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    Comparison of push-recovery control methods for Robotics-OP2 using ankle strategy
    (Gazi Univ, Fac Engineering Architecture, 2024) Aslan, Emrah; Arserim, Muhammet Ali; Ucar, Aysegul
    The main purpose of this study is to develop push-recovery controllers for bipedal humanoid robots. In bipedal humanoid robots, occur balance problems against external pushes. In this article, control methods that will be the solution to the balance problems in humanoid robots are proposed. We aim to ensure that bipedal robots that behave like humans can come to a position of balance against external pushes. When people encounter balance problems as a result of outside pushes, they respond quite successfully. This ability is limited in bipedal humanoid robots. The main reason for this is the complex structures and limited capacities of humanoid robots. In the real world, there are push-recovery strategies created by considering the reactions of people in case of balance disorder. These strategies; are ankle, hip, and step strategies. In this study, the ankle strategy, from the push-recovery strategies, was used. Different control methods have been tried with the ankle strategy. Three different techniques of control were utilized in the applications. These methods are as follows; Classical control method is PD, Model Predictive Control (MPC) based on prediction, and Deep Q Network (DQN) as deep reinforcement learning algorithm. The applications were carried out on the Robotis-OP2 robot. Simulation tests were done in 3D in the Webots simulator. The humanoid robot was tested with three methods and the results were compared. It has been determined that the Deep Q Network algorithm gives the best results among these methods.
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    Öğe
    Object Detection on FPGAs and GPUs by Using Accelerated Deep Learning
    (Ieee, 2019) Cambay, V. Yusuf; Ucar, Aysegul; Arserim, M. Ali
    Object detection and recognition is one of the main tasks in many areas such as autonomous unmanned ground vehicles, robotic and medical image processing. Recently, deep learning has been used by many researchers in these areas when the data measure is large. In particular, one of the most up-to-date structures of deep learning, Convolutional Neural Networks (CNNs) has achieved great success in this field. Real-time works related to CNNs are carried out by using GPU-Graphics Processing Units. Although GPUs provides high stability, they requires high power, energy consumption, and large computational load problems. In order to overcome this problem, it has started to used the Field Programmable Gate Arrays (FPGAs). In this article, object detection and recognition procedures were performed using the ZYNQ XC7Z020 development board including both the ARM processor and the FPGA. Real-time object recognition has been made with the Movidius USB-GPU externally plugged into the FPGA. The results are given with figures.