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Improving the kinematic control of robots with computer vision
Fallon, J. Barry
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This dissertation describes the development and application of a computer vision system for improving the performance of robots. The vision-based approach determines position and orientation (pose) parameters more directly than conventional approaches that are based on kinematics and joint feedback. Traditional robot control systems rely on kinematic models, measured joint variables, knowledge of objects in the workspace, and the calibrated robot base pose to correctly position and orient a tool. Since this conventional approach involves a large number of parameters, unacceptable pose errors may accumulate. In contrast, the vision system approach uses images from a tool-mounted camera and geometric knowledge of objects in the workspace to accurately track and determine the end-effector pose. This approach is advantageous because the camera directly observes the parameters of interest (position and orientation of the robot tool with respect to the work-piece) during the positioning process. The vision approach is verified and its utility demonstrated by increasing the automation and accuracy of computer controlled robots used in the nuclear service industry. The overall solution strategy involves tracking and pose determination. Tracking is used as a coarse positioner and to verify the toolhead position prior to performing crucial servicing operations. Pose determination is used to calibrate the base location of the robot, verify the tool pose for insertions, and compute a precise correction if necessary. The major contributions of this work lie within its comprehensive treatment, which begins with theoretical modeling and follows through to the details of application. Specific contributions are made in the areas of robotics, image processing, calibration, tracking, pose determination, kinematic control strategies, and nuclear service operations. Performance results from laboratory experiments and actual field testing have been encouraging. The vision-based strategy offers robustness to the conventional error stackups encountered in robotics and promises to improve the accuracy, flexibility, and cost of both specialized and general-purpose robotic systems.
- Doctoral Dissertations