Robots--Calibration

In this thesis work, techniques developed in the science of genetic computing is applied to solve the problem of planning a robot calibration experiment. Robot calibration is a process by the robot accuracy is enhanced through modification of its control software. The selection of robot measurement configurations is an important element in successfully completing a robot calibration experiment. A classical genetic algorithm is first customized for a type of robot measurement configuration selection problem in which the robot workspace constraints are defined in terms of robot joint limits. The genetic parameters are tuned in a systematic way to greatly enhance the performance of the algorithm. A recruit-oriented genetic algorithm is then proposed, together with new genetic operators. Examples are also given to illustrate the concepts of this new genetic algorithm. This new algorithm is aimed at solving another type of configuration selection problem, in which not all points in the robot workspace are measurable by an external measuring device. Extensive simulation studies are conducted for both classical and recruit-oriented genetic algorithms, to examine the effectiveness of these algorithms.

The focus of this thesis is the kinematic calibration of a SCARA arm with a hand-mounted camera. Kinematic calibration can greatly improve the accuracy of SCARA arms, which are widely used in electronic assembly lines. Vision-based robot calibration has the potential of being a fast, nonintrusive, low-cost, and autonomous approach. In this thesis, we apply a vision-based technique to calibrate SCARA arms. The robot under investigation is modeled by the modified complete and parametrically continuous model. By repeatedly calibrating the camera, the pose of the robot end-effector are collected at various robot measurement configurations. A least squares technique is then applied to estimate the geometric error parameters of the SCARA arm using the measured robot poses. In order to improve the robustness of the method, a new approach is proposed to calibrate the hand-mounted camera. The calibration algorithm is designed to deal with the case in which the camera sensor plane is nearly-parallel to the camera calibration board. Practical issues regarding robot calibration in general and SCARA arm calibration in particular are also addressed. Experiment studies reveal that the proposed camera-aided approach is a viable means for accuracy enhancement of SCARA arms.

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.

To assess and evaluate the performance of robots and machine tools dynamically, it
is desirable to have a precision measuring device that performs dynamic measurement
of end-effector positions of such robots and machine tools. Among possible
measurement techniques, Laser Tracking Systems (LTSs) exlnbit the capability of high
accuracy, large workspace, high sampling rate, and automatic target-tracking,. and thus
are well-suited for robot calibration both kinematically and dynamically.
In this dissertation, the design and implementation of a control system for a homemade
laser tracking measurement systems is addressed and calibration of a robot using
the laser tracking system is demonstrated Design and development of a control system for a LTS is a challenging task. It
involves a deep understanding of laser interferometry,. controls, mechanics and optics,.
both in theoretical perspective and in implementation aspect. One of the most important
requirements for a successful design and implementation of a control system for the
LTS is proper installation and alignment of the laser and optical system,. or laser
transducer system. The precision of measurement using the LTS depends highly on the
accuracy of the laser transducer system, as well as the accuracy of the installation and
alignment of the optical system. Hence, in reference to the experimental alignment
method presented in this dissertation, major error sources affecting the system
measurement accuracy are identified and analyzed. A manual compensation method is
developed to eliminate the effects of these error sources effectively in the measurement
system. Considerations on proper design and installation of laser and optical
components are indicated in this dissertation.
As a part of the conventional control system design, a dynamic system model of the
LTS is required. In this study, a detailed derivation and analysis of the dynamic model
of the motor gimbal system using Lagrange-Euler equations of motion is developed for
both ideal and complete gimbal systems. Based on this system model,. a conventional
controller is designed.
Fuzzy Logic Controllers (FLC) are designed in order to suppress noise or
disturbances that exist in the motor driver subsystem. By using the relevant control
strategies. noise and disturbances present in the electrical control channels are shown to
reduce significantly. To improve the system performance further, a spectrum analysis of the error sources and disturbances existing in the system is conducted. Major noise
sources are effectively suppressed by using a two-stage fuzzy logic control strategy. A
comparison study on the performances of different control strategies is given in this
dissertation, in reference to the following: An ideal system model, a system with a long
time delay, a system with various noise sources and a system model with uncertainties.
Both simulation and experimental results are furnished to illustrate the advantages of
the FLC in respect of its transient response, steady-state response, and tracking
performance. Furthermore, noise reduction in the laser tracking system is demonstrated.
Another important issue concerning a successful application of the LTS in the
calibration of a robot is the estimation of system accuracy. Hence, a detailed analysis of
system accuracy of the LTS is presented in this worL This analysis is also verified by
experimental methods by means of tracking a Coordinate Measuring Machine available
in the FAU Robotics Center. Using the developed LTS, a PUMA robot in the FAU
Robotics Center is calibrated. The results obtained are confirmative with the data
available in the literature.
In summary, the proposed methodology towards the design and implementation of a
control system for LTSs has been shown to be successful by performing experimental
tracking and calibration studies at the FAU Robotics Center.