sensor beam cannot cover a large region simultaneously and a moving object may be distorted in the acoustic image because of the relatively low scanning speed. Due to such distortions in the data flows, objects may be indistinguishable from random noise or reverberation in acoustic images. The research presented here addresses the afore-mentioned problems relating to the theme of automatic detection from acoustic images. It is concerned with the detection and tracking of small underwater objects in order to protect autonomous underwater vehicles using sonar (SOund Navigation and Range). In the present study, these vehicles operated in laboratory water tanks or natural river environments. This research made use of self provided analytical studies that differentiated between reverberation and real object echoes. Detections were achieved automatically by using signal and image processing techniques. This research consists of three important and linked strategies. Firstly, a simple and fast reverberation suppression filter was provided, based on the understanding of the mechanism of the sonar sensor. Secondly, a robust detection system was developed to perceive small suspended obstacles in the water. Thirdly and finally, arc features were successfully extracted from the acoustic images and mathematical maps were generated from those features. The majority of experiments were derived from the elliptical water tank and the River Torrens, Adelaide, South Australia. For this project, a sequence of sonar images was taken from the same sonar location in the elliptical water tank. Further, a sequence of sonar images was taken from a sequence of sonar locations in the natural river. They provided different data sets for the assessment and evaluation of self developed algorithms. Results shown in this thesis confirm the favourable outcomes of the investigation and applied methodology. / http://proxy.library.adelaide.edu.au/login?url= http://library.adelaide.edu.au/cgi-bin/Pwebrecon.cgi?BBID=1454839 / Thesis (M.Eng.Sc.) -- University of Adelaide, School of Mechanical Engineering, 2010
| Identifer | oai:union.ndltd.org:ADTP/291052 |
| Date | January 2010 |
| Creators | Zhao, Shi |
| Source Sets | Australiasian Digital Theses Program |
| Detected Language | English |
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