Design and Development of an Autonomous Underwater Vehicle (AUV-FKEUTeM)

Abstract—This paper describes a design and development of an Autonomous Underwater Vehicle (AUV). AUV are robotic submarines that are a part of the emerging field of autonomous and unmanned vehicles. This project shows the design implementation of an AUV as a test bed platform for a variety of research in underwater technologies especially involving small-scale, surface water and low-cost underwater robots. The general design and its consideration are well discussed in this paper. The AUV prototype has been developed by SolidWork. It will have a fixed mechanical system and body, having a modular electronic system that allows development of various controllers. The controller and motors has been tested in small scale surface water and the result is encouraging. Some of the factor affecting the AUV performance is also elaborated for future research in this area

Faster upper body pose recognition and estimation using compute unified device architecture

>Magister Scientiae - MScThe SASL project is in the process of developing a machine translation system that can translate fully-fledged phrases between SASL and English in real-time. To-date, several systems have been developed by the project focusing on facial expression, hand shape, hand motion, hand orientation and hand location recognition and estimation. Achmed developed a highly accurate upper body pose recognition and estimation system. The system is capable of recognizing and estimating the location of the arms from a twodimensional video captured from a monocular view at an accuracy of 88%. The system operates at well below real-time speeds. This research aims to investigate the use of optimizations and parallel processing techniques using the CUDA framework on Achmed’s algorithm to achieve real-time upper body pose recognition and estimation. A detailed analysis of Achmed’s algorithm identified potential improvements to the algorithm. Are- implementation of Achmed’s algorithm on the CUDA framework, coupled with these improvements culminated in an enhanced upper body pose recognition and estimation system that operates in real-time with an increased accuracy

AN INTELLIGENT NAVIGATION SYSTEM FOR AN AUTONOMOUS UNDERWATER VEHICLE

The work in this thesis concerns with the development of a novel multisensor data fusion (MSDF) technique, which combines synergistically Kalman filtering, fuzzy logic and genetic algorithm approaches, aimed to enhance the accuracy of an autonomous underwater vehicle (AUV) navigation system, formed by an integration of global positioning system and inertial navigation system (GPS/INS). The Kalman filter has been a popular method for integrating the data produced by the GPS and INS to provide optimal estimates of AUVs position and attitude. In this thesis, a sequential use of a linear Kalman filter and extended Kalman filter is proposed. The former is used to fuse the data from a variety of INS sensors whose output is used as an input to the later where integration with GPS data takes place. The use of an adaptation scheme based on fuzzy logic approaches to cope with the divergence problem caused by the insufficiently known a priori filter statistics is also explored. The choice of fuzzy membership functions for the adaptation scheme is first carried out using a heuristic approach. Single objective and multiobjective genetic algorithm techniques are then used to optimize the parameters of the membership functions with respect to a certain performance criteria in order to improve the overall accuracy of the integrated navigation system. Results are presented that show that the proposed algorithms can provide a significant improvement in the overall navigation performance of an autonomous underwater vehicle navigation. The proposed technique is known to be the first method used in relation to AUV navigation technology and is thus considered as a major contribution thereof.J&S Marine Ltd., Qinetiq, Subsea 7 and South West Water PL

Upper body pose recognition and estimation towards the translation of South African sign language

Masters of ScienceRecognising and estimating gestures is a fundamental aspect towards translating from a sign language to a spoken language. It is a challenging problem and at the same time, a growing phenomenon in Computer Vision. This thesis presents two approaches, an example-based and a learning-based approach, for performing integrated detection, segmentation and 3D estimation of the human upper body from a single camera view. It investigates whether an upper body pose can be estimated from a database of exemplars with labelled poses. It also investigates whether an upper body pose can be estimated using skin feature extraction, Support Vector Machines (SVM) and a 3D human body model. The example-based and learning-based approaches obtained success rates of 64% and 88%, respectively. An analysis of the two approaches have shown that, although the learning-based system generally performs better than the example-based system, both approaches are suitable to recognise and estimate upper body poses in a South African sign language recognition and translation system.South Afric

An Adaptive Video Event Mining System For An Autonomous Underwater Vehicle

This paper presents the results obtained in the development of an adaptive architecture for automated event discovery in sub-sea recorded video footage. The Video Marking System that has been built is the first step of the development of a vision system for an autonomous underwater vehicle. The principal aim of our work is to build an adaptive architecture that provides the vision system with the intelligence and robustness required to deal with the great variability found in underwater video. Different image processing techniques are embedded within the adaptive architecture; all of them running in parallel, adaptively parameterized, and assigned a confidence level by a voting system. Utilizing a simplified world model, the various outputs from the active image processing approaches are combined into an improved event description