Abstract: This paper presents the results of a mechatronic design based on a 4-wheel omnidirectional mobile robot that can be used in indoor logistic applications. The low-level control has been selected using two open-source hardware (Raspberry Pi 3 Model B+ and Arduino Mega 2560) that control four industrial motors, four ultrasound sensors, four optical encoders, a vision system of two cameras, and a Hokuyo URG-04LX-UG01 laser scanner. Moreover, the system is powered with a lithium battery that can supply 24 V DC and a maximum current-hour of 20Ah.The Robot Operating System (ROS) has been implemented in the Raspberry Pi and the performance is evaluated with the selection of the sensors and hardware selected. The mechatronic system is evaluated and proposed safe modes of power distribution for controlling all the electronic devices based on different tests. Therefore, based on different performance results, some recommendations are indicated for using the Raspberry Pi and Arduino in terms of power, communication, and distribution of control for different devices. According to these recommendations, the selection of sensors is distributed in both real-time controllers (Arduino and Raspberry Pi). On the other hand, the drivers of the cameras have been implemented in Linux and a python program has been implemented to access the cameras. These cameras will be used for implementing a deep learning algorithm to recognize people and objects. In this way, the level of intelligence can be increased in combination with the maps that can be obtained from the laser scanner.
Abstract: Since the vision system application in industrial environment for autonomous purposes is required intensely, the image recognition technique becomes an important research topic. Here, deep learning algorithm is employed in image system to recognize the industrial object and integrate with a 7A6 Series Manipulator for object automatic gripping task. PC and Graphic Processing Unit (GPU) are chosen to construct the 3D Vision Recognition System. Depth Camera (Intel RealSense SR300) is employed to extract the image for object recognition and coordinate derivation. The YOLOv2 scheme is adopted in Convolution neural network (CNN) structure for object classification and center point prediction. Additionally, image processing strategy is used to find the object contour for calculating the object orientation angle. Then, the specified object location and orientation information are sent to robotic controller. Finally, a six-axis manipulator can grasp the specific object in a random environment based on the user command and the extracted image information. The experimental results show that YOLOv2 has been successfully employed to detect the object location and category with confidence near 0.9 and 3D position error less than 0.4 mm. It is useful for future intelligent robotic application in industrial 4.0 environment.
Abstract: In this paper we present the design and the implementation of a target tracking system where the target is set to be a moving person in a video sequence. The system can be applied easily as a vision system for mobile robot. The system is composed of two major parts the first is the detection of the person in the video frame using the SVM learning machine based on the “HOG” descriptors. The second part is the tracking of a moving person it’s done by using a combination of the Kalman filter and a modified version of the Camshift tracking algorithm by adding the target motion feature to the color feature, the experimental results had shown that the new algorithm had overcame the traditional Camshift algorithm in robustness and in case of occlusion.
Abstract: The shear modulus of a timber beam can be determined
using torsion test or shear field test method. The shear field test
method is based on shear distortion measurement of the beam at the
zone with the constant transverse load in the standardized four-point
bending test. The current code of practice advises using two metallic
arms act as an instrument to measure the diagonal displacement of
the constructing square. The size and the position of the constructing
square might influence the shear modulus determination. This study
aimed to investigate the size and the position effect of the square
in the shear field test method. A binocular stereo vision system has
been employed to determine the 3D displacement of a grid of target
points. Six glue laminated beams were produced and tested. Analysis
of Variance (ANOVA) was performed on the acquired data to evaluate
the significance of the size effect and the position effect of the square.
The results have shown that the size of the square has a noticeable
influence on the value of shear modulus, while, the position of the
square within the area with the constant shear force does not affect
the measured mean shear modulus.
Abstract: Machine vision system provides automatic inspection to reduce manufacturing costs considerably. However, only a few principles have been found to optimize machine vision system and help it function more accurately in industrial practice. Mostly, there were complicated and impractical design techniques to improve the accuracy of machine vision system. This paper discusses implementing the Six Sigma Define, Measure, Analyze, Improve, and Control (DMAIC) approach to optimize the setup parameters of machine vision system when it is used as a direct measurement technique. This research follows a case study showing how Six Sigma DMAIC methodology has been put into use.
Abstract: The timber beam end effect in the torsion test is
evaluated using binocular stereo vision system. It is recommended by
BS EN 408:2010+A1:2012 to exclude a distance of two to three times
of cross-sectional thickness (b) from ends to avoid the end effect;
whereas, this study indicates that this distance is not sufficiently far
enough to remove this effect in slender cross-sections. The shear
modulus of six timber beams with different aspect ratios is determined
at the various angles and cross-sections. The result of this experiment
shows that the end affected span of each specimen varies depending
on their aspect ratios. It is concluded that by increasing the aspect
ratio this span will increase. However, by increasing the distance
from the ends to the values greater than 6b, the shear modulus trend
becomes constant and end effect will be negligible. Moreover, it is
concluded that end affected span is preferred to be depth-dependent
rather than thickness-dependant.
Abstract: In this study a system of machine vision and singulation was developed to separate paddy from rice and determine paddy husking and rice breakage percentages. The machine vision system consists of three main components including an imaging chamber, a digital camera, a computer equipped with image processing software. The singulation device consists of a kernel holding surface, a motor with vacuum fan, and a dimmer. For separation of paddy from rice (in the image), it was necessary to set a threshold. Therefore, some images of paddy and rice were sampled and the RGB values of the images were extracted using MATLAB software. Then mean and standard deviation of the data were determined. An Image processing algorithm was developed using MATLAB to determine paddy/rice separation and rice breakage and paddy husking percentages, using blue to red ratio. Tests showed that, a threshold of 0.75 is suitable for separating paddy from rice kernels. Results from the evaluation of the image processing algorithm showed that the accuracies obtained with the algorithm were 98.36% and 91.81% for paddy husking and rice breakage percentage, respectively. Analysis also showed that a suction of 45 mmHg to 50 mmHg yielding 81.3% separation efficiency is appropriate for operation of the kernel singulation system.
Abstract: Motion Tracking and Stereo Vision are complicated,
albeit well-understood problems in computer vision. Existing
softwares that combine the two approaches to perform stereo motion
tracking typically employ complicated and computationally expensive
procedures. The purpose of this study is to create a simple and
effective solution capable of combining the two approaches. The
study aims to explore a strategy to combine the two techniques
of two-dimensional motion tracking using Kalman Filter; and depth
detection of object using Stereo Vision. In conventional approaches
objects in the scene of interest are observed using a single camera.
However for Stereo Motion Tracking; the scene of interest is
observed using video feeds from two calibrated cameras. Using two
simultaneous measurements from the two cameras a calculation for
the depth of the object from the plane containing the cameras is made.
The approach attempts to capture the entire three-dimensional spatial
information of each object at the scene and represent it through a
software estimator object. In discrete intervals, the estimator tracks
object motion in the plane parallel to plane containing cameras and
updates the perpendicular distance value of the object from the plane
containing the cameras as depth. The ability to efficiently track
the motion of objects in three-dimensional space using a simplified
approach could prove to be an indispensable tool in a variety of
surveillance scenarios. The approach may find application from high
security surveillance scenes such as premises of bank vaults, prisons
or other detention facilities; to low cost applications in supermarkets
and car parking lots.
Abstract: This article is deal with the experimental
investigations of the laser diode matrixes (LDM) based on the
AlGaAs/GaAs heterostructures (lasing wavelength 790-880 nm) to
find optimal LDM parameters for active vision systems. In particular,
the dependence of LDM radiation pulse power on the pulse duration
and LDA active layer heating as well as the LDM radiation
divergence are discussed.
Abstract: Skin detection is an important task for computer
vision systems. A good method of skin detection means a good and
successful result of the system.
The colour is a good descriptor for image segmentation and
classification; it allows detecting skin colour in the images. The
lighting changes and the objects that have a colour similar than skin
colour make the operation of skin detection difficult.
In this paper, we proposed a method using the YCbCr colour space
for skin detection and lighting effects elimination, then we use the
information of texture to eliminate the false regions detected by the
YCbCr skin model.
Abstract: Eyes are an essential and conspicuous organ of the human body. Human eyes are outward and inward portals of the body that allows to see the outside world and provides glimpses into ones inner thoughts and feelings. Inevitable blindness and visual impairments may results from eye-related disease, trauma, or congenital or degenerative conditions that cannot be corrected by conventional means. The study emphasizes innovative tools that will serve as an aid to the blind and visually impaired (VI) individuals. The researchers fabricated a prototype that utilizes the Microsoft Kinect for Windows and Arduino microcontroller board. The prototype facilitates advanced gesture recognition, voice recognition, obstacle detection and indoor environment navigation. Open Computer Vision (OpenCV) performs image analysis, and gesture tracking to transform Kinect data to the desired output. A computer vision technology device provides greater accessibility for those with vision impairments.
Abstract: This paper presents a solution for a robotic
manipulation problem. We formulate the problem as combining
target identification, tracking and interception. The task in our
solution is sensing a target on a conveyor belt and then intercepting
robot-s end-effector at a convenient rendezvous point. We used
an object recognition method which identifies the target and finds
its position from visualized scene picture, then the robot system
generates a solution for rendezvous problem using the target-s initial
position and belt velocity . The interception of the target and the
end-effector is executed at a convenient rendezvous point along the
target-s calculated trajectory. Experimental results are obtained using
a real platform with an industrial robot and a vision system over it.
Abstract: This article gives a short preview of the new software
created especially for palletizing process in automated production
systems. Each chapter of this article is about problem solving in
development of modules in Java programming language. First part
describes structure of the software, its modules and data flow
between them. Second part describes all deployment methods, which
are implemented in the software. Next chapter is about twodimensional
editor created for manipulation with objects in each
layer of the load and gives calculations for collision control. Module
of virtual reality used for three-dimensional preview and creation of
the load is described in the fifth chapter. The last part of this article
describes communication and data flow between control system of
the robot, vision system and software.
Abstract: This paper introduces our first efforts of developing a
new team for RoboCup Middle Size Competition. In our robots we
have applied omni directional based mobile system with omnidirectional
vision system and fuzzy control algorithm to navigate
robots. The control architecture of MRL middle-size robots is a three
layered architecture, Planning, Sequencing, and Executing. It also
uses Blackboard system to achieve coordination among agents.
Moreover, the architecture should have minimum dependency on low
level structure and have a uniform protocol to interact with real
robot.
Abstract: In the domain of machine vision, the
measurement of length is done using cameras where the
accuracy is directly proportional to the resolution of the
camera and inversely to the size of the object. Since most of
the pixels are wasted imaging the entire body as opposed to
just imaging the edges in a conventional system, a double
aperture system is constructed to focus on the edges to
measure at higher resolution. The paper discusses the
complexities and how they are mitigated to realize a practical
machine vision system.
Abstract: Recent developments in automotive technology are focused on economy, comfort and safety. Vehicle tracking and collision detection systems are attracting attention of many investigators focused on safety of driving in the field of automotive mechatronics. In this paper, a vision-based vehicle detection system is presented. Developed system is intended to be used in collision detection and driver alert. The system uses RGB images captured by a camera in a car driven in the highway. Images captured by the moving camera are used to detect the moving vehicles in the image. A vehicle ahead of the camera is detected in daylight conditions. The proposed method detects moving vehicles by subtracting successive images. Plate height of the vehicle is determined by using a plate recognition algorithm. Distance of the moving object is calculated by using the plate height. After determination of the distance of the moving vehicle relative speed of the vehicle and Time-to-Collision are calculated by using distances measured in successive images. Results obtained in road tests are discussed in order to validate the use of the proposed method.
Abstract: In this paper, a solution is presented for a robotic
manipulation problem in industrial settings. The problem is sensing
objects on a conveyor belt, identifying the target, planning and
tracking an interception trajectory between end effector and the
target. Such a problem could be formulated as combining object
recognition, tracking and interception. For this purpose, we integrated
a vision system to the manipulation system and employed tracking
algorithms. The control approach is implemented on a real industrial
manipulation setting, which consists of a conveyor belt, objects
moving on it, a robotic manipulator, and a visual sensor above the
conveyor. The trjectory for robotic interception at a rendezvous point
on the conveyor belt is analytically calculated. Test results show that
tracking the raget along this trajectory results in interception and
grabbing of the target object.
Abstract: This paper presents the theoretical background and
the real implementation of an automated computer system to
introduce machine vision in flower, fruit and vegetable processing
for recollection, cutting, packaging, classification, or fumigation
tasks. The considerations and implementation issues presented in this
work can be applied to a wide range of varieties of flowers, fruits and
vegetables, although some of them are especially relevant due to the
great amount of units that are manipulated and processed each year
over the world. The computer vision algorithms developed in this
work are shown in detail, and can be easily extended to other
applications. A special attention is given to the electromagnetic
compatibility in order to avoid noisy images. Furthermore, real
experimentation has been carried out in order to validate the
developed application. In particular, the tests show that the method
has good robustness and high success percentage in the object
characterization.
Abstract: This paper is concerned with the application of the vision control algorithm for robot's point placement task in discontinuous trajectory caused by obstacle. The presented vision control algorithm consists of four models, which are the robot kinematic model, vision system model, parameters estimation model, and robot joint angle estimation model.When the robot moves toward a target along discontinuous trajectory, several types of obstacles appear in two obstacle regions. Then, this study is to investigate how these changes will affect the presented vision control algorithm.Thus, the practicality of the vision control algorithm is demonstrated experimentally by performing the robot's point placement task in discontinuous trajectory by obstacle.
Abstract: This paper presents an new vision technique for
robotic manipulation of randomly oriented objects in industrial
applications. The proposed approach uses 2D and 3D vision for
efficiently extracting the 3D pose of an object in the presence of
multiple randomly positioned objects. 2D vision permits to quickly
select the objects of interest for 3D processing with a new modified
ICP algorithm (FaR-ICP), thus reducing significantly the processing
time. The extracted 3D pose is then sent to the robot manipulator for
picking. The tests show that the proposed system achieves high
performances