Modern externally powered upper-body prostheses are conventionally actuated by electric servomotors. Although these motors achieve reasonable kinematic performance, they are voluminous and heavy. Deterring factors such as these lead to a substantial proportion of upper extremity amputees avoiding the use of their prostheses. Therefore, it is apparent that there exists a need for functional prosthetic devices that are compact and light-weight. The realization of such a device requires an alternative actuation technology, and biological inspiration suggests that tendon based systems are advantageous. Shape memory alloys are a type of smart material that exhibit an actuation mechanism resembling the biological equivalent. As such, shape memory alloy enabled devices promise to be of major importance in the future of dexterous robotics, and to prosthetics in particular. This thesis investigates the issues surrounding the practical application of shape memory alloys as artificial muscles in a three fingered robot hand. First the function of the human hand and the kinematic requirements for manipulation are reviewed. An overview of artificial hands is provided, followed by a discussion on shape memory alloys focused on the unique phenomena of the shape memory effect. Second, the forward and inverse kinematics of the artificial finger are established in order to relate the desired finger tip contact point to the required joint angles. This is followed by the design of the requisite instrumentation and control systems. Due to the highly nonlinear nature of both the SMA and the robot hand, alternative control approaches such as neural networks are reviewed. Finally, a large-strain SMA actuator is proposed and the concepts explored herein are applied to the design, manufacture, and evaluation of an SMA actuated robotic hand.

In this thesis, we develop a new language for genetic programming, specifically designed for high-level controller scripting on mobile robots. We then test this language against previous conventions on the Robots Everywhere Antbot platform. We develop a genetic programming framework using Python and the new language, to create corridor-following programs in a simple simulation. Using this framework, we conduct a variety of experiments to find good parameters and techniques that apply to this new language, which can evolve the best controllers. Our results suggest that hierarchical GP using a measure of elitism is likely the best solution, and that the new language is very capable of evolving solutions with a high degree of robustness and generality.

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<p> The past decade has seen the rapid development and deployment of unmanned systems throughout the world in both civilian and military applications. Significant development has been led by the Department of Defense (DoD), which has sought to develop and field military systems, such as unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs), with elevated levels of autonomy to accomplish their mission with reduced funding and manpower. As their role increases, such systems must be able to adapt and learn, and make nondeterministic decisions. Current unmanned systems exhibit minimal autonomous behaviors. As their autonomy increases and their behaviors become more intelligent (adapting and learning from previous experiences), the state space for their behaviors becomes non deterministic or intractably complex. </p><p> Consequently, fielding such systems requires extensive testing and evaluation, as well as verification and validation to determine a system&rsquo;s performance and the acceptable level of risk to make it releasable &ndash; a challenging task. To address this, I apply a novel systems perspective to develop a model-based framework to predict future system performance based on the complexity of the operating environment using newly introduced complexity measures and learned costs. Herein I consider an autonomous military ground robot navigating in complex off-road environments. Using my model and data from Defense Advanced Research Projects Agency (DARPA)-led experiments, I demonstrate the accuracy with which my model can predict system performance and then validate my model against other experimental results.</p>

<p> Field mapping and estimation become a challenging problem, with their various applications on non-linear estimation, geolocation, and positioning systems. In this research, we develop novel algorithms based on interval analysis and introduce a solution for autonomous map construction, field mapping, geolocation, and simultaneous localization and mapping (SLAM), providing applications on indoor geolocation and other potential areas. </p><p> Generally, the localization algorithm includes a quasi-state estimation and a dynamic estimation. Quasi-static estimation collects each single measurement and give a group of estimation intervals on the pre-constructed field map. Results from quasi-static estimation are processed into the dynamic estimation algorithm, having properties of removing redundant intervals while keep the best estimation results. Sizes of estimation from quasi-static estimation are proved to be related to the resolution of the map and the quality of the sensor. Based on quasi-state estimation algorithm, we develop an algorithm to fuse different type of measurements and discuss the condition when this algorithm an be applied effectively. </p><p> Having theoretical guarantees, we apply these algorithms to augment the accuracy of cell phone geolocation by taking advantage of local variations of magnetic intensity. Thus, the sources of disturbances to magnetometer readings caused indoors are effectively used as beacons for localization. We construct a magnetic intensity map for an indoor environment by collecting magnetic field data over each floor tile. We then test the algorithms without position initialization and obtain indoor geolocation to within 2m while slowly walking over a complex path of 80 meters. The geolocation errors are smaller in the vicinity of large magnetic disturbances. After fusing the magnetometer measurement with inertial measurements on the cell phone, the algorithm yields even smaller geolocation errors of under 50cm for a moving user. </p><p> The map construction and geolocation algorithms are then extended to realize the SLAM, with hierarchical structure of estimation update and localization update. When a new user steps into a random map, the dead reckoning algorithm with assistance of IMU and Kalman filter provides initial estimation of position on the map, which coordinates the corresponding reading of magnetic field intensity as well as all other sources such as WiFi received signal strength (RSS), to construct an initial map. Based on the initial map, we then apply the localization algorithm to estimate new geolocations consequently and fuse the estimation intervals both from IMU and from crowd-sourced field maps to reduce the estimation size and eventually revise the map as well as the geolocation. </p><p> In this research, we have built up mathematical model and developed mathematical solutions with corresponding theories and proofs. Our theoretical results connect geolocation accuracy to combinations of sensor and map properties. </p>

<p> Unmanned aerial vehicles are quickly becoming a significant and permanent feature in today's world of aviation. Amongst the various types of UAVs, a popular type is the quadcopter. Also referred to as a quadrotor, this rotor craft's defining feature is that it has four propellers. While its use is common in the hobbyist community, this aircraft's use within industry is blooming. </p><p> Presented are the efforts to design and implement a controller for a BeagleBone based quadcopter. As part of this effort, characteristics of the quadcopter were experimentally determined. These characteristics consist of physical properties of the quadcopter, such as the moments of inertia, the motor performance characteristics, and variance within its sensors. A model was then created and implemented within a MATLAB environment to simulate the flight of the quadcopter. With a simulated environment created, a controller was designed to control the flight of the quadcopter and a Kalman Filter was implemented to filter a sensor input. These designs were then verified in the simulated environment.</p>