Automating the Process of Work-Piece Recognition and Location for a Pick-and-Place Robot in a SFMS

Sharan, Roneel V.; Onwubolu, Godfrey C.

doi:10.5815/ijigsp.2014.04.02

Cited by 5 publications

(3 citation statements)

References 11 publications

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“…No research presents a complete method of the existing different methods in the case of different light intensities. While the lighting system is one of the most important factors affecting feature extraction from the captured image [5]. Motivated by these studies, the present study aimed to design an efficient robotic system that can recognize an object in various light conditions.…”

Section: Motivationmentioning

confidence: 99%

“…To implement the object recognition process, Perfect lighting is required for system operation. The lighting system is one of the most important factors that affect features from extraction from captured images [5]. Thus, the change in the intensity of the lighting will hinder the process of image processing and thus impede the process of picking up and location.…”

Section: Introductionmentioning

confidence: 99%

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Smart Robot Vision for a Pick and Place Robotic System

Mohammed¹,

AL-Khafaji²,

Abbas³

2023

ETJ

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The movement of the 5 DOF robotic arm was controlled through the geometric approach in inverse kinematics analysis  HSV color space, a series of filters (M edian, Bilateral, and Gaussian), and the drawcontour method to discover objects' colors, shapes, and centroid were implemented.  The shapes and colors of the objects in different lighting conditions ranging from 5 to 7000 lux with an accuracy of 93.83% were discovered.The main contribution of this paper is to develop an innovative algorithm to accurately detect and identify the shape and color of objects under various light intensities and find their location to be manipulated by a pick-and-place robotic arm. Workpieces of various shapes and colors are dispersed on the robot's work plane and manipulated according to its specifications. The proposed algorithm utilizes the HSV color model to distinguish between different object colors and shapes. The S channel is used to detect the shapes of objects. After that, a series of filters (M edian, Bilateral, and Gaussian) are applied to reduce the noise of the segmented image to make the process of discovering the shap e and coordinates of the objects successful. The draw-contour method is used to discover the object's shapes. After the shape of the object is discovered, the centroid coordinates are calculated. After extensive testing on 354 images that are captured in various lighting conditions in the range of (5-7000 lux), the overall system performance of 93.83% is achieved, and the average execution time is 2.21s. Finally, we had a dependable flexible automatic pick and place system that could correctly detect and identify the objects based on their features.

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Section: Motivationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Smart Robot Vision for a Pick and Place Robotic System

Mohammed¹,

AL-Khafaji²,

Abbas³

2023

ETJ

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“…The scheme is equipped with sensors, such as vision and ultrasonic sensors, that are installed at the end-effector of the robots to recognize the components. Sharan et al (2014) developed a vision system to automatically classify workpieces concerning their shape and color and to determine their location for manipulation by -and-place robots developed in-house from their work-plane. Nishihara and Okamoto proposed an AR system to guide users in assembly tasks by recognizing each part to be assembled through image processing techniques and guiding parts placement with graphical signs.…”

Section: Related Workmentioning

confidence: 99%

Projection-based augmented reality system for assembly guidance and monitoring

Chen

Tian

et al. 2020

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Purpose This study aims to monitor and guide the assembly process. The operators need to change the assembly process according to the products’ specifications during manual assembly of mass customized production. Traditional information inquiry and display methods, such as manual lookup of assembly drawings or electronic manuals, are inefficient and error-prone. Design/methodology/approach This paper proposes a projection-based augmented reality system (PBARS) for assembly guidance and monitoring. The system includes a projection method based on viewpoint tracking, in which the position of the operator’s head is tracked and the projection images are changed correspondingly. The assembly monitoring phase applies a method for parts recognition. First, the pixel local binary pattern (PX-LBP) operator is achieved by merging the classical LBP operator with the pixel classification process. Afterward, the PX-LBP features of the depth images are extracted and the randomized decision forests classifier is used to get the pixel classification prediction image (PCPI). Parts recognition and assembly monitoring is performed by PCPI analysis. Findings The projection image changes with the viewpoint of the human body, hence the operators always perceive the three-dimensional guiding scene from different viewpoints, improving the human-computer interaction. Part recognition and assembly monitoring were achieved by comparing the PCPIs, in which missing and erroneous assembly can be detected online. Originality/value This paper designed the PBARS to monitor and guide the assembly process simultaneously, with potential applications in mass customized production. The parts recognition and assembly monitoring based on pixels classification provides a novel method for assembly monitoring.

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