Intelligent Selective Compliance Articulated Robot Arm robot with object recognition in a multi-agent manufacturing system

Zakhama, Afef; Charrabi, Lotfi; Jelassi, Khaled

doi:10.1177/1729881419841145

Cited by 18 publications

(14 citation statements)

References 44 publications

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“…Emerging research topics include machine learning algorithms to identify technical defects in the manufacturing sector (such as to identify relationships that can be harnessed to preempt electrical defects at downline inspection stations) [45], genetic algorithms to predict customer needs [46], and robot actions using a multi-platform software application [47]. A very nice application was proposed by Zakhama et al [48]. In particular, a SCARA robot's system and software implementation were used to detect defects in the product.…”

Section: Application Papersmentioning

confidence: 99%

Smart Manufacturing Systems and Applied Industrial Technologies for a Sustainable Industry: A Systematic Literature Review

et al. 2020

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Smart manufacturing is considered as a new paradigm that makes work smarter and more connected, bringing speed and flexibility through the introduction of digital innovation. Today, digital innovation is closely linked to the “sustainability” of companies. Digital innovation and sustainability are two inseparable principles that are based on the concept of circular economy. Digital innovation enables a circular economy model, promoting the use of solutions like digital platforms, smart devices, and artificial intelligence that help to optimize resources. Thus, the purpose of the research is to present a systematic literature review on what enabling technologies can promote new circular business models. A total of 31 articles were included in the study. Our results showed that realization of the circular economy involved two main changes: (i) managerial changes and (ii) legislative changes. Furthermore, the creation of the circular economy can certainly be facilitated by innovation, especially through the introduction of new technologies and through the introduction of digital innovations.

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Section: Application Papersmentioning

confidence: 99%

Smart Manufacturing Systems and Applied Industrial Technologies for a Sustainable Industry: A Systematic Literature Review

et al. 2020

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“…Here, δ (φ represents a Dirac function defined in (5). The segmentation result of the LIF model is as good as that of the LBF model but faces a certain limitation, for example, the sensitivity of the initial contour position and a stop of the contour at the local minima.…”

Section: Local Image Fitting Model (Lif)mentioning

confidence: 99%

“…Image segmentation is often used to extract the informative parts from an image that are used for further analysis or understanding. This is an essential tool in computer image analysis, computer vision, medical imaging, image identification, and image classification [1][2][3][4][5][6]. In the past decades, numerous models have been proposed for image segmentation, including thresholding [7][8][9], clustering [10,11], and active contour models (ACMs) [12,13].…”

Section: Introductionmentioning

confidence: 99%

Saliency-Driven Active Contour Model for Image Segmentation

et al. 2020

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Active contour models have achieved prominent success in the area of image segmentation, allowing complex objects to be segmented from the background for further analysis. Existing models can be divided into region-based active contour models and edge-based active contour models. However, both models use direct image data to achieve segmentation and face many challenging problems in terms of the initial contour position, noise sensitivity, local minima and inefficiency owing to the in-homogeneity of image intensities. The saliency map of an image changes the image representation, making it more visual and meaningful. In this study, we propose a novel model that uses the advantages of a saliency map with local image information (LIF) and overcomes the drawbacks of previous models. The proposed model is driven by a saliency map of an image and the local image information to enhance the progress of the active contour models. In this model, the saliency map of an image is first computed to find the saliency driven local fitting energy. Then, the saliency-driven local fitting energy is combined with the LIF model, resulting in a final novel energy functional. This final energy functional is formulated through a level set formulation, and regulation terms are added to evolve the contour more precisely across the object boundaries. The quality of the proposed method was verified on different synthetic images, real images and publicly available datasets, including medical images. The image segmentation results, and quantitative comparisons confirmed the contour initialization independence, noise insensitivity, and superior segmentation accuracy of the proposed model in comparison to the other segmentation models.

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“…Therefore, such a complex origin of positioning errors limits the implementation of common error compensation techniques [ 12 , 13 , 14 , 15 , 16 , 17 , 18 ]. Therefore, new and reliable methods, such as visual recognition systems and machine learning (ML) algorithms, can be applied to improve robot positioning accuracy and repeatability during operation [ 19 , 20 , 21 , 22 ].…”

Section: Introductionmentioning

confidence: 99%

Deep Q-Learning in Robotics: Improvement of Accuracy and Repeatability

Šumanas

Petronis

Bučinskas

et al. 2022

Sensors

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Recent industrial robotics covers a broad part of the manufacturing spectrum and other human everyday life applications; the performance of these devices has become increasingly important. Positioning accuracy and repeatability, as well as operating speed, are essential in any industrial robotics application. Robot positioning errors are complex due to the extensive combination of their sources and cannot be compensated for using conventional methods. Some robot positioning errors can be compensated for only using machine learning (ML) procedures. Reinforced machine learning increases the robot’s positioning accuracy and expands its implementation capabilities. The provided methodology presents an easy and focused approach for industrial in situ robot position adjustment in real-time during production setup or readjustment cases. The scientific value of this approach is a methodology using an ML procedure without huge external datasets for the procedure and extensive computing facilities. This paper presents a deep q-learning algorithm applied to improve the positioning accuracy of an articulated KUKA youBot robot during operation. A significant improvement of the positioning accuracy was achieved approximately after 260 iterations in the online mode and initial simulation of the ML procedure.

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Intelligent Selective Compliance Articulated Robot Arm robot with object recognition in a multi-agent manufacturing system

Cited by 18 publications

References 44 publications

Smart Manufacturing Systems and Applied Industrial Technologies for a Sustainable Industry: A Systematic Literature Review

Smart Manufacturing Systems and Applied Industrial Technologies for a Sustainable Industry: A Systematic Literature Review

Saliency-Driven Active Contour Model for Image Segmentation

Deep Q-Learning in Robotics: Improvement of Accuracy and Repeatability

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