Computer Vision-Based Object Recognition and Automatic Pneumatic Soft Gripping

2019 ◽  
Author(s):  
Ebrahim Shahabi ◽  
Wei-Hao Lu ◽  
Po Ting Lin ◽  
Chin-Hsing Kuo
Keyword(s):  
Computer Vision ◽  
Object Recognition ◽  
Random Sample ◽  
Low Cost ◽  
Commercial Products ◽  
Object Shape ◽  
Soft Actuator ◽  
3D Printed ◽  
Novel Method ◽  
Random Sample Consensus

Abstract During recent years, soft robotic is a new sub-class of the robots. Soft robotic has several engaging features, such as lightweight, low cost, simple fabrication, easy control, etc. Commercial products such as soft grippers are now available to apply in various fields and applications, for example, agriculture, medicine, machinery, etc. This paper proposes a novel method of grasping in soft robotic fields using computer vision to find the shape, size, and angle of the object to define the best type of grasping mode. Random Sample Consensus (RANSAC) was used to iteratively select randomly sampled 3D points to determine the working plane and identify the randomly placed object. Furthermore, we designed and fabricated a 3D-printed pneumatic soft actuator. The ratio of payload over weight is around 16. Experiments showed the proposed computer vision techniques and pneumatic soft gripper are capable of automatically recognize the object shape and perform soft gripping.

scholarly journals P-RANSAC: An Integrity Monitoring Approach for GNSS Signal Degraded Scenario

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Gaetano Castaldo ◽  
Antonio Angrisano ◽  
Salvatore Gaglione ◽  
Salvatore Troisi
Keyword(s):  
Computer Vision ◽  
Random Sample ◽  
Simultaneous Presence ◽  
Static Test ◽  
Navigation Solution ◽  
Processing Data ◽  
Urban Scenario ◽  
Random Sample Consensus ◽  
Quality Check ◽  
Ransac Algorithm

Satellite navigation is critical in signal-degraded environments where signals are corrupted and GNSS systems do not guarantee an accurate and continuous positioning. In particular measurements in urban scenario are strongly affected by gross errors, degrading navigation solution; hence a quality check on the measurements, defined as RAIM, is important. Classical RAIM techniques work properly in case of single outlier but have to be modified to take into account the simultaneous presence of multiple outliers. This work is focused on the implementation of random sample consensus (RANSAC) algorithm, developed for computer vision tasks, in the GNSS context. This method is capable of detecting multiple satellite failures; it calculates position solutions based on subsets of four satellites and compares them with the pseudoranges of all the satellites not contributing to the solution. In this work, a modification to the original RANSAC method is proposed and an analysis of its performance is conducted, processing data collected in a static test.

scholarly journals Development of Weather-Resistant 3D Printed Structures by Multi-Material Additive Manufacturing

2020 ◽  
Vol 4 (3) ◽  
pp. 94 ◽  
Author(s):  
Arash Afshar ◽  
Roy Wood
Keyword(s):  
Mechanical Properties ◽  
Additive Manufacturing ◽  
Structural Integrity ◽  
Low Cost ◽  
Acrylonitrile Butadiene Styrene ◽  
Outdoor Environments ◽  
3D Printed ◽  
Novel Method ◽  
Structural Surface

Additive manufacturing, or 3D printing, has had a big impact on the manufacturing world through its low cost, material recyclability, and fabrication of intricate geometries with a high resolution. Three-dimensionally printed polymer structures in aerospace, marine, construction, and automotive industries are usually intended for service in outdoor environments. During long-term exposures to harsh environmental conditions, the mechanical properties of these structures can be degraded significantly. Developing coating systems for 3D printed parts that protect the structural surface against environmental effects and provide desired surface properties is crucial for the long-term integrity of these structures. In this study, a novel method was presented to create 3D printed structures coated with a weather-resistant material in a single manufacturing operation using multi-material additive manufacturing. One group of specimens was 3D printed from acrylonitrile-butadiene-styrene (ABS) material and the other group was printed from ABS and acrylic-styrene-acrylonitrile (ASA) as a substrate and coating material, respectively. The uncoated ABS specimens suffered significant degradation in the mechanical properties, particularly in the failure strain and toughness, during exposure to UV radiation, moisture, and high temperature. However, the ASA coating preserved the mechanical properties and structural integrity of ABS 3D printed structures in aggressive environments.

Optimize Fundamental Matrix Estimation Based on RANSAC

2011 ◽  
Vol 50-51 ◽  
pp. 333-337
Author(s):  
Jun Zhou
Keyword(s):  
Computer Vision ◽  
Random Sample ◽  
Robust Estimation ◽  
Structure From Motion ◽  
Fundamental Matrix ◽  
Estimation Algorithm ◽  
Matrix Estimation ◽  
Random Sample Consensus ◽  
M Estimation ◽  
Ransac Algorithm

Fundamental matrix estimation is a central problem in computer vision and forms the basis of tasks such as stereo imaging and structure from motion, and which is especially difficult since it is often based on correspondences that are spoilt by noise and outliers. The Random Sample Consensus (RANSAC) algorithm is a popular tool for robust estimation, primarily due to its ability to tolerate a tremendous fraction of outliers. In this article, we provide an approach for improve of RANSAC that has the benefit of offering fast and accurate RANSAC, and combine the M-estimation algorithm get the fundamental matrix. Experimental results are given that support the adopted approach and demonstrate the algorithm is a practical technique for fundamental matrix estimation.

scholarly journals Literatur Riview Transliterasi Hurub Arab ke Latin

SAINTEKBU ◽  
2020 ◽  
Vol 12 (2) ◽  
pp. 58-67
Author(s):  
Ahmat Nurwakit
Keyword(s):  
Neural Network ◽  
Computer Vision ◽  
Object Recognition ◽  
Character Recognition ◽  
Low Cost ◽  
Video Tracking ◽  
Vector Data ◽  
3D Pose Estimation ◽  
Eigen Value ◽  
Tracking Object

Computer vision adalah bidang interdisiplin yang mempelajari tentang bagaimana komputer dapat melakukan pemahaman terhadap citra digital dan video. Dalam persepektif engineering computer vision ditujukan untuk melakukan automasi terhadap sistem visual manusia. Tahap dalam computer vision meliputi pengambilan (acquiring), pemrosesan (processing), analisis (analyzing) dan pemahaman (understanding) terhadap citra digital. Computer vision berfokus pada sistem cerdas yang dapat melakukan ekstraksi data dari citra digital ke dalam bentuk numerik Sub domain dari computer vision meliputi  scene reconstruction, event detection, video tracking, object recognition, 3D pose estimation, learning, indexing, motion estimation, dan image restoration. Handwriting character recognition (pengenalan tulisan tangan) adalah salah satu cabang dari object recognition, yaitu kemampuan komputer untuk menerima dan menafsirkan input tulisan tangan yang dapat dimengerti dari sumber seperti dokumen kertas, foto, layar sentuh dan perangkat lainnya. Gambar dari teks tertulis dapat digunakan secara luring dari selembar kertas oleh pemindai optik (rekognisi karakter optik). Selain itu, gerakan ujung pena dapat dimengerti secara daring, misalnya dengan menggunakan permukaan layar komputer berbasis pena. Salah satu aksara yang dijadikan objek dalam pengenalan tulisan tangan adalah huruf huruf arab pegon (yang selanjutnya disebut pegon). Huruf pegon biasa digunakan dalam terjemah kitab kuning dalam bahasa daerah (umunya) Jawa, Sunda, atau Melayu. Kenyataannya terjemah kitab-kitab kuning klasik di indonesian lebih banyak menggunakan 3 bahasa tersebut, sehingga orang-orang  yang tidak menguasai salah satu dari bahasa tersebut akan kesuliatan untuk mendapatkan terjemah.Berbeda dengan huruf arab baku, huruf  pegon memiliki beberapa karakter yang merupakan rekayasa agar dapat dibaca menyesuaikan lidah bahasa daerah bersangkutan. Masalah yang muncul adalah huruf pegon tidak dapat dibaca oleh seseorang yang tidak memiliki kosa kata dalam bahasa bersangkutan sehingga membutuhkan proses penerjemahan. Proses penerjemahan sendiri hanya mungkin dilakukan jika kalimat tertulis dalam huruf latin. Pengenalan tulisan tangan saat ini sudah banyak dilakukan menggunakan banyak metode, terutama yang paling banyak dari varian Jaringan Syaraf Tiruan (neural network). Walaupun tingkat akurasi dari neural network tinggi, tetapi computatiion cost metode-metode ini sangat besar.  Eigenspace adalah subspace dari aljabar linier yang terdiri dari sekumpulan eigen vector. Tiap eigen vector terbentuk dari banyak eigen value. Salah satu pemanfaatannya adalah eigenface yang digunakan dalam pengenalan wajah. Caranya adalah dengan mengubah citra digital ke dalam eigen value yang kemudian disusun menjadi eigen vector. Data uji kemudian akan dihitung jaraknya terhadap semua vector yang ada kemudian diambil yang nilainya paling dekat. Metode ini cukup low cost. Keyword: transliterasi, eigen, huruf arab, huruf latin

Epipolar Geometry Estimation Using Improved LO-RANSAC

2011 ◽  
Vol 213 ◽  
pp. 255-259
Author(s):  
Jun Zhou
Keyword(s):  
Computer Vision ◽  
Random Sample ◽  
Robust Estimation ◽  
Epipolar Geometry ◽  
Large Subset ◽  
Geometry Estimation ◽  
Random Sample Consensus ◽  
And Robotics ◽  
Ransac Algorithm

The estimation of the epipolar geometry is of great interest for a number of computer vision and robotics tasks, and which is especially difficult when the putative correspondences include a low percentage of inliers correspondences or a large subset of the inliers is consistent with a degenerate configuration of the epipolar geometry that is totally incorrect. The Random Sample Consensus (RANSAC) algorithm is a popular tool for robust estimation, primarily due to its ability to tolerate a tremendous fraction of outliers. In this paper, we propose an approach for improve of locally optimized RANSAC (LO-RANSAC) that has the benefit of offering fast and accurate RANSAC. The resulting algorithm when tested on real images with or without degenerate configurations gives quality estimations and achieves significant speedups compared to the LO-RANSAC algorithms.

Design and Fabrication of Pneumatic Soft Gripper

2018 ◽  
Author(s):  
Zhonghua Guo ◽  
Zhongsheng Sun ◽  
Xiaoning Li
Keyword(s):  
Compressed Air ◽  
Work Piece ◽  
Soft Body ◽  
Object Shape ◽  
Soft Actuator ◽  
3D Printed ◽  
Experimental Approaches ◽  
Different Shapes ◽  
The Stability ◽  
Gripping Force

In this paper, a pneumatic soft gripper is proposed with inspiration from sea anemone. The gripper is composed of an actuator and several silicone tentacles. With the power of compressed air, the soft actuator expands and folds the tentacles. The gripper wraps tentacles around the object and highly compliant tentacles conforms to the shapes of an object, enveloping and holding it. The physical model is fabricated with 3D printed PLA mold and silicone gel. The gripping mechanics are analyzed according to the experimental gripping operations. On basis of the experimental and analysis result, the compliant gripping is realized while the stability is to be increased. So the tentacle structure is then improved by multi-chamber soft body and vacuum jamming bag. The jamming bag is combined to the end of each tentacle, where the bag is filled with particles to conform to the object shape. Therefore, a reliable constraint is realized between the gripper and the object under vacuum conditions. The bending motion and shaping effect are verified through theoretical and experimental approaches. The important parameters in the vacuum jamming process are also obtained. With such device, soft adaptive bodies enlarges the contact area to adapt to the work-piece where vacuum jamming bags increase the gripping force and stability. It is convenient for universal gripping operation for objects with different shapes.

Pioneering Low-Cost 3D-Printed Transtibial Prosthetics to Serve a Rural Population in Sierra Leone

2020 ◽  
Author(s):  
Merel van der Stelt ◽  
Martin P. Grobusch ◽  
Abdul R. Koroma ◽  
Marco Papenburg ◽  
Ismaila Kebbie ◽  
...  
Keyword(s):  
Sierra Leone ◽  
Rural Population ◽  
Low Cost ◽  
3D Printed

scholarly journals UV Inscription and Pressure Induced Long-Period Gratings through 3D Printed Amplitude Masks

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 1977
Author(s):  
Ricardo Oliveira ◽  
Liliana M. Sousa ◽  
Ana M. Rocha ◽  
Rogério Nogueira ◽  
Lúcia Bilro
Keyword(s):  
State Of The Art ◽  
Low Cost ◽  
Resonance Wavelength ◽  
Complex Structures ◽  
Pressing Method ◽  
Long Period ◽  
Long Period Gratings ◽  
3D Printed ◽  
Mechanical Pressing ◽  
First Time

In this work, we demonstrate for the first time the capability to inscribe long-period gratings (LPGs) with UV radiation using simple and low cost amplitude masks fabricated with a consumer grade 3D printer. The spectrum obtained for a grating with 690 µm period and 38 mm length presented good quality, showing sharp resonances (i.e., 3 dB bandwidth < 3 nm), low out-of-band loss (~0.2 dB), and dip losses up to 18 dB. Furthermore, the capability to select the resonance wavelength has been demonstrated using different amplitude mask periods. The customization of the masks makes it possible to fabricate gratings with complex structures. Additionally, the simplicity in 3D printing an amplitude mask solves the problem of the lack of amplitude masks on the market and avoids the use of high resolution motorized stages, as is the case of the point-by-point technique. Finally, the 3D printed masks were also used to induce LPGs using the mechanical pressing method. Due to the better resolution of these masks compared to ones described on the state of the art, we were able to induce gratings with higher quality, such as low out-of-band loss (0.6 dB), reduced spectral ripples, and narrow bandwidths (~3 nm).

scholarly journals BoSL FAL Pump: a small, low-cost, easily constructed, 3D-printed peristaltic pump for sampling of waters

HardwareX ◽  
2021 ◽  
pp. e00214
Author(s):  
David T. McCarthy ◽  
Baiqian Shi ◽  
Miao Wang ◽  
Stephen Catsamas
Keyword(s):  
Low Cost ◽  
Peristaltic Pump ◽  
3D Printed

scholarly journals An Automated Light Trap to Monitor Moths (Lepidoptera) Using Computer Vision-Based Tracking and Deep Learning

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 343
Author(s):  
Kim Bjerge ◽  
Jakob Bonde Nielsen ◽  
Martin Videbæk Sepstrup ◽  
Flemming Helsing-Nielsen ◽  
Toke Thomas Høye
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Vision System ◽  
Low Cost ◽  
Light Trap ◽  
Automatic Monitoring ◽  
Light Sources ◽  
Monitoring Methods ◽  
Computer Vision System ◽  
Substantial Investment

Insect monitoring methods are typically very time-consuming and involve substantial investment in species identification following manual trapping in the field. Insect traps are often only serviced weekly, resulting in low temporal resolution of the monitoring data, which hampers the ecological interpretation. This paper presents a portable computer vision system capable of attracting and detecting live insects. More specifically, the paper proposes detection and classification of species by recording images of live individuals attracted to a light trap. An Automated Moth Trap (AMT) with multiple light sources and a camera was designed to attract and monitor live insects during twilight and night hours. A computer vision algorithm referred to as Moth Classification and Counting (MCC), based on deep learning analysis of the captured images, tracked and counted the number of insects and identified moth species. Observations over 48 nights resulted in the capture of more than 250,000 images with an average of 5675 images per night. A customized convolutional neural network was trained on 2000 labeled images of live moths represented by eight different classes, achieving a high validation F1-score of 0.93. The algorithm measured an average classification and tracking F1-score of 0.71 and a tracking detection rate of 0.79. Overall, the proposed computer vision system and algorithm showed promising results as a low-cost solution for non-destructive and automatic monitoring of moths.

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