This thesis examines the problem of localising an Autonomous Guided Vehicle (AGV) travelling in an unknown environment. In this problem, the AGV faces the dual task of modeling the environment and simultaneously localising its position within it. The Simultaneous Localisation and Map Building (SLAM) problem is currently one of the most important goals of AGV research. Solving this problem would allow anAGV to be deployed easily, with very little initial preparation. The AGV would also be exible and able to cope with modi cations in the environment. A solution to the SLAM problem would enable an AGV to would be truly \autonomous." The thesis examines the SLAM problem from an estimation theoretic point ofview. The estimation approach provides a rigorous framework for the analysis and has also proven to be successful in actual applications. The most signi cant contribution of this thesis is to provide, for the rst time, a detailed development of the theory of the SLAM problem. It is shown that correlations arise between errors in the vehicle and the map estimates, and these correlations are identi ed as fundamentally important to the solution of the SLAM problem. It is demonstrated that ignoring these correlations results in the loss of the fundamental structure of the SLAM problem and leads to inconsistency in map

Inertial measurement components, which sense either acceleration or angular rate, are being embedded into common user interface devices more frequently as their cost continues to drop dramatically. These devices hold a number of advantages over other sensing technologies: they measure relevant parameters for human interfaces and can easily be embedded into wireless, mobile platforms. The work in this dissertation demonstrates that inertial measurement can be used to acquire rich data about human gestures, that we can derive efficient algorithms for using this data in gesture recognition, and that the concept of a parameterized atomic gesture recognition has merit. Further we show that a framework combining these three levels of description can be easily used by designers to create robust applications.

This paper addresses the trajectory tracking problem for the low-speed maneuvering of fully actuated underwater vehicles. It is organized as follows: First a brief review of previously reported control studies and plant models is presented. Second, an experimentally validated plant model for the Johns Hopkins University Remotely Operated Underwater Vehicle (JHUROV) is reviewed. Third, the stability of linear PD control and a family of model-based controllers is examined analytically and demonstrated with numerical simulations. Finally we report results from experimental trials comparing the performance of these controllers over a wide range of operating conditions. The experimental results corroborate the analytical predictions that the model-based controllers outperform PD control over a wide range of operating conditions. The exactly linearizing model-based controller is outperformed by its non exactly linearizing counterpart. The adaptive controllers are shown to provide reasonable online plant parameter estimates, and velocity and position tracking consistent with theoretical predictions --- providing good velocity tracking and, with the appropriate parameter update law, position tracking. The effects of reference trajectory, "bad" model parameters, feedback gains, adaptation gains, and thruster saturation are experimentally evaluated. To the best of our knowledge, this is the first reported comparative experimental study of this class of model-based controllers for underwater vehicles.

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sized solid state accelerometer in the absence of absolute positioning information is performed in this paper. Acceleration signal outputted by the sensor is doubly integrated with time that yields the travel distance. Due to bias and Thermal noise of the accelerometer which is accumulate in time, Kalman filter is need to be design to increase the accuracy of position estimation system. A modeling for bias of solid state accelerometer ADXL250 QCG without aiding sensor evaluated in this paper. During the preceding time instants, the position information becomes increasingly untrustworthy and the validity of the position updates decays. So, we used a smoother algorithm based Kalman filter for better estimation accuracy in position estimation. Result show smoothing algorithm increase the accuracy of position estimate for short time duration.