Camera-aided self-calibration of robot manipulators

Robot calibration is a software-based accuracy enhancement process. It is normally implemented in a well-controlled environment. However, for a system that function in a natural environment, it is desirable that the system is capable of performing a calibration task without any external expensive calibration apparatus and elaborate setups, i.e., system self-calibration. Vision systems have become standard automation components as cameras are normally integral components of most robotic manipulators. This research focuses on camera-aided robot self-calibration.; Unlike classical vision-based robot calibration methods, which need both image coordinates and precise 3D world coordinates of calibration points, the self-calibration algorithms proposed in the dissertation only require a sequence of images of objects in a natural environment and a known scale. A new robot self-calibration algorithm using a known scale at every camera pose is proposed in the dissertation. It has been known that, the extrinsic parameters of the camera along with its intrinsic parameters can be obtained up to a scale factor by using the corresponding image points of objects due to the factor that the system is inherently under-determined. Now, if the camera is treated as the tool of the robot, one is then able to compute the corresponding robot pose directly from the camera, extrinsic parameters once the scale factor is available. This scale factor, which changes from one camera pose to another, can be uniquely determined from the known scale at each robot pose.; The limitation of the above approach for robot self-calibration is that the known scale has to be utilized at every robot measurement pose. A new algorithm is proposed by using the known scale only once in the entire self-calibration procedure. The prerequisite of this calibration algorithm is a carefully planned optimal measurement trajectory for the estimation of the scale factor. By taking into consideration of the observability of the link error parameters, the problem can be formulated either as a constrained or a weighted minimization problem that can be solved by an optimization procedure.; A new method for camera lens distortion calibration by using only point correspondences of two images without knowing the camera movement is described in the dissertation. The images for robot calibration can be shared for lens distortion coefficient calibration. This characteristic saves the user much effort in collecting image data and makes it possible to conduct a robot calibration task on line.; Extensive simulations and experiment studies on a PUMA 560 robot at FAU Robotics Center reveal the convenience and effectiveness of the proposed self-calibration approaches. Compared to other robot calibration algorithms, the proposed algorithms in this dissertation are more autonomous and can be applied to a natural environment.