Increasing the Production System Productivity Using Inertial Navigation

Božek, Pavol; Nikitin, Yu. P.; Bezák, Pavol; Fedorko, Gabriel; Fábián, Michal

doi:10.21062/ujep/x.2015/a/1213-2489/mt/15/3/274

Cited by 16 publications

(13 citation statements)

References 13 publications

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“…Sensors suffer from several inadequacies and limitations, which can degrade their performance. Some sources of performance degradation are due to drifting errors, where a small offset can lead to a huge error when readings are accumulated over time, as in IMU [13,14]. Additionally, errors can be due to low sensor resolution, surface irregularities, or wheel slipping, as in-wheel odometers.…”

Section: Sensor Technology and Sensor Fusion Overviewmentioning

confidence: 99%

Deep Learning Sensor Fusion for Autonomous Vehicle Perception and Localization: A Review

Fayyad

Jaradat

Gruyer

et al. 2020

Sensors

324

130

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Autonomous vehicles (AV) are expected to improve, reshape, and revolutionize the future of ground transportation. It is anticipated that ordinary vehicles will one day be replaced with smart vehicles that are able to make decisions and perform driving tasks on their own. In order to achieve this objective, self-driving vehicles are equipped with sensors that are used to sense and perceive both their surroundings and the faraway environment, using further advances in communication technologies, such as 5G. In the meantime, local perception, as with human beings, will continue to be an effective means for controlling the vehicle at short range. In the other hand, extended perception allows for anticipation of distant events and produces smarter behavior to guide the vehicle to its destination while respecting a set of criteria (safety, energy management, traffic optimization, comfort). In spite of the remarkable advancements of sensor technologies in terms of their effectiveness and applicability for AV systems in recent years, sensors can still fail because of noise, ambient conditions, or manufacturing defects, among other factors; hence, it is not advisable to rely on a single sensor for any of the autonomous driving tasks. The practical solution is to incorporate multiple competitive and complementary sensors that work synergistically to overcome their individual shortcomings. This article provides a comprehensive review of the state-of-the-art methods utilized to improve the performance of AV systems in short-range or local vehicle environments. Specifically, it focuses on recent studies that use deep learning sensor fusion algorithms for perception, localization, and mapping. The article concludes by highlighting some of the current trends and possible future research directions.

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Section: Sensor Technology and Sensor Fusion Overviewmentioning

confidence: 99%

Deep Learning Sensor Fusion for Autonomous Vehicle Perception and Localization: A Review

Fayyad

Jaradat

Gruyer

et al. 2020

Sensors

324

130

View full text Add to dashboard Cite

show abstract

“…2), both metallic particles of various shapes and dimensions mostly in the range of 100 to 400 μm, as well as many fragments of nonmetallic fibers and particles, were observed. In many cases, these non-metallic particles are bonded with metal, which may be due to the PCB production process, in which thin films of good electrical conductivity metals (mainly Cu and Sn, Au, Ag, Pt) are applied on a glass fiber and epoxy laminate [7,29,31]. This may create difficulties in the ES process, leading to "contamination" of the metallic product with non-metallic particles.…”

Section: Fig 3 Image Of the Concentrate From Es (Qbsd Sem)mentioning

confidence: 99%

Recovery of Metals from Printed Circuit Boards By Means of Electrostatic Separation

Franke

Suponik

Nuckowski

et al. 2020

Management Systems in Production Engineering

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Without the use of appropriate recycling technologies, the growing amount of electronic waste in the world can be a threat to the development of new technologies, and in the case of improper waste management, may have a negative impact on the environment. This is due to the fact that this waste contains large amounts of valuable metals and toxic polymers. Therefore, it should be recycled in accordance with the assumptions of the circular economy. The methods of mechanical recovery of metals from electronic waste, including printed circuits, may be widely used in the future by waste management companies as well as metal production and processing companies. That is why, a well-known and easily applicable electrostatic separation (ES) method was used to recover metals from printed circuit boards. The grain class of 0.32 - 0.10 mm, obtained after grinding the boards, was fed to a separator. Feed and separation products were analyzed by means of ICP-AES, SEM/EDS and XRD. The concentrate yield obtained after electrostatic separation amounted to 32.3% of the feed. Its density was 11.1 g/cc. Out of the 91.44% elements identified in the concentrate, over 90% were metals. XRD, SEM observations and EDS analysis confirmed the presence of non-metallic materials in the concentrate. This relatively high content of impurities indicates the need to grind printed circuit board into grain classes smaller than 0.32-0.10 mm.

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“…QR (quick response) Code was invented in 1994 by Denso Wave for the automotive industry in Japan, but nowadays has much wider usage. They are widely used in segments such as manufacturing, logistics, sales, media, advertising, tourism, e-commerce, identification, and authentication [ 1 , 2 ]. The QR Code often contains additional information about the product, the object or the place where it is located.…”

Section: Introductionmentioning

confidence: 99%

Identification of QR Code Perspective Distortion Based on Edge Directions and Edge Projections Analysis

2020

Self Cite

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QR (quick response) Codes are one of the most popular types of two-dimensional (2D) matrix codes currently used in a wide variety of fields. Two-dimensional matrix codes, compared to 1D bar codes, can encode significantly more data in the same area. We have compared algorithms capable of localizing multiple QR Codes in an image using typical finder patterns, which are present in three corners of a QR Code. Finally, we present a novel approach to identify perspective distortion by analyzing the direction of horizontal and vertical edges and by maximizing the standard deviation of horizontal and vertical projections of these edges. This algorithm is computationally efficient, works well for low-resolution images, and is also suited to real-time processing.

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Increasing the Production System Productivity Using Inertial Navigation

Cited by 16 publications

References 13 publications

Deep Learning Sensor Fusion for Autonomous Vehicle Perception and Localization: A Review

Deep Learning Sensor Fusion for Autonomous Vehicle Perception and Localization: A Review

Recovery of Metals from Printed Circuit Boards By Means of Electrostatic Separation

Identification of QR Code Perspective Distortion Based on Edge Directions and Edge Projections Analysis

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