Researcher:
Aydın, Yusuf

Profile Picture
ORCID

Job Title

PhD Student

First Name

Yusuf

Last Name

Aydın

Name

Name Variants

Aydın, Yusuf

Email Address

Birth Date

Filters

2014 - 20209

Advanced Search

Filter by

Settings

Researcher Of Publications: search.filters.isAuthorOfPublication.da561662-784b-4262-b2a2-4792b7c30a18

Search Results

Now showing 1 - 9 of 9
  • Placeholder
    PublicationMetadata only
    A variable-fractional order admittance controller for pHRI
    (IEEE Inc., 2020) Patoglu, Volkan; Tokatli, Ozan; Department of Mechanical Engineering; N/A; N/A; N/A; Başdoğan, Çağatay; Aydın, Yusuf; Şirintuna, Doğanay; Çaldıran, Ozan; Faculty Member; PhD Student; PhD Student; PhD Student; Department of Mechanical Engineering; College of Engineering; Graduate School of Sciences and Engineering; Graduate School of Sciences and Engineering; Graduate School of Sciences and Engineering; 125489; 328776; N/A; N/A
    In today's automation driven manufacturing environments, emerging technologies like cobots (collaborative robots) and augmented reality interfaces can help integrating humans into the production workflow to benefit from their adaptability and cognitive skills. In such settings, humans are expected to work with robots side by side and physically interact with them. However, the trade-off between stability and transparency is a core challenge in the presence of physical human robot interaction (pHRI). While stability is of utmost importance for safety, transparency is required for fully exploiting the precision and ability of robots in handling labor intensive tasks. In this work, we propose a new variable admittance controller based on fractional order control to handle this trade-off more effectively. We compared the performance of fractional order variable admittance controller with a classical admittance controller with fixed parameters as a baseline and an integer order variable admittance controller during a realistic drilling task. Our comparisons indicate that the proposed controller led to a more transparent interaction compared to the other controllers without sacrificing the stability. We also demonstrate a use case for an augmented reality (AR) headset which can augment human sensory capabilities for reaching a certain drilling depth otherwise not possible without changing the role of the robot as the decision maker. © 2020 IEEE.
  • Placeholder
    PublicationMetadata only
    A new control architecture for physical human-robot interaction based on haptic communication
    (Ieee, 2014) N/A; N/A; Department of Mechanical Engineering; Aydın, Yusuf; Arghavani, Nasser; Başdoğan, Çağatay; PhD Student; PhD Student; Faculty Member; Department of Mechanical Engineering; Graduate School of Sciences and Engineering; Graduate School of Sciences and Engineering; College of Engineering; 328776; N/A; 125489
    In the near future, humans and robots are expected to perform collaborative tasks involving physical interaction in various different environments such as homes, hospitals, and factories. One important research topic in physical Human-Robot Interaction (pHRI) is to develop tacit and natural haptic communication between the partners. Although there are already several studies in the area of Human-Robot Interaction, the number of studies investigating the physical interaction between the partners and in particular the haptic communication are limited and the interaction in such systems is still artificial when compared to natural human-human collaboration. Although the tasks involving physical interaction such as the table transportation can be planned and executed naturally and intuitively by two humans, there are unfortunately no robots in the market that can collaborate and perform the same tasks with us. In this study, we propose a new controller for the robotic partner that is designed to a) detect the intentions of the human partner through haptic channel using a fuzzy controller b) adjust its contribution to the task via a variable impedance controller and c) resolve the conflicts during the task execution by controlling the internal forces. The results of the simulations performed in Simulink/Matlab show that the proposed controller is superior to the stand-alone standard/variable impedance controllers.
  • Placeholder
    PublicationMetadata only
    Stable physical human-robot interaction using fractional order admittance control
    (IEEE Computer Soc, 2018) Tokatlı, Ozan; Patoğlu, Volkan; N/A; Department of Mechanical Engineering; Aydın, Yusuf; Başdoğan, Çağatay; PhD Student; Faculty Member; Department of Mechanical Engineering; Graduate School of Sciences and Engineering; College of Engineering; 328776; 125489
    In the near future, humans and robots are expected to perform collaborative tasks involving physical interaction in various environments, such as homes, hospitals, and factories. Robots are good at precision, strength, and repetition, while humans are better at cognitive tasks. The concept, known as physical human-robot interaction (pHRI), takes advantage of these abilities and is highly beneficial by bringing speed, flexibility, and ergonomics to the execution of complex tasks. Current research in pHRI focuses on designing controllers and developing new methods which offer a better tradeoff between robust stability and high interaction performance. In this paper, we propose a new controller, fractional order admittance controller, for pHRI systems. The stability and transparency analyses of the new control system are performed computationally with human-in-the-loop. Impedance matching is proposed to map fractional order control parameters to integer order ones, and then the stability robustness of the system is studied analytically. Furthermore, the interaction performance is investigated experimentally through two human subject studies involving continuous contact with linear and nonlinear viscoelastic environments. The results indicate that the fractional order admittance controller can be made more robust and transparent than the integer order admittance controller and the use of fractional order term can reduce the human effort during tasks involving contact interactions with environment.
  • Placeholder
    PublicationMetadata only
    Detecting human motion intention during phri using artificial neural networks trained by EMG signals
    (Ieee, 2020) N/A; N/A; N/A; N/A; Department of Mechanical Engineering; Şirintuna, Doğanay; Özdamar, İdil; Aydın, Yusuf; Başdoğan, Çağatay; PhD Student; Master Student; PhD Student; Faculty Member; Department of Mechanical Engineering; Graduate School of Sciences and Engineering; Graduate School of Sciences and Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; N/A; 328776; 125489
    With the recent advances in cobot (collaborative robot) technology, we can now work with a robot side by side in manufacturing environments. The collaboration between human and cobot can be enhanced by detecting the intentions of human to make the production more flexible and effective in future factories. In this regard, interpreting human intention and then adjusting the controller of cobot accordingly to assist human is a core challenge in physical human-robot interaction (pHRI). In this study, we propose a classifier based on Artificial Neural Networks (ANN) that predicts intended direction of human movement by utilizing electromyography (EMG) signals acquired from human arm muscles. We employ this classifier in an admittance control architecture to constrain human arm motion to the intended direction and prevent undesired movements along other directions. The proposed classifier and the control architecture have been validated through a path following task by utilizing a KUKA LBR iiwa 7 R800 cobot. The results of our experimental study with 6 participants show that the proposed architecture provides an effective assistance to human during the execution of task and reduces undesired motion errors, while not sacrificing from the task completion time.
  • Placeholder
    PublicationMetadata only
    Fractional order admittance control for physical human-robot interaction
    (Ieee, 2017) Tokatli, Ozan; Patoglu, Volkan; N/A; Department of Mechanical Engineering; Aydın, Yusuf; Başdoğan, Çağatay; PhD Student; Faculty Member; Department of Mechanical Engineering; Graduate School of Sciences and Engineering; College of Engineering; 328776; 125489
    In physical human-robot interaction (pHRI), the cognitive skill of a human is combined with the accuracy, repeatability and strength of a robot. While the promises and potential outcomes of pHRI are glamorous, the control of such coupled systems is challenging in many aspects. In this paper, we propose a new controller, fractional order admittance controller, for pHRI systems. The stability analysis of the new control system with human in-the-loop is performed and the interaction performance is investigated experimentally with 10 subjects during a task imitating a contact with a stiff environment. The results show that the fractional order controller is more robust than the standard admittance controller and helps to reduce the human effort in task execution.
  • Thumbnail Image
    PublicationOpen Access
    Fractional order admittance control for physical human-robot interaction
    (Institute of Electrical and Electronics Engineers (IEEE), 2017) Tokatli, O.; Patoglu, V.; Department of Mechanical Engineering; Aydın, Yusuf; Başdoğan, Çağatay; Faculty Member; Department of Mechanical Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; 125489
    In physical human-robot interaction (pHRI), the cognitive skill of a human is combined with the accuracy, repeatability and strength of a robot. While the promises and potential outcomes of pHRI are glamorous, the control of such coupled systems is challenging in many aspects. In this paper, we propose a new controller, fractional order admittance controller, for pHRI systems. The stability analysis of the new control system with human in-the-loop is performed and the interaction performance is investigated experimentally with 10 subjects during a task imitating a contact with a stiff environment. The results show that the fractional order controller is more robust than the standard admittance controller and helps to reduce the human effort in task execution.
  • Thumbnail Image
    PublicationOpen Access
    Towards collaborative drilling with a cobot using admittance controller
    (Sage, 2020) Department of Mechanical Engineering; Aydın, Yusuf; Şirintuna, Doğanay; Başdoğan, Çağatay; Faculty Member; Department of Mechanical Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; N/A; 125489
    In the near future, collaborative robots (cobots) are expected to play a vital role in the manufacturing and automation sectors. It is predicted that workers will work side by side in collaboration with cobots to surpass fully automated factories. In this regard, physical human-robot interaction (pHRI) aims to develop natural communication between the partners to bring speed, flexibility, and ergonomics to the execution of complex manufacturing tasks. One challenge in pHRI is to design an optimal interaction controller to balance the limitations introduced by the contradicting nature of transparency and stability requirements. In this paper, a general methodology to design an admittance controller for a pHRI system is developed by considering the stability and transparency objectives. In our approach, collaborative robot constrains the movement of human operator to help with a pHRI task while an augmented reality (AR) interface informs the operator about its phases. To this end, dynamical characterization of the collaborative robot (LBR IIWA 7 R800, KUKA Inc.) is presented first. Then, the stability and transparency analyses for our pHRI task involving collaborative drilling with this robot are reported. A range of allowable parameters for the admittance controller is determined by superimposing the stability and transparency graphs. Finally, three different sets of parameters are selected from the allowable range and the effect of admittance controllers utilizing these parameter sets on the task performance is investigated.
  • Thumbnail Image
    PublicationOpen Access
    Detecting human motion intention during pHRI using artificial neural networks trained by EMG signals
    (Institute of Electrical and Electronics Engineers (IEEE), 2020) N/A; Department of Mechanical Engineering; Şirintuna, Doğanay; Özdamar, İdil; Aydın, Yusuf; Başdoğan, Çağatay; Faculty Member; Department of Mechanical Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; N/A; N/A; 125489
    With the recent advances in cobot (collaborative robot) technology, we can now work with a robot side by side in manufacturing environments. The collaboration between human and cobot can be enhanced by detecting the intentions of human to make the production more flexible and effective in future factories. In this regard, interpreting human intention and then adjusting the controller of cobot accordingly to assist human is a core challenge in physical human-robot interaction (pHRI). In this study, we propose a classifier based on Artificial Neural Networks (ANN) that predicts intended direction of human movement by utilizing electromyography (EMG) signals acquired from human arm muscles. We employ this classifier in an admittance control architecture to constrain human arm motion to the intended direction and prevent undesired movements along other directions. The proposed classifier and the control architecture have been validated through a path following task by utilizing a KUKA LBR iiwa 7 R800 cobot. The results of our experimental study with 6 participants show that the proposed architecture provides an effective assistance to human during the execution of task and reduces undesired motion errors, while not sacrificing from the task completion time.
  • Thumbnail Image
    PublicationOpen Access
    A computational multicriteria optimization approach to controller design for pysical human-robot interaction
    (Institute of Electrical and Electronics Engineers (IEEE), 2020) Tokatlı, Ozan; Patoğlu, Volkan; Department of Mechanical Engineering; Aydın, Yusuf; Başdoğan, Çağatay; Faculty Member; Department of Mechanical Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; 125489
    Physical human-robot interaction (pHRI) integrates the benefits of human operator and a collaborative robot in tasks involving physical interaction, with the aim of increasing the task performance. However, the design of interaction controllers that achieve safe and transparent operations is challenging, mainly due to the contradicting nature of these objectives. Knowing that attaining perfect transparency is practically unachievable, controllers that allow better compromise between these objectives are desirable. In this article, we propose a multicriteria optimization framework, which jointly optimizes the stability robustness and transparency of a closed-loop pHRI system for a given interaction controller. In particular, we propose a Pareto optimization framework that allows the designer to make informed decisions by thoroughly studying the tradeoff between stability robustness and transparency. The proposed framework involves a search over the discretized controller parameter space to compute the Pareto front curve and a selection of controller parameters that yield maximum attainable transparency and stability robustness by studying this tradeoff curve. The proposed framework not only leads to the design of an optimal controller, but also enables a fair comparison among different interaction controllers. In order to demonstrate the practical use of the proposed approach, integer and fractional order admittance controllers are studied as a case study and compared both analytically and experimentally. The experimental results validate the proposed design framework and show that the achievable transparency under fractional order admittance controller is higher than that of integer order one, when both controllers are designed to ensure the same level of stability robustness.