IoT-based Closed Algal Cultivation System with Vision System for Cell Count through ImageJ via Raspberry Pi

Vision devices are currently one of the most widely used sensory elements in robots: commercial autonomous cars and vacuum cleaners, for example, have cameras. These vision devices can provide a great amount of information about robot surroundings. However, platforms for robotics education usually lack such devices, mainly because of the computing limitations of low cost processors. New educational platforms using Raspberry Pi are able to overcome this limitation while keeping costs low, but extracting information from the raw images is complex for children. This paper presents an open source vision system that simplifies the use of cameras in robotics education. It includes functions for the visual detection of complex objects and a visual memory that computes obstacle distances beyond the small field of view of regular cameras. The system was experimentally validated using the PiCam camera mounted on a pan unit on a Raspberry Pi-based robot. The performance and accuracy of the proposed vision system was studied and then used to solve two visual educational exercises: safe visual navigation with obstacle avoidance and person-following behavior.

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Artificial vision system for object classification in real time using Raspberry Pi and a Web camera

Revista de Tecnologías de la Información y Comunicaciones ◽

10.35429/jitc.2021.13.5.20.25 ◽

2021 ◽

pp. 20-25

Author(s):

Tomás Serrano-Ramírez ◽

Ninfa del Carmen Lozano-Rincón ◽

Arturo Mandujano-Nava ◽

Yosafat Jetsemaní Sámano-Flores

Keyword(s):

Embedded System ◽

Real Time ◽

Vision System ◽

Low Cost ◽

Artificial Vision ◽

Maintenance Cost ◽

Raspberry Pi ◽

Smart Cameras ◽

Suggested Technique ◽

Web Camera

Computer vision systems are an essential part in industrial automation tasks such as: identification, selection, measurement, defect detection and quality control in parts and components. There are smart cameras used to perform tasks, however, their high acquisition and maintenance cost is restrictive. In this work, a novel low-cost artificial vision system is proposed for classifying objects in real time, using the Raspberry Pi 3B + embedded system, a Web camera and the Open CV artificial vision library. The suggested technique comprises the training of a supervised classification system of the Haar Cascade type, with image banks of the object to be recognized, subsequently generating a predictive model which is put to the test with real-time detection, as well as the calculation for the prediction error. This seeks to build a powerful vision system, affordable and also developed using free software.

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A Maturity Estimation of Bell Pepper (Capsicum annuum L.) by Artificial Vision System for Quality Control

Applied Sciences ◽

10.3390/app10155097 ◽

2020 ◽

Vol 10 (15) ◽

pp. 5097

Author(s):

Marcos-Jesús Villaseñor-Aguilar ◽

Micael-Gerardo Bravo-Sánchez ◽

José-Alfredo Padilla-Medina ◽

Jorge Luis Vázquez-Vera ◽

Ramón-Gerardo Guevara-González ◽

...

Keyword(s):

Fuzzy Logic ◽

Capsicum Annuum ◽

Vision System ◽

Bell Pepper ◽

Artificial Vision ◽

Raspberry Pi ◽

Maturity Stage ◽

Capsicum Annuum L ◽

Bell Peppers ◽

Yellow Orange

Sweet bell peppers are a Solanaceous fruit belonging to the Capsicum annuum L. species whose consumption is popular in world gastronomy due to its wide variety of colors (ranging green, yellow, orange, red, and purple), shapes, and sizes and the absence of spicy flavor. In addition, these fruits have a characteristic flavor and nutritional attributes that include ascorbic acid, polyphenols, and carotenoids. A quality criterion for the harvest of this fruit is maturity; this attribute is visually determined by the consumer when verifying the color of the fruit’s pericarp. The present work proposes an artificial vision system that automatically describes ripeness levels of the bell pepper and compares the Fuzzy logic (FL) and Neuronal Networks for the classification stage. In this investigation, maturity stages of bell peppers were referenced by measuring total soluble solids (TSS), ° Brix, using refractometry. The proposed method was integrated in four stages. The first one consists in the image acquisition of five views using the Raspberry Pi 5 Megapixel camera. The second one is the segmentation of acquired image samples, where background and noise are removed from each image. The third phase is the segmentation of the regions of interest (green, yellow, orange and red) using the connect components algorithm to select areas. The last phase is the classification, which outputs the maturity stage. The classificatory was designed using Matlab’s Fuzzy Logic Toolbox and Deep Learning Toolbox. Its implementation was carried out onto Raspberry Pi platform. It tested the maturity classifier models using neural networks (RBF-ANN) and fuzzy logic models (ANFIS) with an accuracy of 100% and 88%, respectively. Finally, it was constructed with a content of ° Brix prediction model with small improvements regarding the state of art.

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Autonomous Mars Rover Design using SOLIDWORKS and User Interface development via Internet of Things

International Journal for Research in Applied Science and Engineering Technology ◽

10.22214/ijraset.2021.38639 ◽

2021 ◽

Vol 9 (10) ◽

pp. 1441-1451

Author(s):

Srutanjay Ramesh

Keyword(s):

User Interface ◽

Internet Of Things ◽

Graphical User Interface ◽

Vision System ◽

Raspberry Pi ◽

Sensors And Actuators ◽

Multiple Control ◽

Mars Rover ◽

Voice Control ◽

Motion Studies

Abstract: In this paper, an autonomous Mars Rover is designed using the software SOLIDWORKS and a mechanical model is developed with in-depth simulations to analyse the functions of the vehicle. Furthermore, a graphical user interface is also developed based on the principles of Internet of Things using Node-Red to control and monitor the rover remotely. The red planet, i.e.; Mars, has been the centre of attraction for over 2 decades now, with astrophysicists and engineers working in unison to build devices and launch shuttle programs to understand and learn about the planet and gather more intelligence. This paper proposes the detailed development of a 6-wheeled rover that could explore the terrains of Mars, featuring a stereo vision system that could provide live video coverage and a robotic arm that can facilitate investigation of the surface, in an attempt to contribute to and fulfil the human race’s mission to Mars. It employs multiple onboard sensors that can acquire necessary data pertaining to the environmental conditions and actuators that enable functionality, with the sensors and actuators integrated onto a control system based on microcontrollers and microprocessors such as Arduino and Raspberry Pi. The rover also has a provision of a payload bay in its rear which enables it to carry loads. The SOLIDWORKS tool from Dassault systèmes is used to design and model the rover and carry out static analysis and motion studies. The GUI developed in the further sections allows overall voice control for the user and makes the task of monitoring the rover a much simpler task by eliminating the complexity that rises due to multiple control platforms. Keywords: Mars Rover, Graphical User Interface (GUI), Chassis, Mastcam, Actuators, Internet of Things (IoT), Nitinol, Payload

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Spatial Frequency Analysis for Improved Quality in Big Area Additive Manufacturing (BAAM)

Volume 2: Advanced Manufacturing ◽

10.1115/imece2017-70630 ◽

2017 ◽

Cited By ~ 1

Author(s):

Eric MacDonald ◽

Edward Burden ◽

Jason Walker ◽

Jonathan Kelly ◽

Brett Conner ◽

...

Keyword(s):

Computer Vision ◽

Additive Manufacturing ◽

3D Printing ◽

Fourier Analysis ◽

Open Source ◽

Vision System ◽

Raspberry Pi ◽

Grand Challenge ◽

Material Loss ◽

Correct Operation

Process control in 3D printing (also known formally as Additive Manufacturing - AM) has largely been absent even in production systems. Simultaneously, computer vision has become more accessible with open source libraries (e.g. OpenCV, used successfully for traversing the state of California in an autonomous vehicle to win a DARPA Grand Challenge). 3D printing is particularly well suited to be enhanced by computer vision as fabrication is layer wise and predictable assuming correct operation. Big Area Additive Manufacturing (BAAM) — operating at significantly larger scales than traditional 3D printing — stands to benefit given the higher throughput of material (hundreds of pounds per hour) and the associated high costs of errant fabrication. Furthermore, minimum feature sizes in BAAM, such as individual layers, are sufficiently large to be analyzed with standard photography. With computer vision, sophisticated algorithms can be applied to identify problems early in the process that are not normally manifest until after process completion. Subtle and latent defects can be remediated before the onset of permanent damage or at a minimum the process can be aborted to avoid significant material loss. Fourier analysis can provide a useful perspective of the spatial periodicity of the layers of exposed surfaces during fabrication and this spectral information can inform the process of surface roughness, delamination, and deposition consistency in a data efficient manner. The large layer thickness of BAAM allow for Fourier analysis to be performed with standard photography. This paper explores the implementation and advantages of a low cost computer vision system that leverages OpenCV libraries operating on a Raspberry Pi Linux computer with simple yet high resolution photography — driven by the hypothesis that quality and yield of open source BAAM hardware can be dramatically enhanced.

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Application of a computer vision system for recognizing tomato fruits and determining their position relative to the gripper device of the harvesting robot

Journal of Physics Conference Series ◽

10.1088/1742-6596/2091/1/012063 ◽

2021 ◽

Vol 2091 (1) ◽

pp. 012063

Author(s):

A.V. Rybakov ◽

A.N. Marenkov ◽

V.A. Kuznetsova ◽

A.V. Stanishevskaya

Keyword(s):

Computer Vision ◽

Inverse Kinematics ◽

Vision System ◽

Raspberry Pi ◽

Computer Vision System ◽

Tomato Fruits ◽

Hardware Complex ◽

Inverse Kinematics Problem ◽

Plat Form ◽

Harvesting Robot

Abstract The article presents a method for recognizing tomato fruits covered with foliage, de-termining their centers and boundaries using the OpenCV computer vision library and a hardware complex based on Raspberry Pi 4. The methods for solving the inverse kinematics problem for the five-link robotic manipulator designed by the authors, installed on a mobile plat-form, in order to create a robot for collecting fruits are considered. The simulation of the manipulator movement in the Scilab environment is performed.

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Autonomous navigation system with small four-wheel drive platform

E3S Web of Conferences ◽

10.1051/e3sconf/202016605004 ◽

2020 ◽

Vol 166 ◽

pp. 05004

Author(s):

Martin Bogdanovskyi ◽

Andrii Tkachuk ◽

Oleksandr Dobrzhanskyi ◽

Anna Humeniuk

Keyword(s):

Autonomous Navigation ◽

Hybrid Control ◽

Vision System ◽

Movement Control ◽

Image Sensor ◽

Obstacle Detection ◽

Raspberry Pi ◽

Navigation Systems ◽

Wheel Drive ◽

Four Wheel Drive

The task of achieving greater flexibility and maneuverability of small transport and service units’ motion in modern factories by developing small autonomous navigation systems plays crucial role in complex automation of transport logistics nowadays. To solve navigation task, it was proposed the following approach, where as a means of assessing the environment was used computer vision system based on 5-megapixel CMOS image sensor and for the front obstacle detection was used auxiliary ultrasonic sensor as a limit switch. Authors solved the problem of yawing using artificial marking approach as along two-colored leading lines. For maneuverability increase during the turn was used speed movement control based on lines perspective. The basic design and technical characteristics of the four-wheel drive platform and the algorithm of the Raspberry PI 3/Arduino Nano hybrid control system are presented. Experimental results proved the viability of the proposed approach.

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