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 USE OF SELECTION METHODS IN HOSPITALITY UPOTREBA METODA SELEKCIJE U HOTELSKOJ INDUSTRIJI

Abstract: Given the fact that human capital is one of the major sources of competitive advantage of each and every organization, it is of vital importance to determine the extent to which selection methods are effective in terms of choosing quality staff. The main aim is to establish positive correlation between job requirements and candidate's competences and qualifications, so as to retain the best candidates. The purpose of this work is to examine employee selection methods during the hiring process and investigate possible differences between different ranking hotels. The survey was carried out in a number of three, four and five-star hotels in Belgrade city region using both qualitative and quantitative questions as well as interviews with the human resources professionals. The rationale behind research was to compare selection methods utilized by different hotel rating categories and investigate their effectiveness in terms of employee placement, regarding both managerial and non-managerial positions. Apstrakt: Obzirom da je ljudski kapital jedan od osnovnih izvora konkurentne prednosti svake organizacije, od vitalnog je značaja da se utvrdi do koje mere su metode selekcije zaposlenih efektivne po pitanju odabira kvalitetnog osoblja. Cilj je da se ostvari podudarnost između zahteva radnog mesta sa jedne i kvalifikacija i sposobnosti kandidata sa druge strane, kako bi se u organizaciji zadržali najbolji kandidati. Svrha ovog rada je da se razmotre metode selekcije tokom procesa zapošljavanja i da se istraže eventualne razlike izmedju praksi hotela različitih kategorija. Sprovedena je anketa sa kvantitativnim i kvalitativnim pitanjima, kao i intervjuu sa zaposlenima iz sektora ljudskih resursa u beogradskim hotelima sa tri, četiri i pet zvezdica. Ovim istraživanjem su upoređene metode selekcije koje koriste hoteli različitih kategorija, kao i efektivnost istih u odabiru zaposlenih, kako za rukovodeće, tako i za pozicije hotelskog osoblja

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

Technology-Based Professional Development: The Case of Elementary School Teachers in Belgrade

Research question: This paper investigated the correlation between the perceptions of strategies that affect professional development and the obstacles to successful implementation of technology-based professional development. Motivation: The research sought to determine elements that make professional development effective in the eyes of teachers, so that they may be more apt to use what they learn in classroom practice. The concept draws upon the TPACK framework while discussion and recommendations draw upon the UTAUT stages that teachers pass through when faced with new innovations. This study looks at the variables of a) time spent teaching, b) level of education, c) knowledge/use of computers, d) class preparation, and e) technology seminars of survey participants, to determine what demographical characteristics may have an impact on certain belief patterns surrounding professional development and technology use. Idea: The idea of this study is to look at the effectiveness of professional development to integrate technology into classroom practice and to allow for recommendations for improved technology-based professional development. Data: Data collected from a paper-based survey was completed by elementary school teachers in the school district of the cit

Inteligentno stereo-vizuelno upravljanje mobilnih robota i optimalno terminiranje tehnoloških procesa - pregled rezultata istraživanja u okviru projekta MISSION4.0/Intelligent stereo-visual mobile robot control and optimal process planning and scheduling – overview of research results within the project MISSION4.0

Projekat MISSION4.0 podrazumevao je, u okviru nekoliko radnih paketa, razvoj inteligentnog stereo-vizuelnog upravljanja mobilnih robota, kao i optimalno planiranje i terminiranje tehnoloških procesa, i to baziranim na tehnikama veštačke inteligencije, posebno na konvolucionim veštačkim neuronskim mrežama i biološki inspirisanim algoritmima optimizacije. Tokom dvogodišnjih intenzivnih naučnih istraživanja razvijena je nova metodologija za autonomnu navigaciju i inteligentno upravljanje mobilnih robota sopstvenog razvoja, nazvanih RAICO i DOMINO. Generisanje optimalnog plana terminiranja tehnoloških procesa, u okviru koga se izvršava i inteligentni unutrašnji transport korišćenjem mobilnih robota, takođe je bio jedan od važnih ciljeva ovih naprednih istraživanja. U ovom radu, dat je pregled nekih od ključnih rezultata projekta MISSION4.0, poput publikovanih u vodećim međunarodnim i nacionalnim naučnim časopisima, objavljenih poglavlja u naučnim monografijama, saopštenih i odštampanih naučnih radova u zbornicima prestižnih konferencija održanih u inostranstvu i regionu, zatim u okviru verifikovanih tehničkih rešenja, kao i preko skupova podataka sa otvorenim pristupom