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FIGURE 8.5 Presence or absence of items in an assembly.
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Figure 8.7 is a schematic diagram of the main components of a robot in a typical vision process for manufacturing. A fixed camera surveys a small, carefully lighted area where the objects to be located or inspected are placed. When visual information is needed (as signaled by some external switch or sensors), a digitizer in a robot vision system converts the camera image into a snapshot : a significant array of integer brightness values (called gray levels). This array is sorted in a large random access memory (RAM) array in the robot-vision system called an image buffer or a frame buffer. Once sorted, the image can be displayed on a monitor at any time. More importantly, the image can be analyzed or manipulated by a vision computer, which can be programmed to solve robot vision problems. The vision computer is often connected to a separate general-purpose (host) computer,
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FIGURE 8.7
Schematic diagram of typical vision process.
which can be used to load programs or to perform tasks not directly related to vision. Once an image is acquired, vision processing operations follow a systematic path. Portions of the image buffer may first be manipulated to suppress information that will not be valuable to the task at hand and to enhance the information that will be useful. Next, the vision program extracts a small number of cues from the image perhaps allowing the region of interest to be reduced to exclude even more extraneous data. At this stage, the vision program calculates, from the selected image region, the cues (features) of direct importance to the task at hand and makes a decision about the presence of a known part or its location in the field of view, or perhaps about the presence of specific defects in the object being inspected. Finally, the robot-vision system activates control lines based on the decisions, and (perhaps) transfers a summary of the conclusion to a data storage device or another computer.
End Effector Camera Sensor for Edge Detection and Extraction
A considerable amount of development of synchronized dual camera sensors at a strategic location on a robot end effector has been conducted for processing two-dimensional images stored as binary matrices. A large part of this work has been directed toward solving problems of character recognition. While many of these techniques
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are potentially useful in the present context, it is valuable to note some important differences between the requirements of character recognition and those associated with visual feedback for mechanical assembly.
Shape and Size
All objects presented to the assembly machine are assumed to match an exact template of the reference object. The object may have an arbitrary geometric shape, and the number of possible different objects is essentially unlimited. Any deviation in shape or size, allowing for errors introduced by the visual input system, is a ground for rejection of the object (though this does not imply the intention to perform 100 percent inspection of components). The derived description must therefore contain all the shape and size information originally presented as a stored image. A character recognition system must tolerate considerable distortion, or style, in the characters to be recognized, the most extreme example being handwritten characters. The basic set of characters, however, is limited. The closest approach to a templatematching situation is achieved with the use of a type font specially designed for machine reading, such as optical character recognition (Fig. 8.8).
Position and Orientation
A component may be presented to the assembly machine in any orientation and any position in the field of view. Though a position- and orientation-invariant description is required in order to recognize the component, the measurement of these parameters is also an important function of the visual system to enable subsequent manipulation. While a line character may sometimes be skewed or bowed, individual
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