LEE- Mekatronik Mühendisliği-Doktora

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http://hdl.handle.net/11527/19387

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A control-theoretic approach for vision based quality aware autonomous navigation and mapping toward drone landing

(Graduate School, 2023-12-15) Sözer, Onuralp ; Kumbasar, Tufan ; 518172009 ; Mechatronics Engineering

This thesis presents a novel autonomous navigation approach that is capable of increasing map exploration and accuracy while minimizing the distance traveled for autonomous drone landings. For terrain mapping, a probabilistic sparse elevation map is proposed to represent measurement accuracy and enable the increasing of map quality by continuously applying new measurements with Bayes inference. For exploration, the Quality-Aware Best View (QABV) planner is proposed for autonomous navigation with a dual focus: map exploration and quality. Generated paths allow for visiting viewpoints that provide new measurements for exploring the proposed map and increasing its quality. To reduce the distance traveled, we handle the path-cost information in the framework of control theory to dynamically adjust the path cost of visiting a viewpoint. The proposed methods handle the QABV planner as a system to be controlled and regulate the information contribution of the generated paths. As a result, the path cost is increased to reduce the distance traveled or decreased to escape from a low-information area and avoid getting stuck. The usefulness of the proposed mapping and exploration approach is evaluated in detailed simulation studies including a real-world scenario for a packet delivery drone.
A novel gripper design based on series elastic actuator for object recognition and manipulation

(Graduate School, 2023-03-03) Kaya, Ozan ; Ertuğrul, Şeniz ; 518162009 ; Mechatronics Engineering

Because of Industry 4.0 and its following releases, robotic applications are becoming more significant. The goal of using robots is to automate industrial processes and increase production yield. However, there are still study topics that need to be explored for other problems, such as safety and cooperation. Furthermore, sensor technologies are another important subject for automation. In general, sensors like encoders, cameras, lidar, and proximity are chosen for the control algorithm's feedback sensors. Many times, when only one sensor is used, sensor technologies are insufficient to identify or describe incidental obstacles. Due to this, two or more sensors may be required for continuity and safety. Alternatively, it is proposed that a gripper design with external effect sensitivity may be useful in both reducing the number of sensors and inherently sensing the external effects. For this purpose, a novel gripper mechanism design based on SEA is achieved for object recognition and manipulation. For a low-cost solution, one actuator with a ball-screw mechanism as a linear actuator is used for the fingers' positions. As it is based on SEA, the spring is placed between the linear actuator and the fingers. With this method, the finger can be actuated by one motor. However, they can be rotated independently by external effects. To estimate the external force, the length of the spring is computed by using absolute encoders. As a result of these, the proposed gripper mechanism is sensitive to external effects and can be used for estimating force without any force/torque sensor or tactile sensor. For object recognition, the proposed gripper interacts with the objects placed at the workspace. However, this is not enough to recognize an object. Hence, a DNN model is needed to interpret the interaction between the gripper and an object. Therefore, a DNN model is created in order to achieve recognition by using the points on the defined objects' surfaces. For the training part of DNN, a synthetic data set is generated via CloudCompare. As a result of different hyperparameters' effects on the DNN model, the best model is achieved for the recognition of 11 objects. The experiments are conducted in MEAM laboratory with the gripper mounted on the Staubli Rx160 robot arm. It is proposed for object manipulation that the gripper has the ability to compensate for position faults caused by controller error, an inaccurate model, and so on. The proposed gripper can successfully perform common industrial tasks such as peg-in-hole and surfaces following in collaborative applications. To prove this approach, the experiments are conducted with a haptic device and the gripper mounted on the Staubli Rx160 robot arm and used untrained operators. The results are compared according to control strategies. For this purpose, the user operates the tasks in cases of no guidance and a rigid gripper mechanism, guidance and a rigid gripper mechanism, and a series elastic gripper mechanism with guidance.
Applications of deep reinforcement learning for advanced driving assistance systems

(Graduate School, 2023-07-23) Yavaş, Muharrem Uğur ; Kumbasar, Tufan ; 518162005 ; Mechatronics Engineering

Nowadays, advanced driving support systems are becoming more prevalent every day. For instance, although adaptive cruise control has been present in some mass-produced vehicles since 1980, it is now available in almost every new vehicle model and is becoming usable, especially in congested traffic situations, with the help of developing technology. On the other hand, the autonomous lane centering function developed for highway environments reduces the driving load on drivers. One of the main reasons for the advancement and prevalence of technology is the progress in environmental perception sensors. Decision-making algorithms can obtain high-accuracy positions of lanes and other vehicles' speed and positions on the road by blending data from intelligent camera and radar sensors. Thanks to advancements in artificial intelligence research, the main topic of this thesis is to evaluate the conditions of surrounding vehicles to achieve cruise follow speed, the amount of gas or brake applied, and finally, the lane changing decision by deep reinforcement learning. Deep reinforcement learning is the integration of reinforcement learning theory into new generation artificial neural networks that emerged with the deep learning revolution. In the proposed methods, both the adaptive cruise control and autonomous lane-changing functions designed with deep reinforcement learning have taken more optimal decisions than classical algorithms and the similarity between the decisions taken and those taken by human drivers has been revealed. Adaptive cruise control systems typically calculate the amount of acceleration required to maintain a safe following distance by using information about the distance to the closest vehicle. However, this method is not compatible with human driving behavior, as it involves scanning the entire traffic and taking into account the dynamic elements surrounding the vehicle being driven. In one of our proposed solutions, we designed the adaptive cruise control function using a model-based deep reinforcement learning method. In model-based reinforcement learning, the decision-making policy uses its own internal model during training to minimize interaction with the system. Therefore, one artificial neural network creates the decision-making policy, while a second network creates the internal model. By using the proposed meta-learning approach to train the two neural networks in a closed-loop fashion, we selected two leader vehicle data inputs for the algorithm instead of a single one. In our simulation environment, the model-based artificial intelligence algorithm performed better than the classical intelligent driver model. Additionally, we proposed a hybrid method that switches to the classical driver model if the internal model and real-world observations do not match for a certain period of time, with a fallback mechanism added to the system's internal model. xxiii In the second proposed study on adaptive cruise control, we suggested a discrete driver model inspired by human drivers' use of gas and brake pedals to manipulate them directly. In the analysis performed using data collected from real life, it was observed that drivers were driving at a stable state with certain gas and brake pedals and coped with dynamic conditions by applying delta brake or pedal. Different gas and brake delta levels were determined through statistical inference based on this dataset. In this case, as the inputs of the artificial intelligence algorithm, the position and speeds of all vehicles in a multi-lane highway in front of the vehicle were determined. When considering the superiority of the algorithms that work with a single leader vehicle compared to two leader vehicles on a single lane, the information of the vehicles on the adjacent lanes will help in case of changes in the leading vehicle of the ego vehicle. The deep Q-learning algorithm, which provides the best results in discrete outputs, was used as the decision-making algorithm. In the evaluations performed on both simulation and real test data, the proposed algorithm obtained the highest score. Especially, slowing down the vehicle in line with its own friction by giving a 0 output without pressing both gas and brake pedals, which can be evaluated as tactical decision-making, was frequently preferred by the designed algorithm. The other advanced driver assistance system studied in the thesis work is the autonomous lane-changing function. In the first original study, autonomous lane-changing was designed using deep reinforcement learning method, and the normally long training process was accelerated 5 times with the proposed safety reward feedback. In the autonomous lane-changing problem, the critical task is to process the position and speed information from all vehicles in front and behind in traffic and make safe maneuvers that will cause speed increase at the right time. Especially in complex traffic scenarios created in simulated environments, classical algorithms are adversely affected by sensor uncertainties and noises, and they cannot show optimal performance in the dynamic driving of multiple vehicles. With the uncertainty calculation in the designed deep reinforcement learning algorithm, the confidence level of the decisions made is observed, and progress is made in the important research area of explainable artificial intelligence. It seems that although deep reinforcement learning techniques have achieved significant successes, they still face integration issues in real-world applications. One of the main problems is the lengthy training process, which can take millions of steps, and the fact that policies are optimized through trial and error, making training in real systems impossible. One promising area of research is sim2real transfer, which involves transferring policies trained in simulation directly to real-world applications. In the second original study on autonomous lane changing, a new approach was introduced to measure the transferability between two simulators with different resolutions. The transferability was evaluated using a human-like usage score generated from the traffic situations when lane-changing decisions were made. In the training process, an adjusted reward function was used, and the proposed method outperformed reference methods in terms of both efficiency and safety, achieving the highest human-like lane-changing score.
Applications of multi-agent systems in transportation

(Graduate School, 2023-03-21) Tunç, İlhan ; Söylemez, Mehmet Turan ; 518152012 ; Mechatronics Engineering

Traffic density is a growing drawback of the crowding of cities in contemporary societies. As a consequence of financial and technological innovations, the living standards of people are improving yet this increases the number of cars and traffic density accordingly. Thus, the density of traffic is reducing the quality of life for individuals in metropolitan areas in particular. Traffic is an important factor affecting human life quality in crowded cities. The increasing population and increasing individual vehicle ownership lead to an increase in traffic density. This causes an increase in loss of time and pollution. Traffic density in big cities is an important factor that reduces the quality of human life. Due to the growing population in metropolitan areas and the inadequate infrastructure to accommodate this density, traffic problems are on the rise. As a result, passengers waste more time in traffic, and the amount of emissions, and hence air pollution, also increases. The issue of traffic congestion is a significant concern for numerous metropolitan areas across the globe, as it causes delays, increases commuting time, and contributes to air pollution. Controlling the flow of traffic is problematic in terms of many complexities and uncertainties. Despite this situation, this problem needs to be solved as it reduces productivity and living standards. Modern traffic control methods offer a more effective solution, unlike traditional methods. As traffic congestion continues to increase rapidly in the world, the need to research and apply more effective methods of traffic control than the traditional method is increasing. Solving traffic congestion is one of the most important and complex problems, as it causes chaos in metropolitans, especially during heavy traffic hours. Traditional methods that continue to be used have proven to be inadequate, and as a result, the developing technology has affected all areas as well as the solutions to the traffic control problem. With the emergence of Intelligent Transportation Systems (ITS), utilizing artificial intelligence and communication technologies, a more effective and efficient solution to traffic congestion is possible. Transportation techniques are improving day by day with the pace of growing technology. Intelligent Transportation Systems (ITS) provide advanced services such as high-tech traffic controllers and various transportation modes, reducing the burden on drivers and thus enabling them to meet the need for complex decision-making while on the road. Intelligent transportation solutions have enabled an unprecedented level of data collection within the industry, leading to significant advancements in transportation system management and operation. With the increasing demand and rate of data collection, ITS has also been advancing day by day and increasing the speed of progress of smart transportation systems. ITS can be described as systems consisting of technologies such as electronics, data processing and wireless networks that provide security and efficiency in the transportation network. ITS provides communication and information exchange between each transport unit. These units can be centres that provide information to pedestrians, vehicles, infrastructure, transportation and other peripherals such as traffic lights and other communication and control units. The application of MAS (Multi-Agent Systems) techniques, as a new development in information technology, can help to increase interest in traffic and promote energy-efficient transportation. ITS-based multi-agent technology is an important approach to solving complex traffic problems. The complexity of the elements of the traffic makes them convenient for multi-agent structures. ITS-based multi-agent technology provides us with safer controllers and makes us feel more comfortable in our daily lives. It increases the quality of our lives by decreasing the amount of time spent in traffic and by lowering the amount of emission gases released by our vehicles. The structurally dispersed nature of components in heterogeneous environments causes application difficulties, such as interoperability between agents forming a demand for a unified software platform as an underlying infrastructure. Therefore, it is preferable to use centralized solutions for relatively simple problems such as the one considered in this paper. For both transport decision-makers and drivers, ITS have a great potential for efficient and intelligent traffic management, threat identification, driving comfort and safety. ITS can also provide a flexible approach for the effective management of complex networked transportation systems letting traffic management decision-makers to control signal changes, regulate route flows, and broadcast real-time traffic information. In addition to providing route scheduling, weather forecasting, and emergency services for drivers, ITS (Intelligent Transportation Systems) can also help to reduce driving loads and improve safety. The implementation of ITS (Intelligent Transportation Systems) can generate positive outcomes across a range of areas, spanning from environmental and national security issues to emergency management and transportation. ITS applications can reduce time spent on the road. Short travel times provide economic savings for both individual and commercial vehicles and usually mean less environmental pollution. Intelligent Intersection Management (IIM) technology has started to develop in traffic intersections as part of Traffic Light Control (TLC) systems. Intersections are some of the busiest parts of roads. Therefore, the control of traffic lights plays an important role in decreasing the density. In this thesis, particular attention is given to the control of intersections in order to find solutions to decrease traffic density leading to an increased quality of life in big cities. Intelligent traffic control methods, the use of which is increasing with the development of new methods, are used especially in traffic intersections with high traffic density in order to provide efficient solutions. Control of a single intersection with traffic lights is considered first in the thesis. Various methods, including Fuzzy Logic Control (FLC), Proportional Integral (PI) control and State Space Model Control techniques, have been proposed and compared for a better traffic light controller architecture so as to increase the traffic flow and reduce the overall waiting time of the cars and the emissions released by them. It is demonstrated that the proposed architectures give better results compared to the traditional fixed-time traffic light control method. Different types of traffic intersections are considered in the study. Initially, a simple single-lane traffic intersection with no left or right turn is taken into consideration. Later on, intersections on which three-lane (or four-lane) roads meet with vehicles turning left and right are considered. Finally, a realistic case study, in which the Altunizade Region of Istanbul, is examined to demonstrate the efficiency of some of the proposed methods. The results of simulations indicate that the FLC, in which the positions of the vehicles are used as the state variables, gives superior results in comparison to the other classical methods. In order to increase the efficiency of the FLC further, a built-in learning algorithm is proposed to be used in addition to the FLC. A deep Q-learning method is employed for this purpose as a part of the agent-based traffic light controller. Hence, the resulting intelligent traffic light controller is named DQ-FLSI. In this method, a state matrix which divides the arms of the traffic intersection into cells is used. The traffic light durations are determined using fuzzy logic, and traffic light actions are determined by the help of deep Q-learning. A stability analysis is also carried out for this newly proposed method. Another important traffic problem is route planning. This is particularly important in large cities with complex traffic networks. In order to address this problem, an agent-based traffic route planning method has also been proposed as part of this thesis with the motivation of vehicles choosing the fastest route. In this method, route planning is made by deciding at traffic intersection points. Vehicle agents make decisions when they reach traffic intersections. In this way, dynamic route planning becomes possible for the vehicles. Another solution for the traffic intersection problem is multi-agent reservation-based traffic intersection control. With this method, all vehicles (called agents) can pass the intersection without the need for a traffic light thanks to a traffic intersection agent. A platoon method, which can work in harmony with reservation-based traffic intersection management, is proposed as an improvement in this part of the study. The proposed method aims to reduce the slowdowns that occur when approaching the traffic intersection by properly lining up the vehicles approaching the traffic intersection. It is shown by simulations that the proposed platoon method reduces energy consumption and gas emissions while increasing the average speed of the vehicles, especially as the density of the traffic increases. Work environments for all studied traffic problems are designed and simulated using the SUMO program. Simulation of Urban MObility (SUMO) is an open-source simulation package that works on networks imported from maps, provides various workspaces at micro levels, also allows pedestrian simulation, and has a sufficient set of tools that makes it more reachable.
Classification of ten different motor imagery eeg signals by using deep neural networks

(Graduate School, 2023-08-19) Korhan, Nuri ; Dokur, Zümray ; 518152011 ; Mechatronics Engineering

Brain-Computer Interface (BCI) is a research area that aims at establishing a sustainable communication infrastructure between the brain and machines. The primary purpose of BCI is to restore functionality to paralyzed individuals, but it can also be used for gaming applications. Various modalities such as Electroencephalogram (EEG) and Functional Magnetic Resonance Imaging (fMRI) can be employed in this field. This thesis focuses on EEG-based BCI and specifically explores the classification of ten different motor imagery (MI) tasks using deep neural networks. Motor imagery is a BCI method that aims to detect imagined movements through potential changes on the scalp, which are measured by electrodes during the imagined motor movement. Increasing the number of recognizable tasks in BCI systems, specifically in the field of mechatronics, holds considerable importance. The limited scope of a four-command system significantly inhibits the versatility of these applications, particularly as they become more complex. To illustrate, imagine the operational demands of a drone, which requires absolute control over direction, altitude, speed, and elaborate maneuvers to navigate obstacles in three-dimensional space. The limitations of a four-command system decrease the number of controllable actions, thus undermining the efficacy and the scope of BCI applications. A substantial increase in the number of recognizable tasks in a BCI system signifies not only the expansion of its capabilities, but also a progression in advancing its applicability and versatility. In the first chapter, the problems of BCI are introduced, and the relevant literature is reviewed. In the second chapter, the concepts related to MI, the specific BCI area of interest, are explained. The third chapter examines methods to increase the number of commands in the MI paradigm, discussing previous approaches and the proposed methods. In the fourth chapter, deep learning tools commonly used in the field and employed in this research are introduced and discussed. The fifth and final chapter discusses the obtained results, their implications, and potential future research directions. The findings contribute to the advancement of BCI and demonstrate the feasibility of classifying ten different motor imagery EEG signals using deep neural networks, alongside augmentation, and divergence-based feature extraction. In summarizing the research conducted in this study, emphasis must be placed on the success rates achieved through the application of the developed methods. The techniques of artificial EEG signal generation, data augmentation, and regularization have been utilized, resulting in enhancements in the performance and efficiency of the BCI tasks. The methods employed have demonstrated promising results in various test scenarios. The success rates exceed those observed in traditional approaches documented in the literature. These rates are expanded upon in their respective sections and numerically illustrated in tables within the fifth chapter. Looking at the classification of both simple and combined MI-EEG signals across various studies, mean accuracy rates of around 51.6% and 54.2% were reported using different techniques for feature extraction and classification on three simple and one combined MI-EEG signals across a varied number of subjects. When increasing the number of classes used, as in four simple and four combined MI-EEG signals, a trend of increased mean accuracy was observed. Studies reported accuracy rates of 55% (four simple and four combined classes, dataset 3) and a substantial 70% (four simple and three combined classes) using different methods. The methods developed in this study demonstrate a significant improvement. For dataset 1, the proposed approach achieved an 85% mean accuracy with only DivFE on four simple and six combined classes across three subjects. Dataset 2 shows a 78.6% accuracy across nine subjects. Lastly, for the dataset 3 (four simple and four combined), the model achieved a 77.8% accuracy across seven subjects. These success rates not only validate the effectiveness of the proposed methods but also highlight the potential for future enhancements in BCI applications.
Cooperative control of multi-agent system under time delay

(Graduate School, 2023-09-07) Akkaya, Şirin ; Ergenç, Ali Fuat ; 518142009 ; Mechatronic Engineering

In this Ph.D. dissertation, multi-agent systems are studied in detail using two of the most common examples in practice, which are vehicle platooning systems and formation control of unmanned aerial vehicles. For a better understanding of the study, some basic information such as graph theory, matrix theory, and time-delayed systems are given. Then, the "Cluster Treatment of Characteristic Roots" paradigm, which forms the backbone of the study, is explained, and the existing methods in the literature have been explained. In this study, a new Bezout Resultant matrix-based CTCR method has been proposed, and the steps of the algorithm are explained via simulation examples in detail. The main advantage of the proposed method is that it provides computational convenience for the time-delayed systems in which the degree of characteristic equation is relatively large and not decomposed into factors in obtaining the stability posture of the system in terms of time delay. First, the distributed controller algorithm is selected as the state feedback controller. The closed-loop system matrix is constructed for the cases with and without time delay. The controller coefficients that make the system stable are obtained by using the Routh table and Lyapunov-based methods for the case where the time delay is neglected. However, in the presence of delay, the system is converted into retarded time delay system, and the stability posture is obtained with CTCR methods for single and multiple time delays. Morover, the formation geometry between vehicles is considered as constant policy and constant headway policy. For constant policy, the characteristic equation of the system for delayless and single time delay case, is decomposed into factors, which makes the stability analysis easier. But, this case is not possible for the characteristic equation involved multiple time delay, which direct us to utilize Bezout Resultant matrix-based CTCR method. For constant time headway policy, it is seen that, the characteristic equation cannot decomposed into factors for any cases. So, the sufficient condition is derived for determining the stability of multi-agent system for delay-free case with converted the system matrix to block companion form and block Schwarz form. Then, a PID controller based distributed controller protocol is proposed. The cooperative control problem of multi-agent system with distributed PID controller is converted into an asymptotic stability problem through matrix and state transformations in the absense of time delay. Finally, a Lyapunov function is created and the controller parameters are choosen with the help of linear matrix inequality. In the presence of time delay, the closed-loop system is converted into a neutral-type time delay system. And, the stability posture of the multi-agent system is obtained with the help of Kronecker multiplication and elementary transformation based CTCR method. Finally, all the theoretical studies and simulation results are evaluated with a real-time experimental study. An industrial controller-based real-time simulation for the platoon system with five connected vehicle including a virtual leader is proposed. The constant time headway policy is selected to modeled the desired inter-vehicle distance and the vehicle dynamic states-based distributed control strategy is used to converge to their desired velocities and inter-vehicle distances. Then the multi-agent platooning control problem is converted into LTI system stability analysis problem. The delay-based stability analysis is studied by means of Bezout Resultant matrix-based CTCR method. Numerical simulations are provided to verify the validity of the proposed method. The real-time experiments are carried out on industrial computers to show the applicability of the proposed method in real time systems. The study concluded by evaluating the results and recommendations.
Deep reinforcement learning approach in control of Stewart platform- simulation and control

(Graduate School, 2023-06-08) Yadavari,Hadi ; İkizoğlu, Serhat ; Aghaei Tavakol, Vahid ; 518162002 ; Mechatronics Engineering

As named, this work approaches the Stewart platform's controlling task with reinforcement learning methods, presenting a new simulation environment. The Stewart platform, having a broad range of applications that span from flight and driving simulators to structural test platforms, is a fully parallel robot. Exact control of the Stewart platform is challenging and essential in its applications to deliver the desired performance. The fundamental aim of artificial intelligence is to address complex problems by utilizing sensory information with a high number of dimensions. Reinforcement learning (RL) is a specific area of Machine Learning (ML) that incorporates an agent interacting with its surrounding environment according to some policies to maximize the sum of the future rewards as an objective function. The agent's learning process is based on a reward-penalty scheme according to the quality of the selected action from the policy space. In this manner, RL tries to solve many problems and tasks. The primary focus of this work revolves around acquiring the ability to control a sophisticated model of the Stewart platform through the utilization of cutting-edge deep reinforcement algorithms (DRL) and model-based reinforcement learning algorithms. The question is that why do we need a simulation environment? To learn an optimal policy, reinforcement learning necessitates a multitude of interactions with the environment. Experiences with real robots are expensive, time consuming, hard to replicate, and even dangerous. To safely implement the RL algorithms in real-time applications, a reliable simulation environment that considers all the nonlinearities and uncertainties of the agent environment is inevitable. Therefore, an agent could be trained in the simulation through sufficient trials without concerns about the actual hardware issues. After having accurate parameters of the controller learned by the simulation, they can be transferred to a physical real-time system. With the objective of improving the reliability of learning performance and creating a comprehensive test bed that replicates the system's behavior, we introduce a precisely designed simulation environment. For our simulation environment, we opted for the Gazebo simulator, which is an open-source platform utilizing either Open Dynamic Engine (ODE) or Bullet physics. Integrating Gazebo with ROS can pave the way for efficient complex robotic applications due to the ability to simulate different environments involving multi-agent robots. Although some Computer-Aided Design (CAD-based) simulations of the Stewart platform exist, we choose ROS and Gazebo to benefit from the latest reinforcement learning algorithms with high yield and performance, compatible with the last developed RL frameworks. However, despite many robotic simulations in ROS, it lacks parallel applications and closed linkage structures like the Stewart platform. Consequently, our initial step involves creating a parametric representation of the Stewart platform's kinematics within the Gazebo and Robot Operating System (ROS) frameworks. This representation is then seamlessly integrated with a Python class to facilitate the generation of structures.
Design and evaluation of energy management systems for connected hybrid and electric vehicles

(Graduate School, 2022-07-04) Özdemir, Abdulehad ; Koç, İlker Murat ; 518132001 ; Mechatronics Engineering

Transportation is one of the most significant sources of emissions across various industries. With the effect of Paris Climate Agreement and the Green Deal, environmental concerns and technological progress push the development and market penetration of electric vehicles and hybrid electric vehicles. The number of electric and hybrid vehicles which can be considered as a stepping stone for electric vehicles are increasing day by day. On the other hand, transportation systems are becoming more efficient and safe by the improvement of the communication systems both on the vehicles and the infrastructure. There are significant improvements in connected and autonomous vehicles which has been started with the development of advanced driver assistance systems. The automotive industry, which plays a key role in the development of many accompanying technological ecosystems, is expected to be enhance more changes in the next 10 years than in the past 100 years. It is estimated that this transformation will dominance especially with the technologies progress in connected and autonomous vehicles. The main purpose of the study is to develop smart energy management strategies for connected, hybrid and electric vehicles and evaluate the benefits of developed smart energy management strategies. At the same time, the effects of the transition to electric vehicles in terms of energy consumption and environment is evaluated. For the optimization studies the Well-to-Wheels emission values are calculated and used in order to ensure apple-to-apple comparison. During the thesis study, three articles have been prepared and the preapared articles have been the substructure of the thesis. As of the date of submission of the thesis, one of the articles has been published and the requested revisions have been made for the other two articles and resubmitted. Prepared articles entitled as " Dynamic Programing Based Green Speed Advisory System Design for Mixed Platooning Vehicles", " Driving Cycle Based Energy Management Strategy Development for Range Extended Electric Vehicles " and " Comparative Study on Well-to-Wheels Emissions between Fully Electric and Conventional Automobiles in Istanbul". The article about the comparative study on Well-to-Wheels emissions has been published in the eighty-seventh issue of the "Transportation Research Part D" journal. Turkey's energy mix is analyzed and the emission factor of electricity production of Turkey is estimated in order to make appropriate comparisons during optimization studies. The Well-to-Wheels equivalent carbon dioxide emissions of the electricity is calculated. By considering energy sources, the Well-to-Wheels emission of Turkey is calculated as 520 g carbon dioxide equivalent per kWh. By using the carbon intensity of electricity, it is possible to compare the same variable for electric energy and fossil fuels for hybrid and electric vehicles. Vehicle models are created to use for model-based optimization studies. In order to develop an energy management system for serial hybrid vehicles, all critical subsystems are tested and a vehicle model which is validated by the test data is created. The model is developed by mathematical modelling of vehicle dynamics and testing the the electric motor, motor driver, battery cells and internal combustion engine. The developed models are validated by vehicle level testing on chassis dynamometer. A driving cycle based energy management strategy is developed for range extended electric vehicles to increase system efficiency and equivalent vehicle range. The results showed that; the optimized strategy can save CO2 emission by 6.21%, 1.77% and 0.58% for heavy, moderate and light traffic respectively. The usage of range extender in an efficient way by taking the traffic data into account extends the vehicle range, especially in heavy traffic conditions. For the hybrid vehicles which consumes both electric energy and fossil fuels, It will is important to compare the same value fort he objective function such as equivalent carbon dioxide emission. This study is a good example from this point of view. The developed energy management system will enable connected hybrid vehicles to be in more efficient way by using the route and traffic density information. In addition, vehicle emission maps are developed as a vehicle feature. The vehicles are tested on the chassis dynamometer and emission maps which are based on speed and wheel force are created. It is offered that vehicle emission maps can be used for optimization studies, especially in traffic with different types of vehicles. Considering that there are many ongoing studies on reducing tranportation based emissions, the use of the standardized emission maps are important for system level efficient use of connected vehicles. From this point of view, a multi-layer dynamic programing based optimizer is designed to minimize platooning Well-to-Wheels emissions of platooning vehicles where the platoon consists of an electric, a gasoline and a diesel vehicle. Vehicle emission maps and longitudinal dynamics are used for vehicle modelling. Tank-to-Wheels emission maps of internal combustion engine vehicles are produced by testing the vehicles on a chassis dynamometer. The optimization process has exploration and exploitation layers. The cost function is total Well-to-Wheels emission, design variable is speed trace, constraints are speed limits, traffic light states and vehicle accelerations limits. The test results show that the developed optimizer helps to achieve a 19.8% reduction in total Well-to-Wheels emissions for the defined use case. Thus, there is a significant emission saving potential in using speed advisory system for platooning vehicles through signalized intersections. On the other hand, driving cycles are used to examine the energy consumption and emission emissions of vehicles. In order to analyze the environmental effects of electric vehicles on a real driving cycle, a driving cycle has been developed for Istanbul by statistically analyzing the data collected on the determined routes. By using the developed driving cycle, the vehicle test are conducted. Acoording to the results electric vehicles emit 73.9 g carbon dioxide equivalent per kilometer on the same route, while gasoline vehicles emit 183.4 g equivalent carbon dioxide emissions. Therefore, the transition to electric vehicles should be strengthened by more widespread use of renewable energy in order to effectively reduce emissions associated with electric vehicles in general. At the same time, the results of this study can be a guide for policy makers. In summary, within the scope of the thesis electric carbon intensity of Turkey is calculated by considering Turkey's energy mix and Well-to-Wheels greenhouse gas emissions are analyzed both for conventional and electric vehicles are measured. A dynamic programing based optimizer is developed to decrease total Well-to-Wheels emissions of the mixed conventional and electric platooning vehicles through signalized intersections. Vehicle emission maps are generated both for electric and conventional vehicles for model-based optimization. A driving cycle based energy management strategy is developed for range extended electric vehicles to increase system efficiency and equivalent vehicle range. The vehicle model is developed by critical subsystem testing. An up to date driving cycle for Istanbul is developed (so called Istanbul Driving Cycle) by using collected traffic data across various sections of the city. An internal combustion engine vehicle and an electric vehicle are tested on a chassis dynamometer under the same conditions to determine specific energy consumption and specific emissions.
Developing mobile robot obstacle avoidance methods with model-based and learning-based methods

(Graduate School, 2023-07-19) Özdemir, Aykut ; Bogosyan, Seta O. ; 518162010 ; Mechatronics Engineering

Mobile robot navigation is a crucial area of research and development in robotics that focuses on enabling robots to move autonomously in their environments. Mobile robots are increasingly being used in a wide range of applications, including manufacturing, healthcare, transportation, and search and rescue missions. These robots have the potential to improve efficiency, reduce costs, and enhance safety in a variety of industries. However, for mobile robots to be effective, they must be able to navigate their surroundings with accuracy and reliability. Navigation involves the robot's ability to perceive its environment, plan a path, and execute that path while avoiding obstacles and other hazards. The development of mobile robot navigation systems has been a major area of focus in robotics research for several decades, and it continues to evolve rapidly. Advances in technologies such as sensors, computing, and machine learning have enabled mobile robots to navigate more complex environments and perform increasingly sophisticated tasks. As such, mobile robot navigation is a critical area of study for researchers and engineers who seek to develop intelligent and autonomous systems that can operate in real-world environments. Path planning and obstacle avoidance are two important topics in robotics that are closely related. Path planning refers to the process of determining a safe and efficient path for a robot to travel from its current location to a desired destination. This process takes into account the robot's movement capabilities, the environment it is operating in, and any obstacles that may be present. Obstacle avoidance, on the other hand, involves the robot's ability to detect and avoid obstacles as it navigates its environment. This is an essential component of path planning, as the robot must be able to react to changes in its environment and modify its path accordingly in order to avoid collisions and ensure safety. Both path planning and obstacle avoidance are critical for the development of autonomous robots that can navigate complex environments and perform tasks without human intervention. These topics are the focus of ongoing research in the field of robotics, and advances in technologies such as sensors, mapping algorithms, and machine learning are enabling robots to navigate increasingly complex environments with greater efficiency and safety. This study proposes three novel contributions in the field of robotics. The first is a novel model-based obstacle avoidance method that plans local trajectories by passing through gaps between obstacles. The second is a learning-based sampling method that improves the efficiency of trajectory planning for path planning algorithms. Finally, we proposed a non-holonomic local planner that uses a CNN-based sampling technique. These contributions aim to improve the navigation and path planning capabilities of robots, allowing them to operate more efficiently and safely in complex environments. Overall, this thesis demonstrates the potential of using advanced techniques and technologies, such as machine learning and local planning, to enhance the performance and capabilities of mobile robots.
Development and control of an active torsional vibration damper for vehicle powertrains

(Graduate School, 2021-05-25) Yüceşan, Alişan ; Mugan, Ata ; 518152002 ; Mechatronics Engineering

The emission regulations on internal combustion engines (ICEs) have become more stringent and the importance of fuel efficiency has enhanced due to environmental pollution concerns. As a result, studies on optimization of ICEs put forward the consideration of downsizing, downspeeding and turbo supercharging concepts in designing modern ICEs and powertrains. Despite their numerous advantages, they result in boosted engine torsional vibrations which demands innovative vibration isolation solutions. Such a design solution should be uncomplicated and simple from an automotive manufacturer's point of view, meanwhile, be an extreme performer besides being a cost-effective solution. Passive and active dampers have been utilized to suppress torsional vibrations in the literature. At this point, the passive dampers appear more preferable at the first sight due to their cost advantage while active systems have the disadvantages of having higher costs due to the presence of actuators, sensors and peripherals, advanced complexity and potential of lower efficiency. But conventional passive torsional vibration damper systems like dual-mass flywheel (DMF) reached their limits and they are no longer able to sufficiently isolate the torsional vibrations of the state-of-the-art ICEs. Also, as seen in the literature survey section, there is no kind of active dampers for torsional vibrations which is equipped alone or proposed to be used in this context for vehicle powertrains and there is a huge demand for a new design solution to beat the performance constraints of passive isolation systems.
Diagnosis of brain cancer and contour normal tissue for radiation therapy based on deep learning methods

(Graduate School, 2024-07-18) Halili, Navid ; Doğan, Mustafa ; 518162006 ; Mechatronics Engineering

Brain tumors are one of the deadliest types of cancer ever identified. Rapid and accurate diagnosis of brain tumors, followed by surgical intervention or appropriate treatment, increases the probability of survival. Accurate identification of brain tumors in MRI scans allows precise location of surgical intervention or chemotherapy. Accurate segmentation of brain tumors in MRI scans is challenging due to their varied shapes and requires knowledge and accurate image interpretation. This thesis starts with analyzing machine learning and traditional methods and focuses on the study of edge detection using the Sobel and Canny edge detector algorithm. After that, we use morphology-based techniques to segment the images and evaluate the results. We use K-means techniques for Clustering. Despite various advances, these methods still show limitations in complex situations such as tumor detection and segmentation. In the next step, we analyze the process of dividing photos into parts using transformations. Specifically, we discuss the Wavelet and Contourlet transforms. By using these transformations, we get more detailed information about the analysis of the images. These transformations have many applications, and we can identify the borders of the image and combine them. Finally, we can use this transformation to process and generate deep learning masks using a supervised model. In the following, we analyze new techniques using supervised and deep learning approaches in two specific areas: image classification and image segmentation. As we introduce these methods, we introduce the obstacles facing deep learning and discuss potential strategies to solve and enhance them. Using deep neural networks and the Resnet 50 model, we classify brain images into tumor and non-tumor categories and achieve a satisfactory score of 97% in the F1 criterion. In addition, we introduce and analyze the Unet deep network in deep learning and upgrade it to a RESUNET network for segmentation. The results of this segmentation show that the proposed approach, with different criteria, such as the DICE metric with a score of 0.9434, performs exceptionally during training compared to conventional topologies and shows a faster convergence rate. In the last part, we presented the unsupervised learning system and developed the adversarial generative network to generate brain MRI images. The adversarial generative network is an intelligent network for generating the desired data, and the results show the effectiveness of the adversarial generative network in generating new data. It is of exceptional quality.
Human factor based advanced driver-assistance system (ADAS) design for electric vehicle

(Graduate School, 2022-07-06) Doğan, Dağhan ; Estrada, Ovsanna Seta ; 518122006 ; Mechatronics Engineering

Every year, thousands of traffic accidents occur and thousands of people die or are injured in these accidents. Considering the causes of accidents, it can be said that most of them are human errors. For this reason, studies focus on advanced driver assistance systems, increasing vehicle autonomy levels and driver behavior in traffic, and aim to prevent possible accidents. For a similar purpose, in this study, I aimed to collect data and analyze some human factor technologies that will support advanced driver assistance systems (ADAS) and to produce suggestions on how researchers and manufacturers producing ADAS can use these technologies. Our study focuses on the data of the galvanic skin response (GSR) sensor, which is a wearable sensor and aims to contribute to human factor studies by analyzing the GSR sensor and other sensor data collected from the drivers and the prototype electric vehicle. The study is experimental and requires a realistic vehicle and realistic driver data. Thus, first of all, we aim to design a novel, low-cost and open to development embedded data collection system for the research and education in human factor technologies and ADASs. Equipment used in this simultaneous data acquisition system: an electric vehicle with the power of 750W, Arduino Mega 2560 electronic card, a 10-turn Vishay 860 potentiometer used for steering angle data, the Tamura 300 A AC/DC hall-effect current sensor used for current (torque) data, Pololu force-sensing resistor (FSR) to detect force on the steering wheel and brake pedal, Seeedstudio GSR sensor to detect stress, MinIMU-9 v3 inertial measurement unit (IMU) to detect gyro, accelerometer, and compass, GY-NEO6MV2 global positioning system (GPS) to detect chassis velocity and position, Scancon 2RM 200 encoder to detect wheel velocity and Techsmart dashcam to record the environment and driver behavior. After designing the data collection system and implementing it for the prototype electric vehicle, we are looking for an answer to the question of whether the driver's stress in traffic can be detected with the GSR and FSR sensor data. We collect the GSR and FSR sensor data for 38 drivers using the designed data collection system in the Istanbul Technical University campus and analyze the GSR and FSR sensor data. In addition, a post-driving stress survey is used to improve the reliability and consistency of the stress level analysis and to validate the results. According to analysis results, the GSR sensor detects stress level-gender, stress level-driving experience, stress level-driving frequency and stress level-representative of normal driving behavior relationship, and the FSR sensor determines only gender stress level. Here, stress level-gender results for the GSR and FSR sensor, stress level-driving experience results for the GSR sensor and stress level-driving frequency results for the GSR sensor are consistent with the results of the survey with an accuracy of 100 %. Stress level-representative of normal driving behavior results for GSR sensor are consistent with the results of the survey with an accuracy of 50 %. As a result, the GSR sensor stress results are consistent with the results of the survey with a total accuracy of 87.5 %. The FSR sensor gender stress results are consistent with the results of the survey with an accuracy of 100 %. After the stress level detection study, we collect the IMU, FSR, GSR, current sensor, potentiometer, encoder and GPS data from 38 drivers along a route. Drivers are divided into 2 (risky and normal) classes according to their Euclidean distance from expert driver data for each sensor. The best classification methods are determined along this way for each sensor. Accordingly, all data are classified with the highest accuracy of 92.1% using the Medium Gaussian Support Vector Machine (SVM) method. IMU data is classified with the highest accuracy of 89.5% using the Artificial Neural Network (ANN) method. FSR data is classified with the highest accuracy with 94.7% accuracy using the Medium Gaussian SVM method. GSR data is classified with the highest accuracy with 97.4% accuracy using the Fine K-nearest Neighbors (KNN) method. Current data is classified with the highest accuracy with 100% accuracy using the ANN method. Potentiometer data are classified with the highest accuracy of 97.3% using the ANN method. Encoder data is classified with the highest accuracy with 92.1% accuracy using the Medium Gaussian SVM method. GPS chassis velocity data is classified with the highest accuracy with 94.7% accuracy using the Medium Gaussian SVM method. Thus, we can say that driver behavior is highly predictable for the batch data along a road. Secondly, it is tried to reveal whether the driver behavior we obtained along the above road can be detected instantly. GSR data of the drivers is analyzed individually because the GSR sensor gives the driver instant excitement and stress information. The driving videos of the drivers are shown to the expert driver. The faults and fault moments of the drivers are labeled by the expert driver. On the other hand, the data obtained by the GSR sensor are used to determine when the drivers are excited (stressed) and the reasons for stress are identified by the expert driver. In the analysis, driver-4 (male) and driver-7 (female) data are examined for individual classification. Stress moments are considered class-2 as dangerous situations. Others are considered class-1. In this way, classification methods are applied. As a result, it is found that the fault moments of the drivers are a subset of the stressful moments of the drivers for all drivers. For driver-4, all sensor data which is tagged by stress moments are classified with the highest accuracy with 97.4% accuracy using the ANN method. For driver-7, all sensor data is classified with the highest accuracy with 98.6% accuracy using the Bagged Tree method. Thirdly, we validate the driver status/behavior analysis above by detecting anomalies using Local Outlier Factor (LOF) values for GSR sensor data as a different method. This analysis provides the detection of a driver status with LOF anomaly values of GSR sensor data and other sensor support (camera and GPS) without the need for machine learning. Lastly in the chapter, we analyze the driving confidence in turns. The excitement increases obtained from the GSR sensor on turns have defined the unconfidence of the driver. The velocity and current data of the drivers determined by the GSR sensor in turns are examined and thus drivers are analyzed individually. When the first junction maneuvering data of drivers are analyzed based on the GSR sensor data, drivers numbered 7, 9, 20, 23, 27 and 34 fail at authorizing driving confidence on the first turn. When the second turn data are analyzed, drivers with an ID 9, 20, 23 and 38 could not show a confident drive on the second turn. Skin conductivity information, including abnormal, risky, and unconfident driving information, can be used for torque control of an autonomous electric vehicle. We transform the semi-autonomous electric vehicle into an electric vehicle with longitudinal autonomy. To improve the study, the distance sensor is also used simultaneously with the GSR sensor to detect collisions and intervene. It means that the GSR data is used to control a vehicle with closed-loop and longitudinal autonomy, depending on the driver's condition. Stress data of 38 drivers along a road are obtained above and averaged. These averages are used as input to the system via radio frequency identification (RFID) cards. Thus, an autonomous vehicle with GSR sensor-based torque control is designed. The purpose of this transformation in this study is to integrate our work into autonomous vehicles as well as semi-autonomous vehicles. This shows that biosensors can also be used as input for autonomous vehicles. In the takeover request (TOR) study, different TOR times are tested on five different driving cases with 18 participating drivers. Three of these cases are used to detect the TOR time of drivers, while the other two scenarios are used sequentially to increase the effects of other participating vehicles such as a vehicle approaching the intersection and then is stopped on a lane along the test route causing a possible hazard situation. According to the analysis, drivers do not prefer the authority transition that is very close to the critical situation (TOR 6 s). Because in TOR 6 s, due to the approaching the critical situation, the (g) (pulse deviation) and (fxa) (current deviation multiplied by the average of five consecutive acceleration or deceleration values during manual driving) values are higher and a smooth transition does not occur. It has been observed that most of the drivers make the comfortable and smooth authority transition for TOR 4 s and TOR 2 s. The experienced drivers prefer TOR 4 for authority transition. Since the TOR 0 s authority transition is also a sudden transition, the driver is unready and causes a higher (g) and (fxa). In other words, even if the drivers are far from the critical situation, they do not prefer a sudden authority transition. Take-over request time is evaluated for each driver and three driver categories such as experienced, semi-experienced and inexperienced, and validated by a questionnaire. The TOR time is extracted and personalized for each driver, which may improve the current conditional automated driving technologies' penetration and acceptance. As the experience of the driver increases, more stable results are obtained. The TOR time for inexperienced drivers varies for each case. As a result of data analysis, the wearable biosensor GSR sensor data can be used in different human factor technologies to support ADAS. Because, as seen in our study, we detected the stress and status of the driver using the GSR sensor, detected the driver's fault cluster in traffic and trained it with machine learning methods, transformed a semi-autonomous vehicle into a GSR-based torque-controlled vehicle with longitudinal autonomy, and finally, evaluated takeover request performance using the GSR sensor.
Image processing software tools development for shoulder arthroplasty

(Graduate School, 2021-05-21) Sadeghi, Majid Mohammad ; Ertuğrul, Şeniz ; 518132013 ; Mechatronics Engineering ; Mekatronik Mühendisliği

Reverse shoulder arthroplasty is an operation performed on shoulder joints with diseases such as osteoarthritis and rheumatoid arthritis, complex fractures of the proximal humerus, and osteonecrosis of the humeral head. This operation can face problems and create risky conditions for the patient, which might end in revision operations. In this work, methods are investigated to reduce one of the main reasons for the problems faced in reverse shoulder arthroplasty. This main reason is the wrong positioning of the K-wire which itself results in the wrong positioning of the implant baseplate. The wrong positioning can reduce the range of motion of the shoulder or can lead to complete malfunctioning of the joint. Using pre-operative planning of the surgery, and patient-specific instrumentation, are the solutions evaluated for improvement of the condition and reducing the risk of malpositioning. Preoperative planning which is deciding the correct choice of the procedure before the operation based on the patient, injury type, facilities available, and surgeon's skills, is an important method in improving implant positioning. Preoperative planning can be performed using two-dimensional images of the patient, but use of three dimensional images and computer preoperative planning software tools can improve planning. Patient-specific instrumentation which is a modern orthopaedics technique, uses Computed Tomography or Magnetic Resonance Imaging of a specific patient to create customized guides preoperatively. Prostheses or guides that are designed based on the specific anatomy or injury of a patient provide an opportunity to be implemented more precisely and hence can help improve implant positioning and reduce the risk of complications resulting from malpositioning. The PSI guide generation process is performed using software tools that perform preoperative planning on the three-dimensional models of the patient's data. A new open-source software tool, that provides preoperative planning capability for the surgeon, and also creates a patient-specific guide for K-wire positioning, is developed in this work to test the presented solutions. First, the development of the software, using only open-source platforms, is explained, then using the results of the software an experiment is designed and performed. The experiment evaluated the accuracy of the software results and also compared the results with other existing methods. The experiment contained five different shoulder anatomies and glenoid types. For each type ten different samples were manufactured. Two experienced surgeons experimented on the manufactured bone models and the results were evaluated to differentiate between different anatomies. The results were evaluated to control the version angle, inclination angle, and the entry point location of the K-wire after the experiment. The evaluation of the results presented that this proposed method has good accuracy for all three parameters. Also, the results showed better outcomes for specific types of anatomies when compared to the freehand method and the conventional guide method.
Image reconstruction with deep learning and applications in MR images

(Graduate School, 2022-04-22) Aghabiglou, Amir ; Ekşioğlu, Ender Mete ; 518172001 ; Mechatronics Engineering

In this thesis, in the first step, the novel application of the U-Net structure was considered for the important inverse problem of MRI reconstruction. Deep networks are particularly efficient for the speed-up of the MR image reconstruction process by decreasing the data acquisition time, and they can significantly reduce the aliasing artifacts caused by the undersampling in the k-space. On the first try, it is aimed to develop a novel and efficient unfolding U-Net framework for reconstructing MR images from undersampled k-space data. The new framework should have improved reconstruction performance when compared to competing methodologies. In this step, a novel unfolding framework utilizing the U-Net as a sub-block is being proposed. The introduced U-Net unfolding structure is applied to the magnetic resonance image reconstruction problem. The connection between the unfolding U-Nets is realized in the form of a recently developed projection-based updated data consistency layer. The novel structure is implemented in the PyTorch environment, which is one of the standards for deep learning implementations. The recently created fastMRI dataset which forms an important benchmark for MRI reconstruction is used for training and testing purposes. Despite the many challenges in training rather large networks, novel methodologies have enhanced the capability for having clinical-grade MR image reconstruction in real-time. In recent literature, novel developments have facilitated the utilization of deep networks in various image processing inverse problems. In particular, it has been reported multiple times that the performance of deep networks can be improved by using short connections between layers. In the next step of this thesis, a novel MRI reconstruction method is introduced that utilizes such short connections. The dense connections are used inside densely connected residual blocks. Inside these blocks, the feature maps are concatenated to the subsequent layers. In this way, the extracted information is propagated until the last stage of the block. The efficiency of these densely connected residual blocks was evaluated in MRI reconstruction settings, by augmenting different types of effective deep network models with these blocks in novel structures. The quantitative and qualitative results indicate that this original introduction of the densely connected blocks to the MR image reconstruction problem improves the reconstruction performance significantly. In addition, a novel densely connected residual generative adversarial network (DCR-GAN) is proposed for fast and high-quality reconstruction of MR images. DCR blocks enable the reconstruction network to go deeper by preventing feature loss in the sequential convolutional layers. DCR block concatenates feature maps from multiple steps and gives them as the input to subsequent convolutional layers in a feed-forward manner. In this new model, the DCR block's potential to train relatively deeper structures is utilized to improve quantitative and qualitative reconstruction results in comparison to the other conventional GAN-based models. It can be see from the reconstruction results that the novel DCR-GAN leads to improved reconstruction results without a significant increase in the parameter complexity or run times. The GAN-based structures generally suffer from some limitations. They are slow in convergence and they are unstable during the training step. In this work, these limitations of GAN also was addressed by proposing a new wavelet-based structure. To accomplish this, the wavelet transform packet was incorporated into the GAN structure. The wavelet transform is used in the encoding and decoding steps to create this model. In another word, the downsampling and upsampling layers were replaced with Discrete Wavelet Transform (DWT). DWT is used to replace each pooling process during the contraction phase. As DWT is a reversible package, this downsampling approach guarantees that all information can be retained. DWT can also record both the frequency and position information of feature maps, which will aid in the preservation of fine texture. The inverse wavelet transform is employed in the expansion step to upgrade the size of feature maps. Moreover, recent breakthroughs in this field have inspired us to propose another novel deep unfolding structure for MR image reconstruction. In the last step, the model was trained using not only an iteration of the image itself but also utilizing an updated noise level parameter. The noise level parameter is calculated at each iteration using the error between the network output and the initial zero filling estimate. This new parameter is given as an additional input to the network, and it acts as an evolving regularizer for the image manipulation strength of the network over the unrolling iterations. The introduction of this adaptivity over iterations in the training step also improves the deep models reconstructed image quality in the inference stage. Empirical results indicate that the recommended technique can convergence to better reconstruction results when compared to state-of-the-art unfolding structures devoid of such an adaptive parameter. The introduction of the additional adaptive parameter results in an incremental increase in the parameter complexity, and the required reconstruction times also stand very similar. In this thesis, both quantitative and qualitative results were provided and the proposed model's results were evaluated with cutting-edge techniques in the MR image reconstruction field. Three commonly used evaluation metrics of PSNR, SSIM, and NMSE were used to evaluate simulation results. The statistical differences between developed techniques are investigated using the one-way ANOVA method. Additionally, a t-test is used to specify the major difference between the means of the two proposed structures. Additionally, the robustness of the proposed densely connected residual models was verified by testing them with another dataset type without retraining them. The other dataset differs in size and body tissue type compared to the training dataset. The suggested novel structures in this thesis are improved MR image reconstruction performance compared to state-of-the-art techniques regarding all evaluation metrics. They proved their capacity for reconstructing high-quality images. More importantly, the thesis goal was satisfied regarding the acceleration of MR imaging. The proposed models in this thesis are generally considered to be fast enough to be used even in real-time medical imaging.
İşbirlikçi robotların haptik arayüzlerle teleoperasyonu

(Lisansüstü Eğitim Enstitüsü, 2021-03-03) Argın, Ömer Faruk ; Bayraktaroğlu, Zeki Yağız ; 518142008 ; Mekatronik Mühendisliği ; Mechatronics Engineering

Robotlar günlük hayatımızda ve üretim alanında katlanarak artan bir şekilde kullanılmaktadır. Gelecekte daha da akıllanarak insanlar ile birlikte çalışacak ve diğer robotlarla işbirliği içinde çalışarak akıllı fabrikalar oluşturacaklardır. Robotlar bazı uygulamalarda, insan operatörünün kontrolünde ve uzaktan teleoperasyonla kontrol edilmektedir. Uzakta çalışan robot bilinmeyen ve dinamik bir çevreyle etkileşime girmektedir. Bazı uygulamalarda daha hassas ve kararlı teleoperasyon gerçekleştirebilmek için operatör uzak ortam hakkında görsel geri beslemenin yanında haptik geri beslemeyle beslenmektedir. Bu haptik geribeslemeye sahip teleoperasyon uygulamalarına haptik teleoperasyon sistemleri denir. Bu çalışmada, bir lokal manipülatör ve çok uzak manipülatörle işbirlikçi haptik teleoperasyon için kontrol şemaları önerilmektedir. İşbirilikçi teloperasyon için konum kontrollü, kuvvet/konum kontrollü paylaşımlı kontrollü kontrol şemalar sunulmaktadır. Uzak manipülatörler için önerilen kontrol yapılarının asimptotik kararlılıkları iletişim kanallarında sınırlı zaman gecikmelerinin olduğu durum için Lyapunov analizi ile gösterilmektedir. Önerilen kontrol şemaları endüstriye yönelik basit manipülasyon ve pim yerleştirme uygulamaları ile deneysel olarak gerçekleştirilmektedir. Deneysel uygulamalarda önerilen kontrolcüler, kararlı teleoperasyonlarda konum takip performansları, eğitimli ve eğitimsiz kullanıcılar ile uygulanabilirlik performansı, engelli ortamda manipülasyon ile sağlamlık analizleri ve sistemin şeffaflığı incelenmektedir. Önerilen kontrol şemalarının uygulanması ve deneysel doğrulaması, modüler bir sanal modelleme ortamı yardımıyla elde edilir. Çift yönlü haptik teleoperasyon için önerilen kontrol şeması, birden fazla uzaktan manipülatörle işbirliği görevlerine sanal modelleme ortamı yardımıyla kolayca genişletilebilmektedir. Manipülatörün geometrik parametrelerini içeren kinematik zincirin sanal ortamda tanımlanması ile önerilen iki yönlü ve işbirlikçi kontrolörlerin uygulanması için yeterlidir. Başka bir matematiksel modele ihtiyaç duyulmadığından, geometrik parametreleri mevcut olan herhangi bir rastgele robot kolu haptik teleoperasyon şemasına kolayca entegre edilebilir. Endüstriyel manipülatörlerin teleoperasyon ile kontrolünde, kullanıcı tarafından uygulanan yüksek frekanslı hareket referansları, sistemin kararsız davranışına neden olabilir. Ayrıca manipülatör kontrolünde aktüatör akımları genellikle güvenlik nedenleriyle sınırlandırılır bu da takip problemine neden olmaktadır. Herhangi bir haptik arayüzle kuvvet geri beslemesi olmadığında, insan operatör tarafındaki hareket referansları izin verilen giriş frekanslarını aşabilir. Bu durumlar uzak ortamda istenmeyen hareketlere ve hasar vb. neden olur ve ayrıca deneyimsiz kullanıcıların eğitim sürelerini uzatır. Bu çalışmada önerilen sanal haptik etkileşim kuvveti kullanıcıya teleoperasyon sırasında görünür bir atalet hissi sağlar. Yapay olarak oluşturulan bu ikinci dereceden dinamikler, kullanıcıdan gelen yüksek frekanslı hareket referanslarını filtreler ve bu nedenle deneyimsiz kullanıcıların kolayca teleoperasyon gerçekleştirmesine olanak tanır. Önerilen haptik etkileşim sistemi, manipüle edilen yük ile birlikte çalışan manipülatörler arasında sanal yay sönümleyicilerin kullanılmasına dayanmaktadır. Bu çalışmada sunulan deneysel sonuçlar, önerilen haptik etkileşim sisteminin tahmin edilen katkılarını doğrulamaktadır.
Modeling of dynamic systems and nonlinear system identification

(Graduate School, 2023-03-24) Abedinifar, Masoud ; Ertuğrul, Şeniz ; 518162013 ; Mechatronics Engineering

One of the primary goals of science is to identify and describe the structures and physical laws of nature. When the data corresponding to the input and output of a physical system is available, but the underlying rules and the structure of the system are unknown, it is essential to employ various approaches to determine these rules and structures. Determination of the underlying rules and structure of a system, particularly in some operation regions, is a difficult task because of the existence of some nonlinearities in the structure of the model. Therefore, choosing a reliable approach to identify the structure of the model in the different working regions of the system is crucial. For this purpose, system identification has been established as a critical technique for assisting in the modeling of complex engineering systems. System identification includes all processes of establishing a mathematical model of the systems by measured input-output datasets. The developed mathematical models using system identification methods are commonly used for monitoring, controller design, fault detection, system response prediction, optimization, and other purposes. The procedure of system identification could be classified into three steps: First, the structure of the mathematical model has to be determined. The structure of the mathematical model could be represented with linear or nonlinear models. Second, the unknown coefficients of the mathematical model should be determined by simulation or experimental input-output datasets. Finally, the model with the identified parameters has to be validated with the new input-output datasets. The major aims of this research could be listed as: In the first step, it is planned to develop transparent nonlinear mathematical models of the mechanical systems in a way that each term of the model could be physically interpreted. These models are called "white-box" models, which are developed using physical rules like Kirchhoff's and Newton's rules. Second, the thesis aims to properly determine the nonlinear models of the physical systems utilizing an appropriate system identification methodology. Third, it aims to investigate the existence of the identified physical phenomena, like nonlinear frictional terms, and dead-zone using different statistical methods. To fulfill these purposes, the following steps are performed: First, the general mathematical models of some physical systems are developed. The mathematical models of the physical systems include linear and various nonlinear equations. The linear equations of the model are developed utilizing some physical rules like Kirchhoff's and Newton's rules, etc. For the nonlinear part of the models, the nonlinear equations of some physical phenomena, like nonlinear friction equations and dead-zone, along with time-delay, are compiled and added to the general mathematical model of the physical systems. Then, the appropriate input signals are generated to stimulate all the dynamics of the physical systems in their different working regions. This is performed to capture the effect of all the possible existing nonlinearities in the system's output. In the next step, the output of the mathematical models is collected, and input-output data sets are established. Then, the Particle Swarm Algorithm (PSO) algorithm is coded to determine the unknown parameters of the general mathematical model of the system using input-output datasets. The PSO algorithm's results are evaluated by utilizing the conventional Nonlinear Least Squared Errors (NLSE) estimation method. Afterward, various statistical tests, including the confidence interval test and the null hypothesis test, are executed to investigate the identification results' validity. Finally, using some model evaluation criteria such as Mean Squared Errors (MSE) and coefficient of determination (R2), the capability of the determined models in computing the output of the real systems is evaluated. The framework suggested in this thesis is implemented for four case studies as benchmark problems, ranging from simple to complex in two steps. Initially, two case studies, namely a Direct Current (DC) motor, and a solenoid actuator are chosen, and their mathematical models with various combinations of nonlinearities are constructed in the first stage. The simulation data for both the DC motor and solenoid actuator models are established by utilizing the nonlinear models. First, all kinds of friction nonlinearities are incorporated into the real mathematical models of these components, followed by adding some likely friction nonlinearities to check the effectiveness of the identification algorithms. After that, the identification and validation frameworks are utilized to ascertain the model parameters and verify the credibility of the outcomes. Furthermore, a PSO algorithm with multiple cost functions is used to optimize the design parameters of a solenoid actuator to improve its performance. The second stage involves obtaining actual experimental data from real mechanical systems, which is then utilized to examine the framework developed in the simulation studies. The initial benchmark problem involves collecting real data from the experimental apparatus of the ball and beam mechanism by providing appropriate input signals. Moreover, the identification algorithm's effectiveness is tested for various experimental conditions for the mechanism of the ball and beam. In the second benchmark problem, real data is acquired from a 6-degree-of-freedom (DOF) UR5 robotic manipulator by providing appropriate trajectories. Then, the model parameters are determined, and the reliability of the outcomes is examined using the identification and validation frameworks.
Sahte GPS sinyallerine karşı gömülü sistem tasarımı ve mekatronik sistemlerde uygulanması

(Lisansüstü Eğitim Enstitüsü, 2024-04-01) Tanış, Mustafa ; Yalçın, Müştak Erhan ; 518162008 ; Mekatronik Mühendisli˘gi

Navigasyonun yalnızca bir dokunuş uzaklıkta olduğu, neredeyse tüm akıllı donanımların kullandığı, hiper bağlantılı dünyamızda, Küresel Konumlandırma Sistemi (GPS) çok büyük önem taşımaktadır. Seyahatlerimize rehberlik etmekten, insansız ve otonom hava araçlarının teslimatlarını gerçekleştirmesine kadar, modern yaşamın vazgeçilmez bir parçası haline gelmiştir. Ancak doğruluğunun kusursuz olduğuna inanılan bu teknoloji büyük bir tehdit içermektedir. Bu tehdidin adı, GPS sahtekarlığıdır. GPS sinyallerinin manipülasyonu ile yapılan aldatmalar, güvenilir sandığımız veri ile yıkıcı sonuçlara yol açabilecek saldırılara dönüştürebilir. GPS sahtekarlığının temeli, uydu sinyallerini taklit etmek ve alıcıları yanlış konumları bildirmeleri için kandırmaktır. Örneğin, otoyolda kullanılan otonom veya yarı otonom araç, GPS alıcısı ile güvenle hedefe yönlendirdiğini düşünülür. Fakat aniden, yoldan keskin bir dönüş yaparak ıssız bir alana yönelir. Bu hayali senaryo, sahtekarlığın yıkıcı gücünün gözümüzde canlanmasını sağlıyor. Fakat bu tip saldırıların dünyamızdaki etkileri hayal edilenin ötesine geçebilir. Kötü niyetli gruplar veya rekabet eden ülkelerin elinde bu saldırının sonuçları çok ağır olabilir. Kötü niyetli gruplar veya kişiler, kaos ve aksaklık yaratmak için ürettikleri sahte sinyalleri kullanarak kritik altyapılara, elektrik şebekelerine, hava trafik kontrol sistemlerine, otonom araçlara ve daha birçok alana yönelik koordineli saldırılar gerçekleştirebilirler. Savaşta, aldatma saldırısı iletişim ağlarını sekteye uğratabilir, birlikleri yanlış yönlendirebilir, askeri operasyonları aksatabilir ve potansiyel olarak çatışmanın ölçeğini ve yönünü değiştirebilir. GPS sahteciliğinin insan psikolojisi üzerinde bile endişe verici sonuçları olabilir. GPS gibi temel bir teknolojiye olan güvenin erozyona uğraması geniş kapsamlı sonuçlara da yol açar. Afet veya kriz sırasında doğru konum verilerine dayanan acil durum hizmetlerinin ihtiyacı olan kişilere ulaşamaması güvenin zedelenmesi anlamına gelir. Otonom araçların aynı konuma koordineli saldırılarla yönlendirilip, acil durum ulaşımını engellemesi kaotik bir durum oluşturabilir. GPS sahtekarlığına çözüm bulmanın önemi, artan kolay ve ucuz erişilebilir donanımlar nedeniyle daha da artmaktadır. Bir zamanlar yüksek teknolojili laboratuvarlarla sınırlı kalan sahte yayın ekipmanları giderek daha uygun fiyatlı ve kullanımı kolay hale gelmektedir. Aldatmanın bu şekilde erişilebilir olması, her türlü art niyetli kişi ve grupların hizmetinde olmasına yol açmaktadır. Bu tehlikenin merkezinde, çok yönlü bir kullanım sunan yazılım tabanlı radyo (SDR) donanımları vardır. SDR'ler, farklı frekanslar ve protokoller arasında geçiş yaparak çalışma parametrelerini dinamik olarak değiştirebilen, yazılımla çalışan radyolardır. Araştırma ve sinyal analizi gibi iyi amaçlara yönelik olan bu esneklik, ne yazık ki kötü niyetli aktörlerin de dikkatini çekmektedir. Art niyetli aktörlerin ellerinde SDR, sahte GPS saldırıları hazırlamak için güçlü bir araç haline dönüşmektedir. SDR'ler, gerçek GPS sinyallerini kaydedebilir ve bunları farklı konumlarda yeniden oynatarak alıcıları etkili bir şekilde istenmeyen konumlara yönlendirebilir. Bu teknik, basit olmasına rağmen, insansız hava araçları veya deniz araçları gibi savunmasız sistemlerin navigasyonunu bozabilmektedir. SDR'ler, GPS sinyallerine gömülü verileri değiştirerek uydu konumları veya hızları hakkında yanlış bilgiler sağlayabilmektedir. Bu daha karmaşık yaklaşım, alıcıları tamamen farklı bir yerde olduklarına inandırarak yanıltabilir. GPS sinyallerine hassas zamanlama hataları eklemek bir diğer yanıltma tekniğidir. SDR'ler bunu, belirli uyduların zamanlama imzalarını taklit ederek başarabilir ve bu da alıcıların konumlarını yanlış hesaplamasına neden olabilirler. Her geçen gün SDR'lerin maliyeti ve karmaşıklığı önemli ölçüde azalmakta, bu da onları tasarımcılardan suç örgütlerine kadar daha geniş bir yelpazedeki kişilere kullanım kolaylığı sağlamaktadır. Kompakt ve çok yönlü olan SDR'ler sahada kolayca konuşlandırılabilmekte ve hem sabit hem de mobil ortamlarda sahte GPS saldırılarına olanak tanımaktadır. SDR'lerin erişilebilir ve yaygın olması nedeniyle, bu çalışmada öncelikle deneysel olarak saldırının gerçekleştirilmesi; saldırı adımları, saldırı adımları sırasında ihtiyaç duyulan donanımlar, yazılımların özellikleri ve kullanımı, son olarak da gerçek bir alıcı ile farklı saldırı senaryolarının gerçekleştirilmesi gösterilmiştir. Sahte konum saldırıları karmaşık özelliklere sahip özel donanımlar da gerçekleştirilebilir fakat SDR'ler bu saldırı aşamalarını kolaylaştırarak uygun ve pratik saldırıları gerçekleştirmektedir. SDR'lerin giderek artan önemine karşı, savunma önlemlerine ihtiyaç duyulmaktadır. Sinyal kimliği doğrulama teknikleri ile kriptografik doğrulama yöntemlerinin uygulanması, meşru GPS sinyallerini sahte sinyallerden ayırt edebilir ve SDR tabanlı saldırıların etkinliğini azaltabilir. Standartlaştırılmış test prosedürü ile ortak test ortamları ve değerlendirme metodolojileri oluşturmak, saldırılara karşı koymak için tasarlanan sahtecilik tespit tekniklerini karşılaştırmak ve geliştirmek için çok önemlidir. Sahte GPS saldırılarında SDR ve benzeri donanımların iki farklı rolü vardır. Bir yönüyle bu saldırın gerçekleştirilmesine olanak sağlarken, diğer yönüyle de esnekliği ve programlanabilirliği, bu saldırın tespiti konusunda büyük kolaylıklar sağlamaktadır. Bu nedenle saldırı ve tespit mekanizmasında bu donanımlardan faydalanılmaktadır. Bu tez çalışmasında da belirtilen özelliklerden yararlanılarak saldırı amacıyla kullanıldığı gibi saldırı tespiti amacıyla da kullanılmıştır. Tez çalışmasında deneysel olarak farklı saldırı senaryolarının alıcı tarafında başarılı olmasının ardından, gerçeklenen saldırıların tespit edilmesi üzerine çalışılmıştır. Tasarlanan devre ile sahte GPS sinyalinin güç spektral yoğunluğu kullanarak, herhangi bir kod demodülasyonuna gerek duymadan saldırının tespit edilmesi üzerine istatiksel karar mekanizması oluşturulmuştur. Devrede bulunan RF(Radyo frekansı) ön katmanı sayesinde sinyal ara frekans katmanına indirilerek örneklenmiş , güç spektral yoğunluğu hesaplanan farklı boyutlarda pencereleme işlemi ile RMS(kök kare ortalama) değeri ile karşılaştırılarak bir veri kümesi oluşturulmuştur. Oluşturulan veri kümesinin rastgeleliği kontrol edilerek saldırı tespiti yapılmıştır. Sahte GPS saldırısında orijinale göre bu rastgelelik kaybolmaktadır. Bu nedenle karar mekanizması olarak istatiksel bir test olan Runs test kullanılmıştır. Veri kümesinin test edilmesi ile p değeri, belirlenen alfa seviyesinin altında veya üstünde olmasına göre anlam kazanmaktadır. Alfa seviyesinin altındaki p değeri, kullanılan veri bloğunun rastgele olmadığını ifade etmektedir. Yönteme ilişkin detaylı analiz sonrasında test ortamı oluşturulmuştur. Sahte GPS saldırısı SDR yardımıyla gerçekleştirilmiş ve belirlenen pencere boyutlarında ortaya çıkan veri seti değerlendirilmiştir. Değerlendirme sonuçları ve zaman analizi paylaşılmıştır. Sonuç olarak, GPS verisinin manipülasyonuna yönelik yazılım ve donanım içeren test ortamı kurularak alıcının saldırı altında farklı senaryolarda aldatılması, aldatma saldırısı sonrası kurulan tespit test ortamı ve önerilen yöntem ile saldırının tespitinin yazılım ve donanım kullanarak gerçeklenmesi, benzeri saldırılara karşı uygulanabilir istatiksel karar mekanizması oluşturularak sonuçları ortaya konmuştur. Kurulan sistemin esnekliği ve üzerine farklı donanımların eklenebilmesi ile farklı uydu sistemlerine, GPS verisi kullanan sektörlere yönelik ileriki çalışmaların önünü açan bir devre tasarlanmıştır.
Social behavior learning for an assistive companion robot

(Graduate School, 2023-01-26) Uluer, Pınar ; Köse, Hatice ; 518132005 ; Mechatronics Engineering

Designing robots having the ability to build and maintain interpersonal relationship with humans by exchanging social and emotional cues has attracted much attention recently because robots are vastly in use in a wide variety of places with a diverse range of tasks from healthcare to edutainment industry. In order to interact with humans in a natural way, emotion recognition and expressive behavior generation are crucial for these robots to act in a socially aware manner. This requires the ability to recognize the affective states, actions and intentions of their human companions. The robot should be able to interpret the social cues and its human companion's behavioral patterns to be able to generate assistive social behaviors in return. There are several popularly known robotic platforms such as Kismet, Nao and Pepper which are able to recognize its human partner's emotional state based on facial expressions, vocal cues and body postures and express simple human emotions. However these robots do not have an emotional character or an affective state, they are just capable to mirror the human's expressions independently from the social context of the interaction. For the robots to be accepted as social entities, they should be endowed with the capability to interpret the human's mood as well as his/her emotions and the social context of the interaction. In order to achieve this purpose, it is not enough to treat expressive behaviors of humans as only a mirror of their internal state. Therefore, it is crucial to incorporate a generative account of expressive behavior into an emotional architecture. This requires perceiving and understanding others' affective states and behaving accordingly, it corresponds to the most generic definition of empathy. Empathy is a focal point in the study of interpersonal relationships therefore it should also be considered as one of the major elements in the social interaction between the robots and humans. Humans have the ability to feel empathy not only for other humans but also for fictional characters and robots. But the robots are not able yet to display any empathic behavior in return. The motivation of this thesis study is to design and implement a computational emotion and behavior model for a companion robot. The proposed model is designed to process multi-channel stimuli in order to interpret the affective state of its human companion and to generate in-situ affective social behaviors based on the processed information coming from the human companion and the environment, that is the social context of the interaction. This dissertation attempts to explore the following research objectives: - Would the robot be able to display basic emotions? Could the human companion identify correctly the emotions displayed by the robot? - How could a social robot infer and interpret its companion's emotional states? - How can we computationally model an artificial emotional mechanism and implement it on a social robot to provide a natural social interaction? - Which learning techniques should we use for the affective robot assistant to learn which emotional behavior to express during the interaction? - Could a social robot designed with emotional understanding and expression foster the interaction gain in a HRI scenario based on assistance? In order to explore the answers of these research questions, user studies with children having different developmental profiles and two affective robots, Pepper and Kaspar, were conducted in coordination with two research projects. The first project, titled as RoboRehab, we aimed to use an affective social robot to support the auditory tests of children with hearing disabilities in clinical settings. During their hospital visits, children take several tests to determine the level of their hearing and to adjust their hearing aid or cochlear implant, if necessary. The audiologists mention that the children usually get stressed and tend to be in a negative mood, when they are in the hospital during their consultation. This affects children's' performance in the auditory perception tests negatively, and their cooperation decrease significantly. In Roborehab, we used machine learning-based emotion recognition approaches to detect the children's emotional state and adjust the robot's actions accordingly. We designed a feedback mechanism to reduce the stress of children and help the children to improve their mood during the audiometry tests. We used a socially assistive humanoid robot Pepper, enhanced with emotion recognition, and a tablet interface, to support children in these tests. We investigated the quantitative and qualitative effects of the different test setups involving a robot, a tablet app and the conventional method. We employed traditional machine learning techniques and deep learning approaches to analyze and classify the physiological data (blood volume pulse, skin temperature, and skin conductance) of children collected by E4 smart wristband. The second project, entitled as "Affective loop in Socially Assistive Robotics as an intervention tool for children with autism (EMBOA)", was a research project with the aim of combining affective computing technologies with the social robot intervention in children with autism spectrum disorder (ASD). Children with ASD are known to display limited social and emotional skills in their routine interactions. Inspired by the promising results presented in the social robotics field, we aimed to investigate affective technologies with a social robot, Kaspar, to assist children with ASD in the development of their social and emotional skills, help them to overcome social obstacles and make the children more involved in their interactions. Interaction studies with Kaspar were conducted in 4 collaborating country; Poland, North Macedonia, United Kingdom and Turkey; with more than 65 children with ASD and interaction data collection by different sensor modalities (visual, audio, physiological and interaction-specific data) was performed within a longitudinal study design. In this dissertation, a computational model for emotional understanding and emotional behavior generation and modulation was designed and implemented for a companion robot based on the collected data and findings through The RoboRehab and EMBOA projects. The presented models were designed: (1) to process multi-channel stimuli, i.e. vision-based facial landmarks, physiological data-based signals, in order to detect the affective states of the human companion; (2) to generate in-situ affective social behaviors for the robot based on the interaction context; (3) to adapt the intensity of the robot's emotional expressions based on the preferences of the human companion. Despite challenging situations, with Covid-19 outbreak at the top of the list, and computational limitations, the previously mentioned research objectives were investigated in detail and the results were presented in this dissertation. We were able to answer the five research questions on the affective social behavior of a companion robot. The user studies conducted with hearing-impaired children in RoboRehab showed that Pepper robot was able to display emotional behaviors and the children could correctly identify and interpret them. Moreover, the RoboRehab findings revealed that the affective robot was able to trigger some emotional changes in children and cause difference in their physiological signals. These signals were used to distinguish the emotions of children with machine learning and deep learning approaches in different setups. On the other hand, the results from the EMBOA studies with children with ASD showed that a multimodal approach based on behavioral metrics, i.e. facial action units, physiological signals, interaction-specific metrics, can be used to understand the emotional state and infer the emotional model of the human companion. Different frameworks were presented in order to model an artificial emotional mechanism for a social robot. The first one was a simple linear affect model based on the two-dimensional valence and arousal representation. The second one, a vision-based model where the robot detects the affective state of its companion and develops a behavioral strategy to adapt its emotional behaviors and improve the negative mood of its companion. And finally, a behavioral model were presented for the robot to predict the preferences of its companion and regulate the intensity of its emotional behaviors accordingly. The results of the user studies and the findings revealed that an emotional model can be computationally modeled and implemented for a social robot to adapt its emotional behaviors to the preferences and needs of its companion, consequently, to make the interaction between the human companion-robot pair more natural. The RoboRehab and EMBOA user studies with different groups of children (with typical development, with hearing impairment, with autism) showed that independently from their profile the children were more involved and paid attention to the social robot. Even though the objective evaluation metrics did not point a significant difference, the subjective evaluation of the audiologists, therapists, pedagogues complied with the presented hypothesis. Furthermore, the self-report surveys with children and their parents showed that the children accepted the affective robot as an intelligent and funny social being. These results demonstrate that an affective social robot with personalized emotional behaviors based on the preferences of its companion can foster the interaction gain in an assistive human-robot interaction scenario.

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