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.
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