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In 2010, a donation was given to the University of Victoria Robotics and Mechanisms lab by Roper Industries. It was a Flight Telerobotic Servicer (FTS) Right Finger training tool. This is an electro-hydraulic robotic arm, approximately eight feet long, weighing in excess of four hundred pounds. This arm was designed and built in the late nineteen eighties as part of a program in support of the Space Station Freedom project. The intention of the arm was to assist in the training of astronauts in the use of an end effector which would be mounted at the distal end of the Canadarm©. The end effector would have right and left fingers, as well as a thumb (used for stabilization, not grasping). Unfortunately, the robot did not come with any of the control hardware, software, manuals, or functional descriptions, and the original equipment manufacturers (OEMs) were not able to share any information regarding the nature of the controls. The focus of the present work is to re-animate this arm without additional feedback, operating the arm only by hand-eye control, using currently available electronics and hardware. Also, investigate the absolute position sensors. These are described as near-infinite resolution analog absolute position sensors. Investigation was also conducted on an alternate solution (Vernier Optical Encoder), which was finally were abandoned. Strain-gauge type torque feedback sensors were found to be functional, and can be used without further work on future experimentation. The outcome of the research and assembly is a fully functional electro-hydraulic robotic arm, which is digitally controlled using an XBOX© game controller, using only visual feedback for position. The position sensor work was not as fruitful, with no working position sensors available. The torque feedback sensors are functional, but not utilized in the final work. / Graduate

No description available.

This thesis describes the design of an inexpensive autonomous vehicle system using a small scaled model vehicle. The system is capable of operating in two different modes: telerobotic manual mode and automated driving mode. In telerobotic manual mode, the model vehicle is controlled by a human driver at a stationary remote control station with full-scale steering wheel and gas pedal. The vehicle can either be an unmodified toy remote-control car or a vehicle equipped with wireless radio modem for communication and microcontroller for speed control. In both cases the vehicle also carries a video camera capable of transmitting video images back to the remote control station where they are displayed on a monitor. In automated driving mode, the vehicle's lateral movement is controlled by a lateral control algorithm. The objective of this algorithm is to keep the vehicle in the center of a road. Position and orientation of the vehicle are determined by an image processing algorithm identifying a white middle marker on the road. Two different algorithm for image processing have been designed: one based on the pixel intensity profile and the other on vanishing points in the image plane. For the control algorithm itself, two designs are introduced as well: a simple classical P-control and a control scheme based on H-Infinity. The design and testing of this autonomous vehicle system are performed in the Flexible Low-cost Automated Scaled Highway (FLASH) laboratory at Virginia Tech. / Master of Science

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Semi-autonomous uncrewed aerial vehicles (UAVs) are telerobotic operations by definition where the UAV assumes the role of a telerobot and the human assumes the role of a supervisor. All telerobotic operations are susceptible to time delays due to communication, mechanical, and other constraints. Typically, these delays are small and do not affect the telerobotic operation for most of the tasks. However, for long-distance telerobotic operations like interplanetary rovers, deep underwater vehicles, etc. the delays can be so significant that they can render the entire operation void. This dissertation investigates the use of a novel heterogeneous stereo-vision system to mitigate the effects of time delays in a UAV-based visual interface presented to a human operator. The heterogeneous stereo-vision system consists of an omnidirectional camera and a pan-tilt-zoom camera. Two predictive display setups were developed that modify the delayed video imagery that would otherwise be presented to the operator in a way that provides an almost immediate visual response to the operator's control actions. The usability of the system is determined through human performance testing with and without the predictive algorithms. The results indicate that the predictive algorithm allows more efficient, accurate, and user-friendly operation. The second half of the dissertation deals with improving the performance of the predictive display and expanding the concept of the prediction from a stationary gimbal-camera system to a moving 6 DoF aircraft. Specifically, it talks about a novel extended Kalman filter (EKF)-based nonlinear predictor – the extended Kalman predictor (EKP) – and compares its performance with two linear predictors, the Smith predictor (SP) and the Kalman predictor (KP). This dissertation provides the mathematical formulation of the EKP, as well as the two linear predictors, and describes their use with simulated flight data obtained using a nonlinear motion model for a small, fixed-wing UAV. The EKP performs comparably to the KP when the aircraft motion experiences small perturbations from a nominal trajectory, but the EKP outperforms the KP for larger excursions. The SP performs poorly in every case. / Doctor of Philosophy / Semi-autonomous uncrewed aerial vehicles (UAVs) are telerobotic operations by definition where the aerial vehicle assumes the role of a telerobot and the human assumes the role of a supervisor. This dissertation addresses the challenges posed by time delays in uncrewed aerial vehicle operations, particularly for long-distance operations such as interplanetary exploration and deep-sea missions. It investigates the use of a novel heterogeneous stereo-vision system to mitigate these delays, providing operators with nearly real-time visual feedback. Human performance testing confirms the predictive algorithm allows more efficient, accurate, and user-friendly operation. Additionally, the dissertation presents advancements in the predictive display performance for moving UAVs with six degrees of freedom. It introduces a novel extended Kalman predictor and compares it to traditional linear predictors like the Smith predictor and the Kalman predictor using simulated flight data. The extended Kalman predictor demonstrates superior performance for larger deviations from trajectory, highlighting its effectiveness in predicting the motion of an aircraft when there are time delays present.