Materials and Methods
F. Sample Parts
3.3.2 Part Identification
The parts are recognized with respect to the shape, size and materials from the image captured by vision system through image processing techniques. It is also required to determine the exact position of the target object for robot to grasp. Some of the operations accomplished by a machine vision system in the identification process are recognizing the part, sorting of parts, and gripping the parts oriented from a conveyor.
Recognizing desired part can be termed as a labelling problem based on known object models. For example, in an automated assembly environment, the conveyor belt may contain one or more similar or different objects. To recognize the object of interest, a set of labels corresponding to known object models, the system must be capable of assigning the correct labels to the object or region in the image taken by system. In the process of recognizing objects, feature extraction methods plays an important role. Feature extraction techniques provide either a single or group of features of an image. Typically, the extracted features may be a scalar quantity providing the area or aspect ratio of an image. Similarly it can be in vector format such as coordinate or texture information of an object etc. Feature of the region of interest or the object present in the captured image is extracted. There are several feature extraction techniques like edge map, corner point detection, color histograms, mathematical morphology, moment based and connected components labelling etc. used in the subsequent chapters. These features are used to compare with the features of the stored object models. The object recognition is fully dependent on the efficiency of the feature extraction methods. Feature extraction of image is an indispensable principal step in robot control application.
Several classes of features are
Line features are generally a set of curved line segments named as edges. These segments appear in region of image where the abrupt change of intensity values or brightness occurs. On the other hand, point features are corner points which exist at the intersection of two edge segments. Hence, at a corner point there exist two dominant and different edge directions. For extraction of such features, available edge
62
detection techniques like Canny edge, Sobel, Prewitt and Robert etc. are compared and a hybrid edge detection algorithm, considering fuzzy rules along with wavelet transform procedure, is developed.
As defined above the corner points lie at the intersection of two edges. Co-variance measure of the change in intensity values at each pixel is used to detect a corner point. Corner detection methods like Harris/Plessey, Curvature Scale Space and Wang-Brady [https://en.wikipedia.org/wiki/Corner_detection] are implemented to find the corner points and the efficient detector is considered for part identification.
Region of interest features are connecting group of pixels that are identical with respect to some constraint. Region growing technique is used to identify the exact part in an image.
3.3.3 Part Measurement
Perfect measurement of part features is helpful in accurate identification of parts and its grasping point determination. The part detection and identification is performed by extraction of features like distance and size measurement of the desired object. The distance between the robot and desired object is measured by coordinate transformation method.
Factors like boundary, area, region of interest point and center of gravity are measured for making the part recognition process autonomous. Once the correct part is identified, then the robot has to pick it for placing in desired place. For this, grasping points as points of interest are determined. These measurements are performed by mathematical morphology and moment based shape descriptor. The descriptors are some representative framework to outline a predefined shape. Accurate reconstruction of object or shape may not be possible from the descriptor. But the descriptor of different shapes must be distinguishable from each other. For tracking of a desired part in an assembly environment, the accurate measurement of part is essential.
3.3.4 Stable Grasping
A large variety of objects in an assembly work cell are manipulated by robots. Manipulation of objects is to establish a physical connection between the robot end-effector and the concerned object. In our context, this physical connection is a robotic grasp. Effectiveness of the grasping technique depends grasp planning algorithms and the shape of the object. Stable grasping is to transport an object from one place to another without changing the orientation.
63
3.3.5 Robot Navigation and Control
Vision-based robot control is used to control the motion of a robot and navigate it by using extracted feature information from a vision sensor. Tracking of the part from feeder to final product is carried out by vision system. Image based and position based servoing techniques are used for navigating the robot end effector to the desire position. With the help of visual data, the navigational control is used for avoiding obstacles and automatic path planning of a robot.
3.4
Summary
This chapter includes the significant materials required for the development of vision system based part assembly system. The description about the necessary accessories used for development of a vision guidance system is given. The important methods required for the successful integration of vision guidance system with a robotic system are discussed in brief.
62