KEYWORDS: Digital signal processing, Robots, Visual process modeling, Robotic systems, Robot vision, Systems modeling, Image processing, Data modeling, Data fusion, Motion models
The logical structure of robot vision systems is introduced. It can be used as a basis for designing such systems. Both sensor data fusion and knowledge representation in a vision system may be broken down into four hierarchical levels where at each level knowledge representation in the form of partial models and data fusion complement each other. Multi-processor architectures supporting this type of structure are introduced as a particularly efficient basis for robot vision systems. Practical implementation is given.
KEYWORDS: Robots, Digital signal processing, Control systems, Spatial resolution, Signal processing, Intelligence systems, Cameras, Pixel resolution, Raster graphics, Image segmentation
A new concept for knowledge representation and structure of the knowledge module for vision-guided robots is introduced. It allows the robot to acquire, accumulate and adapt automatically whatever knowledge it may need and to gain experience in the course of its normal operation, i.e., learning by doing, thus, to improve its skills and operating speed over time. The knowledge module is structured into a set of a fairly independent submodules each performing a limited task, and sub-knowledge bases each contains limited knowledge. Such a structure allows to use the acquired knowledge flexibly and efficiently. It makes also easily to extend the knowledge base when the robot's number of degrees of freedom that must be controlled increases. The concept was realized and evaluated in real-world experiments on an uncalibrated vision-guided 5-DOF manipulator to grasp a variety of differently shaped objects.
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