Sensory-Based Colour Sorting Automated Robotic Cell.
ABSTRACT Robotics application in colour recognition using fiber optic cabled sensors interfaced with robot controller and Programmable Logic Controller (PLC) is discussed in this paper. The sensors send input signals to the robot controller and the specified program will be executed with respect to the triggered input. The aim of this research work is to recognize colour by pin point detection and sorting of object specimens with respect to their colour attributes, which includes hue, saturation and luminance level. The controller programs were designed to control the robot and the conveyor belt independently parallel to each other via relays, to be synchronized during operation. Finally, the calculative results were verified experimentally and the real time implementation was carried out. It can be observed how controllers are integrated and synchronized in a system to perform a desired operation without conflict using real time applications such as chemical, pharmaceutical, agricultural, food industries and even recycling.
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ABSTRACT: The fundamental issues to be addressed in automatic sorting systems are sensing, i.e., detecting and classifying the objects to be sorted, and gripping, i.e., realizing the required separation in the most efficient way. While the sensing issue is considered in Part 1 of this work, the topic of this paper is the evaluation and optimization of the gripping performance. For a sorting system to have a “good” behavior, it is required to pick up “the more it is possible”, in terms of percentage of gripped objects with respect to the total (gripping rate), and in terms of gripped objects per time unit (throughput). In the sorting of recyclable packaging (considered here as in Part 1 of this work), the flow of incoming items cannot be controlled and only its stochastic description may be known, so that the optimization of the above indices is a crucial and not trivial issue. Here, the problem is addressed at two different levels: first, different robot kinematics are characterized in terms of the parameters that affect the system performance, proving that the choice of a suitable kinematic structure is the first available tool to get a satisfactory system behavior. Then, for the class of redundant (or flexible) kinematic devices, the system performance is shown to depend on the strategy that governs the gripping order for the items on the belt. For this nonstandard scheduling problem, two innovative solutions have been devised, based on suitable modifications of standard queuing strategies. The proposed algorithms have the same complexity of the corresponding standard ones, but improved performance in the considered case, as confirmed by the reported simulation results.Robotics and Computer-integrated Manufacturing - ROBOT COMPUT-INTEGR MANUF. 01/2000; 16(2):81-90.
Conference Proceeding: Color Recognition in Outdoor Images.[show abstract] [hide abstract]
ABSTRACT: The color associated with an object in machine vision images is not constant; under varying illuminating and viewing conditions (such as in outdoor images), the perceived color of an object can vary significantly, thus making color-based recognition difficult. Existing methods in color-based recognition have been applied mostly to indoor and/or constrained imagery, but not to realistic outdoor data. This work analyzes the variation of object color in outdoor images with respect to existing models of daylight illumination and surface reflectance. Two approaches for color recognition are then proposed: the first develops context-based models of daylight illumination and hybrid surface reflectance, and predicts the color of objects based on scene context. The second method shows that object color can be nonparametrically “learned” through classification methods such as Neural Networks and Multivariate Decision Trees. The methods have been successfully tested in domains such as road/highway scenes, off-road navigation and military target detectionComputer Vision, 1998. Sixth International Conference on; 01/1998
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ABSTRACT: The classification performance, based on measurements obtained by a dedicated remote near-infrared sensor, is validated. Goal is the separation of demolition waste in three fractions: wood, plastic, and stone. In phase one, reference objects are collected and measured in order to develop the classification algorithm and to obtain reference classification results. In phases two and three, the validation performance and robustness are tested under laboratory and industrial conditions. In phase two, preliminary measurements are performed in the laboratory, indicating that some sensor hardware modifications are necessary. In phase three, measurements are performed on a pilot plant according to the following validation design. On the conveyor belt, objects are measured in the middle and at both borders, wet objects are measured in the middle, and a small set of objects is measured during 4 consecutive days. It is checked whether the classification performance obeys the predefined demands. The applied chemometrical techniques are well capable of separating dry demolition waste if the objects are positioned in the middle of the conveyor belt. It is recommended to overcome the sensor miniaturization-scale limitations by applying larger optical parts. The hardware sensor is not robust to wet objects, although this problem was accounted for during the development of the classification procedure. Including wet objects in the training set might overcome this restriction.Analytica Chimica Acta - ANAL CHIM ACTA. 01/2002; 453(1):117-124.