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ADAPTIVE OPERATION MODEL FOR INTERIOR SMART LOGISTICS IN CYBER PHYSICAL SYSTEMS
(2021) Dönmez, Emrah; Okumuş, Fatih; Kocamaz, Adnan Fatih
Logistics operations are among the main activities in industrial production areas. Today, vehicles that are usually electric and manually operated by a driver are used to perform these operations. Logistics robots are an important alternative that can be used in this field, and their use in integration with cyber physical systems in industrial fields is increasingly common. The biggest advantage of the logistics robots is that they provide autonomous driving capabilities and optimum parameters for the entire system in accordance with industry 4.0 concept. In this study, an adaptive logistics robot system that can be integrated into the Cyber Physical System (CPS) system in an environment with cyber physical system infrastructure has been developed. In this context, positioning, path planning, multi-task allocation, energy management, task prioritization, optimization and obstacle avoidance issues are analyzed and simple solutions are proposed. The experiments have been carried out in eight different configurations and the average distance and energy costs have been improved by 5.1% and 6.6%, respectively.
Application of Blockchain Powered Mobile Robots In Healthcare: Use Cases, Research Challenges and Future Trends
(2022) Okumuş, Fatih; Kocamaz, Adnan Fatih; Şen, Mehmed Oğuz
Using service robots in healthcare is gaining importance in case of emergent situations like pandemics where human labour is considered risky. Multi robot systems of mobile robots have the potential to perform simple but vital tasks in healthcare. However, centralized control with a server computer of these systems carry the risks of single point of failure and ineffective operation of robots, thus decentralized control with blockchain integration offers a better solution. We mention research challenges regarding blockchain powered multi robot systems of mobile robots from use case, blockchain technology and its integration into current computing systems used in medical centers aspects. Then we propose a method for decentralized management and task distribution in a multi robot system by using Hyperledger Fabric as a permissioned blockchain platform and give common use case scenarios. In this system, tasks are assigned to robots depending on the selection of nearest available robots to the task target. Each robot runs the smart contract containing the task assignment method, so that data traffic for the task assignment process is distributed among the network, instead of stacking up on a single line as in a centralized system. Future research issues and directions for future works are also stated as a conclusion.
Aynalı Sazan (Cyprinus carpio) Balığında İmaj J-Fiji ile Sperm Hücresi Hareketlilik ve Hız Analizi
(2018) Erdoğan, Selim; Gürçay, Selahattin; Kocamaz, Adnan Fatih; Ateş, Burhan; Talu, Muhammed Fatih; Okumuş, Fatih; Özgür, Mustafa Erkan
Öz: Öz: Bu çalışma İmage J-Fiji programıyla sperm hücrelerine ait hareket değerlendirme yöntemi, bu yöntemin uygulanabilirliği, kolaylığı ve/veya zorluğunu araştırılması amacıyla yapılmıştır. Elde edilen parametreler, uluslararası yayınlarda sunulmuş benzer bilgisayar sistemleri ile elde edilmiş verilerle karşılaştırmaları yapılmıştır. Çalışmada aynalı sazan (Cyprinus carpio) türü balıklara ait sperm örnekleri incelenmiştir. İncelenen sperm hücrelerine ait hareketlilik parametreleri sırasıyla VSL: 74.05 ?m/sn, VCL: 115.18 ?m/sn, VAP: 63.85?m/sn, BCF: 10.75 Hz ve ALH: 19.28?m olarak hesaplanmıştır. Sonuç olarak, İmaj J-Fiji programı ile balıklarda sperm hücresi yakalama, işleme ve değerlendirme işlemlerinin kolay, uygulanabilir olduğu ve balık üretim merkezlerinde erkek damızlık balıkların sperm kalitesinin belirlenmesinde pratik ve hızlı bir yöntem sunduğu söylenebilir.
Cardiotocography Analysis based on Segmentation-based Fractal Texture Decomposition and Extreme Learning Machine
(Ieee, 2017) Comert, Zafer; Kocamaz, Adnan Fatih
Fetal heart rate (FHR) has notable patterns for the assessment of fetal physiology and typical stress conditions. FHR signals are obtained using cardiotocography (CTG) devices also providing uterine activities simultaneously and fetal movements. In this study, a total of 88 records consisting of 44 normal and 44 hypoxic fetuses instances obtained from publicly available CTU-UHB database have been considered. The basic morphological features supporting clinical diagnosis, the powers of 4 different spectral bands and Lempel Ziv complexity have been used to define FHR signals. Also, it has been proposed to use segmentation-based fractal texture analysis (SFTA) to identify the signals more accurately. The obtained feature set was applied as the input to extreme learning machine (ELM) with 5-fold cross-validation method. According to experimental results, 79.65% of accuracy, 79.92% of specificity, and 80.95% of sensitivity were obtained. It was observed that the SFTA offers useful statistical features to distinguish normal and hypoxic fetuses.
A chaotic optimization method based on logistic-sine map for numerical function optimization
(Springer London Ltd, 2020) Demir, Fahrettin Burak; Tuncer, Turker; Kocamaz, Adnan Fatih
Meta-heuristic optimization algorithms have been used to solve mathematically unidentifiable problems. The main purpose of the optimization methods on problem-solving is to choose the best solution in predefined conditions. To increase performance of the optimization methods, chaotic maps for instance Logistic, Singer, Sine, Tent, Chebyshev, Circle have been widely used in the literature. However, hybrid 1D chaotic maps have higher performance than the 1D chaotic maps. The hybrid chaotic maps have not been used in the optimization process. In this article, 1D hybrid chaotic map (logistic-sine map)-based novel swarm optimization method is proposed to achieve higher numerical results than other optimization methods. Logistic-sine map has good statistical result, and this advantage is used directly to calculate global optimum value in this study. The proposed algorithm is a swarm-based optimization algorithm, and the seed value of the logistic-sine map is generated from local best solutions to reach global optimum. In order to test the proposed hybrid chaotic map-based optimization method, widely used numerical benchmark functions are chosen. The proposed chaotic optimization method is also tested on compression spring design problem. Results and comparisons clearly show that the proposed chaotic optimization method is successful.
Classification and Comparison of Cardiotocography Signals with Artificial Neural Network and Extreme Learning Machine
(Ieee, 2016) Comert, Zafer; Kocamaz, Adnan Fatih; Gungor, Sami
Cardiotocography (CTG) is a monitoring technique that is used routinely during pregnancy and labor to assess fetal well-being. CTG consists of two signals which are fetal heart rate (FHR) and uterine contraction (UC). Twenty-one features representing the characteristic of FHR have been used in this work. The features are obtained from a large dataset consisting of 2126 records in UCI Machine Learning Repository. The prominent features, such as baseline, the number of acceleration and deceleration patterns, and variability recommended by International Federation of Gynecology and Obstetrics (FIGO) have also taken into account during CTG analysis. The features were applied as the input to feedforward neural network (ANN) and Extreme Learning Machine (ELM) to classify FHR patterns in this study. FHR is recently divided into three classes as normal, suspicious and pathological. According to the results of this study, the accuracy of classification of ANN and ELM were obtained as 91.84% and 93.42%, respectively.
Classification of Haploid and Diploid Maize Seeds by Using Image Processing Techniques and Support Vector Machines
(Ieee, 2018) Altuntas, Yahya; Kocamaz, Adnan Fatih; Cengiz, Rahime; Esmeray, Mesut
In vivo maternal haploid technique is now widely used in advanced maize breeding programs. This technique shortens the breeding period and increases the efficiency of breeding. One of the important processes in this breeding technique is the selection of haploid seeds. The fact that this selection is performed manually reduces the selection success and causes time and labor loss. For this reason, it is a need to develop automatic selection methods that will save time and labor and increase selection success. In this study, a method was proposed to classify haploid and diploid maize seeds by using image processing techniques and support vector machines. Firstly, each maize seed is segmented from its original image. Secondly, five different features were extracted for each maize seed. Finally, obtained features vector is classified by using support vector machines. The proposed method performance was tested by 10-fold cross-validation method. As a result of the test, the success rate of haploid maize seed classification was calculated as 94.25% and the success rate of diploid maize seed classification was 77.91%.
Cloud Based Indoor Navigation for ROS-enabled Automated Guided Vehicles
(Ieee, 2019) Okumus, Fatih; Kocamaz, Adnan Fatih
In Cyber-Physical Systems, logistical activities with automatic guided vehicles (AGV) are indispensable for Industry 4.0 integration. In order to navigate the AGVs to be used in logistics, difficulties such as localization of AGV, mapping the environment, mobile-immobile obstacle avoidance, and optimum task allocation must be overcome. All these operations can be realized with an architecture that provides communication infrastructure and management mechanism in multiple robots. In this publication, cloud-based, ROS-enabled communication and navigation methods for multiple AGVs are proposed. The proposed method was tested and applied successfully in a laboratory environment.
A Cloudware Architecture for Collaboration of Multiple AGVs in Indoor Logistics: Case Study in Fabric Manufacturing Enterprises
(Mdpi, 2020) Okumus, Fatih; Donmez, Emrah; Kocamaz, Adnan Fatih
In Industry 4.0 compatible workshops, the demand for Automated Guided Vehicles (AGVs) used in indoor logistics systems has increased remarkably. In these indoor logistics systems, it may be necessary to execute multiple transport tasks simultaneously using multiple AGVs. However, some challenges require special solutions for AGVs to be used in industrial autonomous transportation. These challenges can be addressed under four main headings: positioning, optimum path planning, collision avoidance and optimum task allocation. The solutions produced for these challenges may require special studies that vary depending on the type of tasks and the working environment in which AGVs are used. This study focuses on the problem of automated indoor logistics carried out in the simultaneous production of textile finishing enterprises. In the study, a centralized cloud system that enables multiple AGVs to work in collaboration has been developed. The finishing enterprise of a denim manufacturing factory was handled as a case study and modelling of mapping-planning processes was carried out using the developed cloud system. In the cloud system, RestFul APIs, for mapping the environment, and WebSocket methods, to track the locations of AGVs, have been developed. A collaboration module in harmony with the working model has been developed for AGVs to be used for fabric transportation. The collaboration module consists of task definition, battery management-optimization, selection of the most suitable batch trolleys (provides mobility of fabrics for the finishing mills), optimum task distribution and collision avoidance stages. In the collaboration module, all the finishing processes until the product arrives the delivery point are defined as tasks. A task allocation algorithm has been developed for the optimum performance of these tasks. The multi-fitness function that optimizes the total path of the AGVs, the elapsed time and the energy spent while performing the tasks have been determined. An assignment matrix based on K nearest neighbor (k-NN) and permutation possibilities was created for the optimal task allocation, and the most appropriate row was selected according to the optimal path totals of each row in the matrix. The D* Lite algorithm has been used to calculate the optimum path between AGVs and goals by avoiding static obstacles. By developing simulation software, the problem model was adapted and the operation of the cloud system was tested. Simulation results showed that the developed cloud system was successfully implemented. Although the developed cloud system has been applied as a case study in fabric finishing workshops with a complex structure, it can be used in different sectors as its logistic processes are similar.
Çoklu Hedeflerin Çoklu Robotlara Paylaştırılması İçin Bir Yük Dengeleme Sistemi
(2019) Dönmez, Emrah; Kocamaz, Adnan Fatih
Öz: Günümüzde robotik alanında verilen görevi icra etme, yol planlama, kontrolör tasarımı gibi konularda yaygın olarak tek robot ile yapılan sistemlere odaklanılmaktadır. Çoklu robotlar ve çoklu hedef/görev paylaşımı üzerine ise daha az sayıda çalışmalar bulunmaktadır. Ancak bu alandaki yöntemlerin geliştirilmesi ve kolektif çalışma modelleri üzerine derinlemesine çalışmalar yapılması gerekmektedir. Bu çalışmada, birden fazla robot ile çok sayıda hedefin gezilmesi için görev paylaşımı ve yük dengeleme sistemi (YDS) geliştirilmiştir. Çalışma çok sayıda hedefin asgari maliyet ile gezilmesi bakımından çoklu gezgin satıcı (Çoklu-GSP) problemine de benzemektedir. Görev paylaşımı sisteminde YDS pasif veya aktif durumlarına göre görev dağılımları yapılmıştır. Yük dengelemede amaç bir robota gereğinden fazla görev verilmesinin önüne geçerek enerji ve maliyeti gözetmektir. İlgili robotlara görev dağılımı yapıldıktan sonra robot sayısı kadar hedef küme oluşturulur. Her bir küme için robot konumu ve mevcut hedefler birer çizge düğümü olarak kabul edilmiştir. Oluşan bu çizge düğümleri tam bağlantılı hale getirilerek mesafe matrisi oluşturulmuştur. Daha sonra yol planı başlangıç düğümü olan robotun ilk pozisyonundan hedef düğümlere en yakın komşu (NN) ve genetik algoritma (GA) yöntemleri ile yapılmıştır. Gidilen bir sonraki düğüm yeni başlangıç pozisyonu olarak kabul edilirken, gezilen her bir düğüm ise çizge bağlantı matrisi içerisinden çıkarılmıştır. Hedef ve robotlar renkli etiketler ile etiketlenmiş ve nesnelerin konumları renk tabanlı nicemleme ve eşikleme yöntemleri ile hesaplanmıştır. Yapılan deneyler sonucunda tasarlanan sistemin değişken sayıda ve/veya farklı hedef dağılımlarında iyi bir şekilde görev paylaşımı yaptığı ve elverişli yol planı oluşturduğu gözlemlenmiştir.
Comparing Path Planning Algorithms for Multiple Mobile Robots
(Ieee, 2018) Okumus, Fatih; Kocamaz, Adnan Fatih
The use of mobile robots in industrial applications is increasing day by day. As a result of this increase, efficiency in the use of mobile robots has also become important. In particular, path planning is a significant area of research to improve efficiency in mobile robot navigation. In path planning, it is aimed to find optimum and obstacle free paths to the starting and ending points to fulfill multiple tasks. In this paper, A *, D * and PSO algorithms, frequently used to optimize the path multiple robots reach targets in an environment with obstacles, have been compared. In addition, a mobile robot simulation software has been developed to measure the performance of algorithms. At the end of the study, the performance of the algorithms was showed by measuring the path lengths and the process time of algorithms.
Continuous rotation invariant features for gradient-based texture classification
(Academic Press Inc Elsevier Science, 2015) Hanbay, Kazim; Alpaslan, Nuh; Talu, Muhammed Fatih; Hanbay, Davut; Karci, Ali; Kocamaz, Adnan Fatih
Extracting rotation invariant features is a valuable technique for the effective classification of rotation invariant texture. The Histograms of Oriented Gradients (HOG) algorithm has been proved to be theoretically simple, and has been applied in many areas. Also, the co-occurrence HOG (CoHOG) algorithm provides a unified description including both statistical and differential properties of a texture patch. However, HOG and CoHOG have some shortcomings: they discard some important texture information and are not invariant to rotation. In this paper, based on the original HOG and CoHOG algorithms, four novel feature extraction methods are proposed. The first method uses Gaussian derivative filters named GDF-HOG. The second and the third methods use eigenvalues of the Hessian matrix named Eig(Hess)-HOG and Eig(Hess)-CoHOG, respectively. The fourth method exploits the Gaussian and means curvatures to calculate curvatures of the image surface named GM-CoHOG. We have empirically shown that the proposed novel extended HOG and CoHOG methods provide useful information for rotation invariance. The classification results are compared with original HOG and CoHOG algorithms methods on the CUReT, KTH-TIPS, KTH-TIPS2-a and UIUC datasets show that proposed four methods achieve best classification result on all datasets. In addition, we make a comparison with several well-known descriptors. The experiments of rotation invariant analysis are carried out on the Brodatz dataset, and promising results are obtained from those experiments. (C) 2014 Elsevier Inc. All rights reserved.
Deep Feature Extraction for Detection of Tomato Plant Diseases and Pests based on Leaf Images
(2021) Altuntaş, Yahya; Kocamaz, Adnan Fatih
Plant diseases and pests cause yield and quality losses. It has great importance to detect plant diseases and pests quickly and with high accuracy in terms of preventing yield and quality losses. Plant disease and pest detection performed by plant protection experts through visual observation is a labor-intensive process with a high error rate. Developing effective, fast and highly successful computer-aided disease detection systems has become a necessity in terms of precision agriculture applications. In this study, well-known pre-trained convolutional neural network (CNN) models AlexNet, GoogLeNet and ResNet-50 are used as feature extractors. In addition, a deep learning model that concatenate deep features extracted from 3 CNN models has been proposed. The deep features were used to train the support vector machine classifier. The proposed model was used to classify leaf images of tomato plant diseases and pests, which is a subset of open-access PlantVillage dataset consisting of a total of 18835 images belonging to 10 classes including a healthy one. Accuracy, precision, sensitivity and f-score performance metrics were used with the hold-out validation method in determining model performances. Experimental results show that the detection of tomato plant diseases and pests is possible using concatenated deep features with an overall accuracy rate of 96.99%.
Design of Mobile Robot Control Infrastructure Based on Decision Trees and Adaptive Potential Area Methods
(Springer Int Publ Ag, 2020) Donmez, Emrah; Kocamaz, Adnan Fatih
There have been a great number of studies in the scope of mobile robot systems. The most critical tasks in these systems are control and path planning. The main goal of the control task is to develop a stable control system. On the other hand, the basic motivation in the path planning task is to find a safe path with an acceptable cost. In most researches, a moving robot is considered as a point mass object and only the simulation experiments are applied. In this study, a decision tree-based mobile robot control has been developed for a static indoor environment hosting obstacle(s). The camera has been hung vertically (eye-out-device configuration) to obtain the configuration area map and track the wheeled mobile robot (WMR). A suitable path plan has been extracted with the adaptive artificial potential field (APF) method on the image obtained from the camera. Virtual distance sensors are used to calculate the potentials for APF. A decision tree-based controller has been developed to model the motion characteristics of the robot. A trigonometry-based approach is used to calculate the controller inputs. The controller has steered the WMR on the path in real time. Both simulation and real-world experiments have been conducted on a WMR in different configuration spaces. It has been determined that the designed system is convenient for controlling the WMR. The data obtained are compared to show the difference between the desired and actual path planning results. The efficiency of the controller method has been greatly improved by using dynamic parameters in the control modules.
Detection and Counting of Oncorhynchus Mykiss Spermatozoa
(Ieee, 2015) Okumus, Fatih; Kocamaz, Adnan Fatih; Ozgur, Mustafa Erkan
Today in the world, a large portion of the trout species grown in the fisheries constitutes Oncorhynchus Mykiss. In order to increase the efficiency of Oncorhynchus Mykiss's production, it is expected to be in fair value of its sperm quality parameters for fertilize. Investigation by examination under a microscope with the conventional method of quality parameters is the waste of time for researchers. In this work, we present a system that can detect the cell concentration, which is an important quality parameter for the fish spermatozoa, with Otsu Threshold and Connected Component Labeling methods.
Determination of Individual Investors' Financial Risk Tolerance by Machine Learning Methods
(Ieee, 2020) Altuntas, Yahya; Kocamaz, Adnan Fatih; Ulkgun, Abdullah Mert
Financial risk tolerance refers to the amount of risk that an investor is willing to take in order to obtain returns. In this study, it was aimed to heuristically determine the individual investor financial risk tolerance by using demographic and socioeconomic variables. For this purpose, a questionnaire consisting of two parts was applied to blond University Computer Engineering Department students and administrative and academic staff. In the first part of the questionnaire, demographic and socioeconomic information of the participants were taken, and in the second part, 13 questions aiming to measure the financial risk tolerance were asked. The participants were labeled as risk-averse, risk-neutral and risk-loving according to their answers. The obtained data were classified by decision tree, k-nearest neighbor and support vector machine methods. 10-fold cross-validation method was used to determine model performances. According to the results of the experiment, the best classification performance was obtained with a overall accuracy value of 66.67% using the decision tree classifier.
Determination of Potential Flooding of Inebolu Basin by CN Method with a GIS-Based Software
(Ieee, 2019) Katmerlikaya, Samet; Kurucak, Gulin; Dabanli, Ahmet; Kocamaz, Adnan Fatih; Sarica, Ogun Ozan; Dogan, Ahmet; Baltaci, Enis
Inaccurate planning and methods of land cover / use occurring in the basins increase the intensity and frequency of flood events. The fact that floods are predictable before they occur is of great importance for preventive activities. Therefore, modeling of the basin; plays an important role in planning and developing local resources. In this study, flooding in Inebolu basin of Kastamonu province is modeled. In this context, GIS-based, flexible and user-friendly grid-based software is developed. Surface water retention capacity and the amount of water flowing on the surface were calculated for each grid. Thus, flood formation areas could be determined in the region where the study was carried out.
Efficient approach for digitization of the cardiotocography signals
(Elsevier, 2020) Comert, Zafer; Sengur, Abdulkadir; Akbulut, Yaman; Budak, Umit; Kocamaz, Adnan Fatih; Bajaj, Varun
Cardiotocography (CTG) is generally provided on printed traces, and digitization of CTG signal is important for forthcoming assessments. In this paper, a new algorithm relies on the box-counting method is offered for the digitization of the CTG signals from CTG printed traces. The introduced algorithm inputs the CTG printed traces and outputs the digital fetal heart rate (FHR) and uterine contraction (UC) signals. The proposed method initially extracts the CTG signal image and gridded background image. Retrieving of the FHR and UC signals on the gridded background disrupts the background grids. So, we employ an algorithm to fix the degraded lines in the gridded background. After the line fixing operation, the boxes in the horizontal and vertical axes are counted for determining the calibration parameters. A set of specific equations are used to determine the calibration parameters. The signal extraction is performed on by red channel thresholding of input CTG printing images. An open-access CTG intrapartum database comprises 552 samples is used in the experiment. As a result, the average correlation coefficients of FHR and UC signals are 0.9811 +/- 0.0251 and 0.9905 +/- 0.0126, respectively. (C) 2019 Elsevier B.V. All rights reserved.
Engelli Ortamlarda Heterojen Dağılmış Hedefler için İşbirlikçi Çoklu Robotların Vizyon Tabanlı Görev Paylaşımlı Kontrolü
(2018) Dirik, Mahmut; Kocamaz, Adnan Fatih
Robotik alanında kontrol tasarımı, makine görmesi, yol planlama, verilen görevi icra etme gibi temel konular bulunmaktadır. Günümüzde bu konular kapsamında yaygın olarak tek robot ile yapılan sistemlere odaklanılmaktadır. Çoklu robotlar ve çoklu hedef/görev paylasımı üzerine de çalısmalar bulunmaktadır. Ancak bu alandaki yöntemlerin gelistirilmesi ve çesitliliginin artırılması için daha derinlemesine çalısmalar yapılması gerekmektedir. Bu çalısmada birden fazla robot ile çok sayıda hedefin gezilmesine yönelik görev paylasımı sistemi ve uygun yol bulma için bir gezinme algoritması gelistirilmistir. Çalısma bu yönü ile çoklu gezgin satıcı (Multi ? Travelling Salesman Problem ? M-TSP) probleminde de benzemektedir. Görev paylasımı sisteminde yük dengeleme pasif veya aktif durumlarına göre görev dagılımları yapılmıstır. Yük dengelemede amaç bir robota gereginden fazla görev verilmesinin önüne geçmektir. Ilgili robotlara görev dagılımı yapıldıktan sonra robot sayısı kadar hedef küme ortaya çıkmaktadır. Her bir küme için robot konumu ve mevcut hedefler birer çizge dügümü olarak kabul edilmistir. Olusan bu çizge dügümleri tam baglantılı hale getirilerek mesafe matrisi olusturulmustur. Daha sonra baslangıç dügümü olan robotun ilk pozisyonundan hedef dügümlere yakınlık maliyetine göre yol planı yapılmıstır. Gidilen bir sonraki dügüm yeni baslangıç pozisyonu olarak kabul edilirken, gezilen her bir dügüm ise çizge baglantı matrisi içerisinden çıkarılmıstır. Hedef ve robotlar renkli etiketler ile etiketlenmis ve nesnelerin konumları görüntü islemede renk tabanlı nicemleme ve esikleme yöntemleri ile hesaplanmıstır. Yapılan deneyler sonucunda tasarlanan sistemin degisken hedef sayısında ve farklı hedef dagılımlarında iyi bir sekilde görev paylasımı yaptıgı ve elverisli yol planı olusturdugu gözlemlenmistir.
Exploring the Feasibility of a Multifunctional Software Platform for Cloud Robotics
(Ieee, 2018) Okumus, Fatih; Kocamaz, Adnan Fatih
Strengthening the infrastructure of the Internet has made global data access easier and so cloud computing is at the forefront. Cloud technologies are used in the many work area, because they provide big data storage capacity, low computational costs and scalable data management. Cloud robotics, a special form of cloud computing, manages mobile robots with the advantages mentioned above, as well as providing a telepathic agreement mechanism among the robots. This work presents the development of a software platform for managing cloud-based operations such as simultaneous localization and mapping (SLAM), path planning and optimum task distribution for mobile robots. Demonstration software has also been implemented in the study, which provides a simulation environment that allows testing of the developed system. At the end of the study, the cloud software was tested with the demonstration application and the test results were shown.

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