ABSTRACT
This thesis describes a
novel approach to realizing self-learning calibration-free stereo-vision-guided
robots. It makes no use of world coordinates and of inverse
perspective or kinematic transformations. Thus, it eliminates the
need for a calibration of the robot and of the vision system and adapts
automatically to changes of the system parameters and of the environment. Also,
it allows the robot to acquire and accumulate automatically whatever knowledge
it may need into its knowledge base and to gain experience in the course of its
normal operation and, therefore, to improve its skills and operating speed over
time. Thus, the robot presents characteristics of a self-learning
system.
Key points of this approach
are the independence of any special infrastructures, the absence of
any quantitative knowledge of the mechanical and kinematic
characteristics of the robot and of the visual system, as well as of the
objects to be grasped and the renunciation of a world coordinate system;
the control of the robot is accomplished by a direct transformation of sensor
data, e.g., image coordinates, to motor control commands. As a result of this,
the robot displays a remarkable degree of robustness against parameter changes.
The objects to be grasped
are detected from the background and other irrelevant image information based
on generic 2-D object models of the object appearance in the images. The
object pose, i.e., position and orientation, and the suitable grasping points
are automatically determined according to the object appearance recognized in
the stereo images.
The approach has been
implemented on a calibration-free vision-guided manipulator with five degrees
of freedom (DOF) and was evaluated in real-word experiments. Depending on the
shape and orientation of the object to be grasped, all five degrees of freedom
may be controlled and used. The experiments have shown that grasping of a variety
of differently shaped objects that may initially be located in arbitrary
positions in the robot's 3-D work space, and automatic skill improvement
are, indeed, possible.
Keywords: Calibration-free
Robots, Intelligent Robots, Knowledge-based Systems, Learning Robots, Machine
Learning, Object Grasping, Object Recognition, Robot Vision, Situation-oriented
and Behavior-based Stereo Vision, Real-time Image Processing, Vision-guided
Robots.