Application of metaheuristic optimization algorithms for image registration in mobile robot visual control

Visual Servoing (VS) of a mobile robot requires advanced digital image processing, and one of the techniques especially fitting for this complex task is Image Registration (IR). In general, IR involves the geometrical alignment of images, and it can be viewed as an optimization problem. Therefore, we propose Metaheuristic Optimization Algorithms (MOA) for IR in VS of a mobile robot. The comprehensive comparison study of three state-of-the-art MOA, namely the Slime Mould Algorithm (SMA), Harris Hawks Optimizer (HHO), and Whale Optimization Algorithm (WOA) is presented. The previously mentioned MOA used for IR are evaluated on 12 pairs of stereo images obtained by a mobile robot stereo vision system in a laboratory model of a manufacturing environment. The MATLAB software package is used for the implementation of the considered optimization algorithms. Acquired experimental results show that SMA outperforms HHO and WOA, while all three algorithms perform satisfactory alignment of images captured from various mobile robot poses

Application of metaheuristic optimization algorithms for image registration in mobile robot visual control

Visual Servoing (VS) of a mobile robot requires advanced digital image processing, and one of the techniques especially fitting for this complex task is Image Registration (IR). In general, IR involves the geometrical alignment of images, and it can be viewed as an optimization problem. Therefore, we propose Metaheuristic Optimization Algorithms (MOA) for IR in VS of a mobile robot. The comprehensive comparison study of three state-of-the-art MOA, namely the Slime Mould Algorithm (SMA), Harris Hawks Optimizer (HHO), and Whale Optimization Algorithm (WOA) is presented. The previously mentioned MOA used for IR are evaluated on 12 pairs of stereo images obtained by a mobile robot stereo vision system in a laboratory model of a manufacturing environment. The MATLAB software package is used for the implementation of the considered optimization algorithms. Acquired experimental results show that SMA outperforms HHO and WOA, while all three algorithms perform satisfactory alignment of images captured from various mobile robot poses

Object Detection and Tracking in Cooperative Multi-Robot Transportation

Contemporary manufacturing systems imply the utilization of autonomous robotic systems, mainly for the execution of manipulation and transportation tasks. With a goal to reduce transportation and manipulation time, improve efficiency, and achieve flexibility of intelligent manufacturing systems, two or more intelligent mobile robots can be exploited. Such multi-robot systems require coordination and some level of communication between heterogeneous or homogeneous robotic systems. In this paper, we propose the utilization of two heterogeneous robotic systems, original intelligent mobile robots RAICO (Robot with Artificial Intelligence based COgnition) and DOMINO (Deep learning-based Omnidirectional Mobile robot with Intelligent cOntrol), for transportation tasks within a laboratory model of a manufacturing environment. In order to reach an adequate cooperation level and avoid collision while moving along predefined paths, our own developed intelligent mobile robots RAICO and DOMINO will communicate their current poses, and object detection and tracking system is developed. A stereo vision system equipped with two parallelly placed industrial-grade cameras is used for image acquisition, while convolutional neural networks are utilized for object detection, classification, and tracking. The proposed object detection and tracking system enables real-time tracking of another mobile robot within the same manufacturing environment. Furthermore, continuous information about mobile robot poses and the size of the bounding box generated by the convolutional neural network in the process of detection of another mobile robot is used for estimation of object movement and collision avoidance. Mobile robot localization through time is performed based on kinematic models of two intelligent mobile robots, and conducted experiments within a laboratory model of manufacturing environment confirm the applicability of the proposed framework for object detection and collision avoidance

THE CONTRIBUTION OF FOOTBALL ELEMENTS TO THE ACTIVITIES OF PRESCHOOL CHILDREN IN THE PHYSICAL EDUCATION CLASS

The goal of this research was to examine the contribution of an innovative program that contains football elements to the activity of preschool children in physical education classes. The research was carried out using a quasi-experimental design with two classes of children of preschool age at Preschool Institution "Cika Jova Zmaj" from Pirot, divided into an experimental (18 subjects) and a control group (16 subjects). The standard physical education program and the experimental football program were implemented over the course of 12 weeks, with a total of 36 hours of physical education or training. The results showed that there are statistically significant differences in the active time of the subjects in class in favor of the experimental group and that this difference was the largest in the seventh week. This research proposes the elements of the football game as one of the traditional forms of physical exercise that shall contribute to the comprehensive motor development of preschool children and additionally activate them in physical education classes

Choline chloride-based deep eutectic solvents in CaO-catalyzed ethanolysis of expired sunflower oil

Choline chloride (ChCl)-based deep eutectic solvents (DESs) with different amides or polyols as hydrogen bond donors were tested as cosolvents in the ethanolysis of expired sunflower oil catalyzed by either calcined or non-calcined CaO. These cosolvents promoted the ethanolysis by a successful activation of non-calcined CaO, which was ascribed to the CaCO3 and Ca(OH)2 dissolution from the surface of the solid catalyst particles. With both calcined and non-calcined CaO, the polyol-based solvents gave higher fatty acid ethyl esters (FAEE) content than the amide-based solvents. Among the amide-based DESs, choline chloride:urea (ChCl:U) was the most efficient activator of non-calcined CaO. Choline chloride:ethylene glycol (ChCl:EG) and choline chloride:propylene glycol (ChCl:PG) were more efficient than choline chloride:glycerol (ChCl:G) even with non-calcined CaO. However, ChCl:G might be more suitable than the others since the use of glycerol, a by-product of the ethanolysis, could reduce the overall biodiesel production costs. FTIR and XRD analyses of the used and separated CaO were performed in order to get more insight into the catalytically active phase(s). Also, the mechanisms of the CaO activation in the presence of the DESs were considered. The phase separation of the reaction mixture was faster in the presence of the DESs. Since ChCl:U and ChCl:G DESs are nontoxic, biodegradable, biorenewable and “green” solvents and provide the elimination of the calcination step of CaO, thus reducing the overall process costs, the non-calcined CaO catalytic systems with these DESs are recommended for further optimization. © 2018 Elsevier B.V.Published version: [https://hdl.handle.net/21.15107/rcub_dais_3694]This is the peer reviewed version of the following article: Troter, D.Z., Todorović, Z.B., Đokić-Stojanović, D.R., Veselinović, L.M., Zdujić, M.V., Veljković, V.B., 2018. Choline chloride-based deep eutectic solvents in CaO-catalyzed ethanolysis of expired sunflower oil. Journal of Molecular Liquids 266, 557–567. [https://doi.org/10.1016/j.molliq.2018.06.106]Supplementary information: [https://hdl.handle.net/21.15107/rcub_dais_3772

Evaluation of the depth of infiltration of urothelial carcinoma in the vesical wall obtained by transurethral intravesical echotomography

Background/Aim. Transitional cell carcinoma (TCC) is the most frequent tumor of the bladder and represents 95−98% of blader neoplasams and 2−3% of all carcinomas in the body. In urogenital oncology more frequent is only prostatic cancer. Evaluation of the depth of infiltration of urothelial carcinoma in the vesical wall represents the clinical base in treatment planning and prognosis. Clinical investigation and convential radiological procedures have a low level of accuracy in estimating the local growth of the tumor. The aims of our investigation were to determine the depth of infiltration of urothelial carcinoma in the vesical wall in the investigated group of patients by transurethral intravesical echotomography (TIE) and computerised tomography (CT scan) and to compare results obtained by both methods with pathohistological (PH) results, and, based on the difference of the results determine which method was more accurate in the evaluation of the depth of infiltration of urothelial carcinoma in the vesical wall. Methods. Thirty patients with TCC of the bladder both genders, aged 51−81 years were involved in our investigation. In all of these patients, radical cystectomy (RC) was performed. This was neccessary to provide the defintive PH result. Transurethral intravesical echotomography was performed by ultrasound scanner type 1846 Bruel and Kjaer, sond type 1850, and the CT scan was perfomed by Pace plus, General Electric, U.S.A. The specimen for the definitive PH result obtained by RC includes all standards of the TNM classification. Results. Using CT scan, the most frequent was T1 stage (17 patients or 56.68%). Using TIE, the most frequent was T2 stage (22 patients or 73.33%). After RC the most frequent was T2 stage (21 patients or 70%). The Kolmogorov-Smirnov test, showed a high significant difference between the results obtained using CT and definitive PH results after RC. The same test showed no statistically significant difference between the results obtained using TIE and definitive PH results. The sensitivity and accurance of TIE compared to definitive PH results was 93.3%, but using CT it was only 33.3%. Conclusion. There was a significant difference between the results obtained using CT and TIE. The results obtained by TIE were in closer relation with the definitive PH results than the results obtained by CT scan. TIE provides more precise evaluation of the depth of infiltration of urothelial carcinoma in the vesical wall than CT scan. We conclude that the use of this procedure in local staging in TCC is justified and represents the clinical basis in the treatment planning and disease outcome prognosis

A Mobile Robot Visual Perception System based on Deep Learning Approach

In this paper, we present the novel mobile robot perception system based on a deep learning framework. The hardware subsystem consists of an Nvidia Jetson Nano development board integrated with two parallelly positioned Basler daA1600-60uc cameras, while the software subsystem is based on the convolutional neural networks utilized for semantic segmentation of the environment scene. A Fully Convolutional neural Network (FCN) based on the ResNet18 backbone architecture is utilized to provide accurate information about machine tool models and background position in the image. FCN model is trained on our custom-developed dataset of a laboratory model of manufacturing environment and implemented on mobile robot RAICO (Robot with Artificial Intelligence based COgnition)

Data Augmentation Methods for Semantic Segmentation-based Mobile Robot Perception System

Data augmentation has become a standard technique for increasing deep learning models’ accuracy and robustness. Different pixel intensity modifications, image transformations, and noise additions represent the most utilized data augmentation methods. In this paper, a comprehensive evaluation of data augmentation techniques for mobile robot perception system is performed. The perception system based on a deep learning model for semantic segmentation is augmented by 17 techniques to obtain better generalization characteristics during the training process. The deep learning model is trained and tested on a custom dataset and utilized in real-time scenarios. The experimental results show the increment of 6.2 in mIoU (mean Intersection over Union) for the best combination of data augmentation strategies

Stereo vision-based algorithm for control of nonholonomic mobile robot

Requirements for an effective and reliable material transport system within advanced manufacturing environment can be fulfilled by using intelligent mobile robots to perform material handling and transportation tasks. In order to re-duce the degree of ambiguity occurring in a dynamic manufacturing environment, mobile robots are equipped with a stereo vision system that can reliably estimate distance to manufacturing entities. In this paper, a new stereo vision-based algorithm for control of nonholonomic mobile robot is proposed. The main control algorithm, based on an error in image parameters (IBVS - Image based visual servoing), is used for positioning of a mobile robot in the de-sired location. For estimation of the error in image parameters, point features are extracted from the current and target camera view via feature detection and description algorithm. A comparison of these algorithms is made on a set of images obtained in laboratory model of the manufacturing environment by using Basler acA1920-25uc cameras. Based on the results of comparison, KAZE feature detection and description algorithm is proven to be best suited for this specific case. In order to verify the stereo visual control system, simulation and real-world experiments are per-formed. Two experiments are conducted on a mobile robot RAICO (Robot with Artificial Intelligence based COgni-tion) in a laboratory model of the manufacturing environment. Experimental results show the effectiveness of the pro-posed stereo visual control system and its applicability in reaching the desired location with minimal accuracy error

Biologically Inspired Optimization Methods for Image Registration in Visual Servoing of a Mobile Robot

Image registration (IR) represents image processing technique that is suitable for use in Visual Servoing (VS). This paper proposes the use of Biologically Inspired Optimization (BIO) methods for IR in VS of nonholonomic mobile robot. The comparison study of three different BIO methods is conducted, namely Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Grey Wolf Optimizer (GWO). The aforementioned optimization algorithms utilized for IR are tested on 24 images of manufacturing entities acquired by mobile robot stereo vision system. The considered algorithms are implemented in the MATLAB environment. The experimental results suggest satisfactory geometrical alignment after IR, whilst GA and PSO outperform GWO