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Subject: search.filters.subject.Human-robot interaction

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Now showing 1 - 4 of 4
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    A multi criteria optimization framework for interaction controller design for physical human-robot interaction
    (Koç University, 2019) Aydın, Yusuf; Başdoğan, Çağatay; 0000-0002-6382-7334; Koç University Graduate School of Sciences and Engineering; Mechanical Engineering; 125489
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    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.
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    Engaging human-robot interaction with batch reinforcement learning
    (Koç University, 2020) Hussain, Nusrah; Erzin, Engin; 0000-0002-2715-2368; Koç University Graduate School of Sciences and Engineering; Electrical and Electronics Engineering; 34503
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    Human inspired communicative cues for intent expressive motion generation
    (Koç University, 2018) Kebüde, Doğancan; Akgün, Barış; 0000-0002-4079-6889; Koç University Graduate School of Sciences and Engineering; Computer Science and Engineering; 258784