Case study: Performance analysis and development of robotized screwing application with integrated vision sensing system for automotive industry

Sága, Milan; Bulej, Vladimír; Čuboňová, Nadežda; Kuric, Ivan; Virgala, Ivan; Eberth, Manfred

doi:10.1177/1729881420923997

“…In MEMS inertial sensor applications, the application of MEMS inertial sensors in the field of consumer electronics is undoubtedly the largest market, but at present, inertial sensors have been applied in the fields of wheeled mobile platform navigation, mobile omniwheel robot trajectory tracking, industrial robots, hexacopter navigation control, wearable devices motion monitoring, underwater vehicle navigation, and torpedo and rocket navigation [ 110 , 111 , 112 , 113 , 114 , 115 , 116 , 117 , 118 , 119 , 120 , 121 , 122 ]. With the improvement of the precision of MEMS inertial sensors and the increase in industrial market demand, the demand for MEMS inertial sensors in the above fields will increase day by day.…”

Section: Discussionmentioning

confidence: 99%

Random Error Reduction Algorithms for MEMS Inertial Sensor Accuracy Improvement—A Review

Han

¹

,

Meng

²

,

Omisore

³

et al. 2020

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Research and industrial studies have indicated that small size, low cost, high precision, and ease of integration are vital features that characterize microelectromechanical systems (MEMS) inertial sensors for mass production and diverse applications. In recent times, sensors like MEMS accelerometers and MEMS gyroscopes have been sought in an increased application range such as medical devices for health care to defense and military weapons. An important limitation of MEMS inertial sensors is repeatedly documented as the ease of being influenced by environmental noise from random sources, along with mechanical and electronic artifacts in the underlying systems, and other random noise. Thus, random error processing is essential for proper elimination of artifact signals and improvement of the accuracy and reliability from such sensors. In this paper, a systematic review is carried out by investigating different random error signal processing models that have been recently developed for MEMS inertial sensor precision improvement. For this purpose, an in-depth literature search was performed on several databases viz., Web of Science, IEEE Xplore, Science Direct, and Association for Computing Machinery Digital Library. Forty-nine representative papers that focused on the processing of signals from MEMS accelerometers, MEMS gyroscopes, and MEMS inertial measuring units, published in journal or conference formats, and indexed on the databases within the last 10 years, were downloaded and carefully reviewed. From this literature overview, 30 mainstream algorithms were extracted and categorized into seven groups, which were analyzed to present the contributions, strengths, and weaknesses of the literature. Additionally, a summary of the models developed in the studies was presented, along with their working principles viz., application domain, and the conclusions made in the studies. Finally, the development trend of MEMS inertial sensor technology and its application prospects were presented.

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“…Several authors, e.g., [1] have mentioned that advanced digital technology is already used in industry, however with Industry 4.0, it will transform production into smarter automation in various ways via the installation of smart robots, sensors, using of collaborative robots and applying production simulations, etc. Nowadays, new applications are constantly appearing in which so-called machine vision (MV) systems based on processing images captured via at least one camera have found strong usage [2]. As a result, their application can reduce total costs and make it possible to solve tasks that would not be possible without the use of such cognitive sensors.…”

Section: Introductionmentioning

confidence: 99%

Approach to Automated Visual Inspection of Objects Based on Artificial Intelligence

Kuric

¹

,

Klarák

²

,

Bulej

³

et al. 2022

Self Cite

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The article discusses the possibility of object detector usage in field of automated visual inspection for objects with specific parameters, specifically various types of defects occurring on the surface of a car tire. Due to the insufficient amount of input data, as well as the need to speed up the development process, the Transfer Learning principle was applied in a designed system. In this approach, the already pre-trained convolutional neural network AlexNet was used, subsequently modified in its last three layers, and again trained on a smaller sample of our own data. The detector used in the designed camera inspection system with the above architecture allowed us to achieve the accuracy and versatility needed to detect elements (defects) whose shape, dimensions and location change with each occurrence. The design of a test facility with the application of a 12-megapixel monochrome camera over the rotational table is briefly described, whose task is to ensure optimal conditions during the scanning process. The evaluation of the proposed control system with the quantification of the recognition capabilities in the individual defects is described at the end of the study. The implementation and verification of such an approach together with the proposed methodology of the visual inspection process of car tires to obtain better classification results for six different defect classes can be considered as the main novel feature of the presented research. Subsequent testing of the designed system on a selected batch of sample images (containing all six types of possible defect) proved the functionality of the entire system while the highest values of successful defect detection certainty were achieved from 85.15% to 99.34%.

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“…With the rapid development of science and technology these years, the artificial intelligence (AI) technology has been widely used in various fields, among which AI and visual sensing technology have developed rapidly. The main function of this technology is to add perception technology to the visual sensor so that it can segment, detect, and recognize the network architecture of the target object [8]. The basic theory of this technology is the convolutional neural network (CNN) algorithm [9], and visual sensing technology assisted by the CNN system has gradually reached or exceeded the recognition level of humans.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence‐Assisted Visual Sensing Technology under Duodenoscopy of Gallbladder Stones

Li

¹

,

Bin

²

,

Shen

³

et al. 2021

Journal of Sensors

0

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Objective. This study is aimed at exploring the application effect of duodenoscopy assisted by visual sensing technology based on convolutional neural network (CNN) segmentation algorithm in the diagnosis and treatment of gallbladder stones, so as to provide safer and more effective treatment methods for patients with gallstones. Methods. 188 patients with gallstones and choledocholithiasis who were admitted to our hospital from January 2016 to April 2021 were selected as the research objects. Based on whether the patients were willing to use AI-assisted visual sensing technology during the treatment process, all patients were divided into two groups, namely, the AI group and the conventional group. Various surgical indicators of patients in two groups were compared. Results. The precision, recall, and mean intersection ratio of the M-Unet-based segmentation algorithm were 94.56%, 96.56%, and 98.92%, respectively. In the AI group, the operation time ( 2.74 ± 0.45 h ), postoperative drainage tube placement time ( 4.31 ± 1.15 d ), time required for recovery of gastrointestinal function ( 1.74 ± 0.54 d ), time required to get out of bed ( 1.14 ± 0.55 h ), and time spent in hospital ( 9.94 ± 1.45 d ) were all shorter compared with those in the conventional group, which were 3.21 ± 0.32 h , 12.14 ± 2.98 d , 2.89 ± 0.67 d , 2.09 ± 0.87 h , and 14.14 ± 1.15 h , showing statistical differences ( P < 0.05 ); the intraoperative blood loss ( 79.74 ± 6.45 mL ) and residual status of stones (0%) in the AI group were much lower than those in the conventional group ( P < 0.05 ). In addition, the incidence of complications (10.26%) and the indicators of postoperative gallbladder function of patients in the AI group were lower greatly than those in the conventional group ( P < 0.05 ). Conclusion. The visual sensing technology assisted by the CNN algorithm showed a good effect on image processing, and endoscopic technology can effectively improve the treatment effect of gallbladder stones combined with choledocholithiasis with the aid of this technology. Therefore, the conclusion in this study proved that visual sensing technology based on intelligent algorithms showed a good future in the medical field.

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Case study: Performance analysis and development of robotized screwing application with integrated vision sensing system for automotive industry

Cited by 31 publications

References 30 publications

Random Error Reduction Algorithms for MEMS Inertial Sensor Accuracy Improvement—A Review

Random Error Reduction Algorithms for MEMS Inertial Sensor Accuracy Improvement—A Review

Approach to Automated Visual Inspection of Objects Based on Artificial Intelligence

Artificial Intelligence‐Assisted Visual Sensing Technology under Duodenoscopy of Gallbladder Stones

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