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Underwater vehicles that use differential thrust for surge and yaw motion control have the advantage of increased maneuverability. Unfortunately, such vehicles usually don’t have thrusters/actuators to control the lateral movements. Hence, they fall into the underactuated vehicle category. The goal of the work in this thesis is to develop an autonomous control system for a differential thrust underwater remotely operated vehicle (ROV) to track predefined position trajectories. This is challenging because the mathematical model for underwater vehicles is highly nonlinear and the environmental disturbances are usually strong and unpredictable. These factors make the design of the control system very difficult. In this work, we use the VideoRay Pro III micro ROV as the test platform, on which we design an autonomous control system. We first present the development and analysis of a hydrodynamic model of the VideoRay Pro III using both analytical and experimental approaches. Based on this model, a state estimator is then designed using the unscented Kalman filter, which yields better estimates of the system states and their uncertainty level in a highly nonlinear system than the commonly used extended Kalman filter. In the controller design, the integrator backstepping technique is used to achieve a Lyapunov stable trajectory tracking controller based on the work by A. P. Aguiar et al. We extended their work by further considering the quadratic drag terms in the vehicle’s hydrodynamic model. The sliding mode control is used to design the bearing and depth controller. Finally, the autonomous control system is validated by simulation and experimental tests. It is shown that the VideoRay Pro III is able to track the predefined trajectory within error range of 0.5 meters.

Underwater vehicles that use differential thrust for surge and yaw motion control have the advantage of increased maneuverability. Unfortunately, such vehicles usually don’t have thrusters/actuators to control the lateral movements. Hence, they fall into the underactuated vehicle category. The goal of the work in this thesis is to develop an autonomous control system for a differential thrust underwater remotely operated vehicle (ROV) to track predefined position trajectories. This is challenging because the mathematical model for underwater vehicles is highly nonlinear and the environmental disturbances are usually strong and unpredictable. These factors make the design of the control system very difficult. In this work, we use the VideoRay Pro III micro ROV as the test platform, on which we design an autonomous control system. We first present the development and analysis of a hydrodynamic model of the VideoRay Pro III using both analytical and experimental approaches. Based on this model, a state estimator is then designed using the unscented Kalman filter, which yields better estimates of the system states and their uncertainty level in a highly nonlinear system than the commonly used extended Kalman filter. In the controller design, the integrator backstepping technique is used to achieve a Lyapunov stable trajectory tracking controller based on the work by A. P. Aguiar et al. We extended their work by further considering the quadratic drag terms in the vehicle’s hydrodynamic model. The sliding mode control is used to design the bearing and depth controller. Finally, the autonomous control system is validated by simulation and experimental tests. It is shown that the VideoRay Pro III is able to track the predefined trajectory within error range of 0.5 meters.

In order for a mobile robot to complete its task it must be able to plan and follow a trajectory. Depending on the environment, it may also be necessary to follow a given velocity profile. This is known as temporal planning. Temporal planning can be used to minimize time of motion and to avoid moving obstacles. For example, assuming the mobile robot is an intelligent wheelchair, it must follow a prescribed path (sidewalk, hospital corridor) while following a strict speed limit (slowing down for pedestrians, cars). Computing a realistic velocity profile for a mobile robot is a challenging task due to a large number of kinematic and dynamic constraints that are involved. Unlike prior works which performed temporal planning in a 2-dimensional environment, this thesis presents a new temporal planning algorithm in a 3-dimensional environment. This algorithm is implemented on a wheeled mobile robot that is to be used in a healthcare setting. The path planning stage is accomplished by using cubic spline functions. A rudimentary trajectory is created by assigning an arbitrary time to each segment of the path. This trajectory is made feasible by applying a number of constraints and using a linear scaling technique. When a velocity profile is provided, a non-linear time scaling technique is used to fit the robots center linear velocity to the specified velocity. A method for avoiding moving obstacles is also implemented. Both simulation and experimental results for the wheeled mobile robot are presented. These results show good agreement with each other. For both simulation and experimentation, six different examples of paths in the Engineering Building of the University of Saskatchewan, were used. Experiments were performed using the PowerBot mobile robot in the robotics lab at the University of Saskatchewan.

This study investigated the crop density in a New Holland BB960 (branch of CNH Global N.V.) large square baler as examined by crop trajectory from the precompression room to the bale chamber. This study also examined both the top and bottom plunger pressures and critical factors affecting the final top and bottom bale densities.<p>The crop trajectories (wad of crop) were measured using a high-speed camera from the side of the baler through viewing windows. The viewing windows were divided into four regions for determining the crop displacement, velocity and acceleration. Crop strain was used to evaluate the potential change in density of the crop before being compressed by the plunger. Generally, the vertical crop strain was found to be higher in the top half of the bale compared to the bottom. <p>Average strain values for side measurements were 12.8% for the top and 2.1% for the bottom. Plunger pressures were measured to compare peak pressures between the top and bottom halves of each compressed wad of crop, and to develop pressure profiles based on the plungers position. Results of comparing the mean peak plunger pressures between the top and bottom locations indicated the mean pressures were significantly higher at the top location with the exception of one particular setting. Resulting pressure profile graphs aided in qualitatively describing the compression process for both top and bottom locations.<p>A stepwise regression model was developed to examine the difference in material quantity in the top half of the bale compared to the bottom, based on bale weights. The model indicated that flake setting, stuffer ratio and number of flakes had the greatest effect on maintaining consistent bale density by comparing top to bottom halves of each bale. The R2 (coefficient of determination) value for the developed model was of 59.9%. The R2 was low although could be accounted for due to the limited number of data points in the developed model.

Supercavitating vehicles are characterized by substantially reduced hydrodynamic drag with respect to fully wetted underwater vehicles. Drag is localized at the nose of the vehicle, where a cavitator generates a cavity that completely envelops the body. This causes the center of pressure to be always ahead of the center of mass, thus violating a fundamental principle of hydrodynamic stability. This unique loading configuration, the complex and non-linear nature of the interaction forces between vehicle and cavity, and the unsteady behavior of the cavity itself make the control and maneuvering of supercavitating vehicles particularly challenging. This study represents an effort towards the evaluation of optimal trajectories for this class of underwater vehicles, which often need to operate in unsteady regimes and near the boundaries of the flight envelope. Flight trajectories and maneuvering strategies for supercavitating vehicles are here obtained through the solution of an optimal control problem. Given a cost function and general constraints and bounds on states and controls, the solution of the optimal control problem yields the control time histories that maneuver the vehicle according to a desired strategy, together with the associated flight path. The optimal control problem is solved using the direct transcription method, which does not require the derivation of the equations of optimal control and leads to the solution of a discrete parameter optimization problem. Examples of maneuvers and resulting trajectories are given to demonstrate the effectiveness of the proposed methodology and the generality of the formulation.

The utilization of micro-scale technologies is limited by the speed of their manufacture. Micro-milling is one particular technology used to manufacture micro-scale parts which could benefit extensively from an increase in throughput. Micro-milling involves a rotating cutter slightly thicker than a human hair removing material while spinning at speeds often over one hundred thousand revolutions per minute. An obvious solution to the throughput bottleneck is to move current micro-mills faster using existing technology; however, simply increasing the operational speed of existing micro-mills will lead to vibration and trajectory following problems. If a micro-mill cannot be positioned precisely, then part tolerances cannot be maintained. Thus any increase in throughput would be counterproductive in terms of overall performance. This dissertation presents techniques to improve the performance of micro-mills, as well as other flexible machines. Theses improvements are possible through the utilization of the vibration suppression scheme of input shaping. By thoughtfully altering the commands sent to flexible systems, their vibration can be significantly reduced. Input shaping was effectively applied to an existing micro-mill, which improved part tolerances and increased operational speed. However, at extremely high speeds, traditional input shaping is not effective at following complicated trajectories. Therefore, new input shaping techniques were developed specifically for trajectory tracking of extremely fast motions on micro-mills and other flexible systems. Often machines cannot achieve these high speeds while maintaining their accuracy because of the mechanical design of the machines themselves. If the mechanical design of micro-mills and other machines consider flexible and lightweight design alternatives that utilize input shaping for vibration suppression instead of stiff and heavy designs, then faster machine motion will be possible. By considering input shaped flexible systems as part of traditional mechanical design processes, these flexible solutions allow vast performance improvement. Specifically, embodiment design can be improved through consideration of input shaping performance requirements. Through these advancements, this dissertation improves the design, control, and performance of micro-mills and other flexible machines.

The main objective of this thesis is to present a new path planning scheme for solving the shortest (collision-free) path problem for an agent (vehicle) operating in a partially known environment. We present two novel algorithms to solve the planning problem. For both of these approaches we assume that the agent has detailed knowledge of the environment and the obstacles only in the vicinity of its current position. Far away obstacles or the final destination are only partially known and may even change dynamically at each instant of time. The path planning scheme is based on information gathered on-line by the available on-board sensor devices. The solution minimizes the total length of the path with respect to a metric that includes actual path length, along with a risk-induced metric. In order to obtain an approximation of the whole configuration space at different levels of fidelity we use a wavelet approximation scheme. In the first proposed algorithm, the path-planning problem is solved using a multi-resolution cell decomposition of the environment obtained from the wavelet transform. In the second algorithm, we extend the results of the the first one by using the multiresolution representation of the environment in conjunction with a conformal mapping to polar coordinates. By performing the cell decomposition in polar coordinates, we can naturally incorporate sector-like cells that are adapted to the data representation collected by the on-board sensor devices.

Inverse problems associated with reservoir characterization are typically underdetermined and often have difficulties associated with stability and convergence of the solution. A common approach to address this issue is through the introduction of prior constraints, regularization or reparameterization to reduce the number of estimated parameters. We propose a dual scale approach to production data integration that relies on a combination of coarse-scale and fine-scale inversions while preserving the essential features of the geologic model. To begin with, we sequentially coarsen the fine-scale geological model by grouping layers in such a way that the heterogeneity measure of an appropriately defined 'static' property is minimized within the layers and maximized between the layers. Our coarsening algorithm results in a non-uniform coarsening of the geologic model with minimal loss of heterogeneity and the ?optimal? number of layers is determined based on a bias-variance trade-off criterion. The coarse-scale model is then updated using production data via a generalized travel time inversion. The coarse-scale inversion proceeds much faster compared to a direct fine-scale inversion because of the significantly reduced parameter space. Furthermore, the iterative minimization is much more effective because at the larger scales there are fewer local minima and those tend to be farther apart. At the end of the coarse-scale inversion, a fine-scale inversion may be carried out, if needed. This constitutes the outer iteration in the overall algorithm. The fine-scale inversion is carried out only if the data misfit is deemed to be unsatisfactory. We propose a fast and robust approach to calibrating geologic models by transient pressure data using a trajectory-based approach that based on a high frequency asymptotic expansion of the diffusivity equation. The trajectory or ray-based methods are routinely used in seismic tomography. In this work, we investigate seismic rays and compare them with streamlines. We then examine the applicability of streamline-based methods for transient pressure data inversion. Specifically, the high frequency asymptotic approach allows us to analytically compute the sensitivity of the pressure responses with respect to reservoir properties such as porosity and permeability. It facilitates a very efficient methodology for the integration of pressure data into geologic models.

In recent years, with the popularization of the mobile devices, more and more location-based applications have been developed. As a result, location data of various objects is widely available. Identifying object groups that tend to move together is an emerging research topic. Existing approaches for identifying mobile group patterns assume the existence of raw location data which records a given object¡¦s position at every equal-spaced time point. However, a moving object may become disconnected voluntarily or involuntarily from time to time, and thus this assumption may not always valid. In this research, we describe the locations of moving object as a (non-continuous) trajectory function. Based on the new model, we re-define the mobile group mining problem and develop efficient algorithms for mining mobile groups. The proposed algorithms are evaluated via synthetic data generated by IBM City Simulator.