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The development of a virtual scenario is desirable in many hostile environments such as in mining or sewer pipelines to inspect its current condition and to identify possible failures. Obtaining a virtual image through the reconstruction of the mineshaft or the inner pipe is a valuable tool for assessment and fault detection. The approach presented in this paper uses a fuzzy controlled robot so that the video information can reliably be taken. The robot fuzzy control guarantees its motion in a straight line while taking the images from the pipe. The control algorithm therefore prevents the robot from overturning while moving inside the pipe. Results from our 3D reconstruction approach using the autonomous mobile robot is presented. The proposed method overcomes the limitations of different detectors by using a novel combination of DoG and Harris Laplace. Once the frame selection is made, then the regions with affine transformations are used to find image features as correspondences using image descriptors such as SIFT (Scale-Invariant Feature Transform). The correspondences are required to obtain the orientation and position of the camera and to obtain the scene structure leading to the creation of the 3D virtual environment. The 3D reconstruction method outperformed similar commercial software which highlights its potential.

Fractal behavior and long-range dependence are widely observed in measurements and characterization of traffic flow in high-speed computer networks of different technologies and coverage levels. This paper presents the results obtained when applying fractal analysis techniques on a time series obtained from traffic captures coming from an application server connected to the Internet through a high-speed link. The results obtained show that traffic flow in the dedicated high-speed network link have fractal behavior when the Hurst exponent is in the range of 0.5, 1, the fractal dimension between 1, 1.5, and the correlation coefficient between -0.5, 0. Based on these results, it is ideal to characterize both the singularities of the traffic and its impulsiveness during a fractal analysis of temporal scales. Finally, based on the results of the time series analyses, the fact that the traffic flows of current computer networks exhibit fractal behavior with a long-range dependency is reaffirmed., 7 pages, 2 figures, 6 tables, preprint for IEEE Transactions on Information Theory 2021

This work describes an array of a $2\times 1$ microstrip patch antenna with 3.5 GHz of center frequency for the WiMax applications. In order to design and simulate this array of two by one with microstrip line feed technique, the software used was the Computer Simulation Tool (CST) by Microwave. The design of the antenna array was made on Rogers RO3035 substrate with the following characteristics: overall size of $110 \times 87\times 1.52\ \text{mm}^{3}$ and dielectric substrate with $\sigma_{r}=3.5$ . The structural design for the antenna was created on the CST software and the characteristic impedance for the transmission line model was used as the basis. The following are the characteristics of the microstrip antenna: small size, light weight, low cost, compatibility with rigid surfaces. Microstrip antennas are low-profile antennas. At first, a single-patch microstrip antenna was designed and simulated, obtaining results that are represented in graphics. Next, a $2\times 1$ square patch microbrand antenna array was designed.

In this paper, we present the design of two different antennas with a band of 2.45 GHz, and their comparison, to calculate the SAR level for the IEEE 802.15.6. The first design consists on a dipole antenna with geometrics of a half wave. The second design is that of a rectangular microstrip antenna using the feedline method. Both of the antennas are designed with the goal of them working in wireless applications. The CST Simulator tool is used to examine the main characteristics of the antennas: return loss, VWSW, gain, and radiation pattern. In this research, we evaluate and compare the SAR levels of the dipole antenna with those of the microstrip antenna. In order to evaluate the maximum SAR levels, the antenna is used in a vertical position on a homogeneous model. In order to calculate the SAR levels for the tissues, we follow the two standards that are accepted nowadays by the FCC and ICNIRP.

This article explores the required amount of time series points from a high-speed computer network to accurately estimate the Hurst exponent. The methodology consists in designing an experiment using estimators that are applied to time series addresses resulting from the capture of high-speed network traffic, followed by addressing the minimum amount of point required to obtain in accurate estimates of the Hurst exponent. The methodology addresses the exhaustive analysis of the Hurst exponent considering bias behaviour, standard deviation, and Mean Squared Error using fractional Gaussian noise signals with stationary increases. Our results show that the Whittle estimator successfully estimates the Hurst exponent in series with few points. Based on the results obtained, a minimum length for the time series is empirically proposed. Finally, to validate the results, the methodology is applied to real traffic captures in a high-speed computer network., Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible. arXiv admin note: substantial text overlap with arXiv:2103.03984

Purpose of this work is to present the design of a Rover and its autonomous navigation system based in the StereoLSD SLAM algorithm for reconstruction of 3D environments using solely artificial vision and its implementation in the rover type mobile robot for lunar surface exploration named Dereumbot. Other objectives of this work are to describe the systems architecture of such rover and its performance in significant tests, such as NASA competitions and analogue simulations. Finally, encountered challenges and future work for this development is described.

In this article, we present the design of a vision system to take objects without previous knowledge. It is considered that these objects do not have a certain position. The manipulation will be carried out by an industrial robot Kuka KR 5, which does not have a vision system, so a Microsoft Kinect camera is used, as well as the PCA (Principal Component Analysis) algorithm to estimate the position and orientation of the objects within the robot's workspace. Among the tasks to be developed is the communication between the camera and the manipulator, the integration of additional components such as; ROS (Robot Operating System), Open CV, Solidworks and MatLab-SimMechanics. In the first stage, the design of the system as whole is presented. In a following work, the manipulative task (pick and place) is carried out with the manipulator robot.

This paper presents a design of microstrip patch antenna with quarter wave transformer for ISM Band. The antenna is designed over the operating frequency is 2.4 GHz using the substrate material as FR-4. The designed antenna can be used for ISM (industrial, scientific and medical) band applications in Wireless Body Area Networks (WBAN). Different performance parameters of the antenna such as operating frequency, Return Loss, Voltage Standing Wave Ratio (VSWR), directivity, SAR have been analyzed both in planar conditions on the human tissue model by using CST Microwave Studio. Finally, Specific Absorption Rate (SAR) is evaluated to satisfy the antenna safety concern on the human body.

The recognition of objects and the proper handling of them is a common problem in service robots. This article presents a set of computational vision algorithms to improve an object detection system using an RGBD sensor. Within this set of algorithms is the computational vision algorithm to identify a plane, a second algorithm to determine the position of an object and a third algorithm that performs the Principal Components Analysis to identify the orientation of the objects and to perform a correct manipulation. Several tests are presented that validate our approach. This group of algorithms is used by the service robot Justina at the upcoming RoboCup 2018 international tournament [1].

Paper presents a multiplatform server application for service management of mobile devices. Mobile devices can use as an alternative for surveillance and location in real-time taking the advantage of Global Positioning System (GPS) and wireless networks (Wi-Fi) technologies. We propose a monitoring system for personal safety it proposes since monitoring evidence in real-time and storage of multimedia files (image and video information).

The article shows an omnidirectional mobile platform control using the artificial intelligence techinique of Fuzzy Logic; the control allows a practical and reliable driving control of 4 omnidirectional wheels. The control module is implemented in FPGA allowing having an independent and autonomous single chip system out of a central computer dependence to be used in different applications like service robots platforms. An additional feature is performed by using Bluetooth communication with a cellular phone as the handset control device. Driving movements for the mobile platform is limited for eight directions, a Fuzzy Logic module controls the travelling of the platform with independent movements for each wheel, physical feedback is implemented by using electronic decoders, and experimental results were achieved with an additional feature of a handset device control based on a smartphone OS Android with Bluetooth communication. Not using external software and having a system using an artificial intelligence technique implemented in fast hardware, gives the system robust and reliable control capabilities.

Industrial production systems for smart factories or the so-called Industry 4.0 will demand high interoperability and connectivity between production modules, so that modules could be monitored in real-time. Production modules should make decisions on their own, with no human intervention, they must be modular and adaptable to changing circumstances and customers' requirements. The autonomous operation of production modules in smart factories imposes asynchronous delays due to several reasons. For instance, object recognition time, grasping time or welding delays that change due to a newly reoriented or positioned component. Consequently, production modules need to be speeded up to compensate for delays in previous production stages. In this paper, we present a novel Distributed Controller that autonomously compensate production delays. The proposed controller compensates for major production delays that affect the total production time of a product with the objective of maintaining the mean production time. Experiments are reported in this paper which show the feasibility of the algorithm working in a real manufacturing scenario.

The use of dual arm robots for human like operations such as the peg-in-hole tasks can be facilitated by learning contact states during manipulation. In this paper, we propose a control scheme that includes the learning of contact states during operations. The approach includes the use of a Motoman SDA-20 dual-arm robot equipped with two three-fingered gripper and a force/torque sensing capability. A real operation using the assembly of an automotive starter motor is used as a case of study. Several patterns are generated and classified during the stages of the starter assembly. Three categories were considered during operations: moving both arms simultaneously and moving the right arm while keeping the left arm static and vice versa. Contact states were generated using the robot and learned by an Artificial Neural Network (ANN) Fuzzy ARTMAP architecture. The output of the ANN has been envisaged to be a motion command to diminish the constrained forces while moving the arm in the assembly direction. Results during contact state classification have shown that manipulative forces can be recognized by the Neural Network Controller (NNC) so that a valid motion command can be issued to the arm robot favoring the assembly task.

The article presents an intelligent traffic light system that allows optimal flow in a car crossing composed of two one-way streets. To achieve this purpose, real-time image processing is used to obtain the counting and space occupied by the vehicles. The algorithms of Backgroud Subtraction and KNN (K-Nearest Neighbor) are used, to determine the congestion vehicular in a street; the libraries of OpenCV are used. The results obtained give a greater ease and response time.

In this work, the design and implementation of a system to control a greenhouse is presented. This system is a control of the temperature, relative humidity, soil moisture and lighting variables inside a greenhouse. With these variables, a system is configured for a specific type of crops, using sensors, a micro controller Arduino and actuators selected by the user. The system allows maintaining the variables in an appropriate way.

Robot vision systems can differentiate parts by pattern matching irrespective of part orientation and location. Some manufacturers offer 3D guidance systems using robust vision and laser systems so that a 3D programmed point can be repeated even if the part is moved varying its location, rotation and orientation within the working space. Despite these developments, current industrial robots are still unable to recognize objects in a robust manner; that is, to distinguish an object among equally shaped objects taking into account not only the object’s contour but also its form and depth information, which is precisely the major contribution of this research. Our hypothesis establishes that it is possible to integrate a robust invariant object recognition capability into industrial robots by using image features from the object’s contour (boundary object information), its form (i.e., type of curvature or topographical surface information) and depth information (from stereo disparity maps). These features can be concatenated in order to form an invariant vector descriptor which is the input to an artificial neural network (ANN) for learning and recognition purposes. In this paper we present the recognition results under different working conditions using a KUKA KR16 industrial robot, which validated our approach.

A common objective in mobile robotics is to find an optimal trajectory to reach a final destination from a starting point location. Obstacles are likely to be encountered in the robot's trajectory that need to be avoided. In this article, a path planning approach for mobile robots that uses the algorithms of potential field in conjunction with Local Minimal Avoidance (LMA) is presented in order to avoid local minima and to achieve an optimal robot's navigation.

During robot welding operations in the manufacturing industry there is a need to modify on-line the welding path due to a mismatch in the position of the components to be welded. These positioning errors are due to multiple factors such as ageing of the components in the part conveyor system, clamp fixtures, disturbances, etc. Therefore, robot reprogramming is needed which requires a stop in the production line and consequently an increment in production costs. In this article, we present an alternative solution to this problem that involves the use of structured lighting using a low-cost laser beam, a CMOS camera and a Fuzzy Controller. To validate the proposed control system, a robotic cell was designed using an industrial KUKA KR16 robot for welding metallic plates. The method was evaluated experimentally under lateral and vertical positioning errors. The control interface includes apart from the misalignment correction, the on/off control of the welding power supply, arc voltage and current adjustment, welding torch speed and the control of the distance between the torch's tip and the welding plate. Obtained results using the experimental design method showed a maximum error of 1.6mm, which is considered appropriate for the welding of industrial beads in metallic plates and which demonstrates the method's effectiveness in practical situations.

In this paper an unsupervised colour image segmentation algorithm is presented. This method combines the advantages of the approaches based on split&merge and region growing, and the use of the RGB and HSV colour representation models. The effectiveness of the proposed method has been verified by the implementation of the algorithm using three different testing images with homogeneous regions, spatially compact and continuous. It was observed that the proposed algorithm outperforms the other analysed techniques requiring shorter processing time when compared with the other analysed methods.

During robot welding operations in the manufacturing industry there is a need to modify on-line the welding path due to a mismatch in the position of the components to be welded. These positioning errors are due to multiple factors such as ageing of the components in the conveyor system, clamp fixtures, disturbances, etc. Therefore, robot reprogramming is needed which requires a stop in the production line and consequently an increment in production costs. This article is an extension of [1]a and presents an alternative solution to this problem that involves the use of structured lighting using a low-cost laser beam, a CMOS camera and a Gaussian singleton fuzzy logic controller. To validate the proposed control system, a robotic cell was designed using an industrial KUKA KR16 robot for welding metallic plates. The method was evaluated experimentally under lateral and vertical positioning errors.

This paper presents an unsupervised algorithm of colour image segmentation. This method combines the advantages of the approaches based on split and merge and region growing, and the use of the RGB and HSV colour representation models. The effectiveness of the proposed method has been verified by the implementation of the algorithm using three different testing images with homogeneous regions, spatially compact and continuous. It was observed that the proposed algorithm outperforms the other analysed techniques requiring shorter processing time when compared with the other methods.

Invariant object recognition aims at recognising an object independently of its position, scale and orientation. This is important in robot skill acquisition during grasping operations especially when working in unstructured environments. In this paper we present an approach to aid the learning of manipulative skills on-line. We introduce and approach based on an ANN for object learning and recognition using a descriptive vector built on recurrent patterns. Experimental learning results using a fast camera are presented. Some simple parts (i.e. circular, squared and radiused-square) were used for comparing different connectionist models (Backpropagation, Perceptron and FuzzyARTMAP) and to select the appropriate model. Later during experiments, complex figures were learned using the chosen FuzzyARTMAP algorithm showing a 93.8% overall efficiency and 100% recognition rate with not so complex parts. Recognition times were lower than 1 ms, which clearly indicates the suitability of the approach to be implemented in robotic real-world operations.