Browsing by Author "Oguz-Ekim, Pinar"

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Citation - Scopus: 1
The Ekf Based Localization and Initialization Algorithms With Uwb and Odometry for Indoor Applications and Ros Ecosystem
(Institute of Electrical and Electronics Engineers Inc., 2020) Oguz-Ekim P.; Bostanci B.; Tekkok S.C.; Soyunmez E.; Bostanci, Bekir; Soyunmez, Emre; Oguz-Ekim, Pinar; Tekkok, Sercan C.
This paper will cover some extension modules over the Turtlebot3 libraries using ultra-wideband (UWB) sensors and propose a solution to the initialization problem along with the localization problem. The Turtlebot3 already has an algorithm named move base for autonomous drive, which uses Light Detection and Ranging (LiDAR) and odometry to localize itself and avoid obstacles. However, it suffers from autonomous initialization. Therefore, ranging data from UWB sensors are used to take the initial pose of the robot to eliminate the initialization problem and advance the move base algorithm to be more robust. This data is also used in the Extended Kalman Filter (EKF) along with odometry to localize the robot. To enable wide-spread adoption, we provide an open source implementation of our algorithms and modules for the robot operating system (ROS) for real environment. Furthermore, we create an open source simulation environment for applications, which use UWB, LiDAR, and odometry data. © 2020 IEEE.
Citation - WoS: 9
Citation - Scopus: 16
The Lidar and Uwb Based Source Localization and Initialization Algorithms for Autonomous Robotic Systems
(Institute of Electrical and Electronics Engineers Inc., 2019) Bostanci B.; Tekkok S.C.; Soyunmez E.; Oguz-Ekim P.; Yeganli F.; Bostanci, Bekir; Yeganli, Faezeh; Soyunmez, Emre; Oguz-Ekim, Pinar; Tekkok, Sercan
This paper covers the source localization algorithm based on the least squares techniques and the squared range measurements obtained from ultra-wide band (UWB) sensors to locate the robot in an indoor environment. Additionally, the initialization algorithm which is based on light detection and Ranging (LiDAR) scans is proposed. It takes the advantage of the estimated location to find the initial orientation of the robot with respect to the previously obtained map. Thus, the crucial problem of the autonomous initialization and localization in robotics is solved. To enable wide-spread adoption, we provide an open source implementation of our algorithms and the modules for the robot operating system (ROS) for real environment. Furthermore, an open source simulation environment is created for applications which employ UWB/LiDAR data. © 2019 Chamber of Turkish Electrical Engineers.
Citation - WoS: 24
Citation - Scopus: 32
Machine Learning Approaches for Municipal Solid Waste Generation Forecasting
(Mary Ann Liebert, Inc, 2021) Oğuz Ekim, Pınar; Oguz-Ekim, Pinar
Municipal solid waste (MSW) generation forecasting can be considered as the biggest challenge of integrated solid waste management systems, particularly for developing countries where data collection is limited. In this study, three different machine learning algorithms, namely backpropagation neural network (BPNN), support vector regression (SVR), and general regression neural network, were applied for different countries. Comparative evaluation of these different algorithms based on gross domestic product, domestic material consumption, and resource productivity were given through the optimum solution. Moreover, the algorithms were tested for the case of Turkey. The results of this study are expected to represent a general outline for stakeholders of Turkey for improving MSW management strategies all over the country, and these results can be extended to similar developing countries across the world. It can be concluded that BPNN and SVR methods can be applied successfully for the case of Turkey and other countries across the world to predict the MSW generation, whereas BPNN is slightly better. If the input and output variables are identified well, machine learning approaches can give a good projection for waste generation, and this projection can be utilized for different countries. Furthermore, the developing countries with missing data can develop more realistic strategies for MSW management by not relying solely on international databases such as Eurostat to forecast MSW generation.