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    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.
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    PublicationMetadata only
    Experimental estimation of gap thickness and electrostatic forces between contacting surfaces under electroadhesion
    (Wiley, 2024) Martinsen, Orjan Grottem; Pettersen, Fred-Johan; Colgate, James Edward; Department of Mechanical Engineering; Aliabbasi, Easa; Başdoğan, Çağatay; Department of Mechanical Engineering; Graduate School of Sciences and Engineering; College of Engineering
    Electroadhesion (EA) is a promising technology with potential applications in robotics, automation, space missions, textiles, tactile displays, and some other fields where efficient and versatile adhesion is required. However, a comprehensive understanding of the physics behind it is lacking due to the limited development of theoretical models and insufficient experimental data to validate them. This article proposes a new and systematic approach based on electrical impedance measurements to infer the electrostatic forces between two dielectric materials under EA. The proposed approach is applied to tactile displays, where skin and voltage-induced touchscreen impedances are measured and subtracted from the total impedance to obtain the remaining impedance to estimate the electrostatic forces between the finger and the touchscreen. This approach also marks the first instance of experimental estimation of the average air gap thickness between a human finger and a voltage-induced capacitive touchscreen. Moreover, the effect of electrode polarization impedance on EA is investigated. Precise measurements of electrical impedances confirm that electrode polarization impedance exists in parallel with the impedance of the air gap, particularly at low frequencies, giving rise to the commonly observed charge leakage phenomenon in EA. A novel and systematic approach is introduced, leveraging electrical impedance measurements to infer electrostatic forces between two dielectric materials under electroadhesion (EA). This innovative approach holds promise for diverse applications spanning robotics, automation, space missions, textiles, and tactile displays. Furthermore, this study sheds light on the physics of EA, offering valuable insights with implications for the design of electroadhesive devices.
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    Large language model-based chatbots in higher education
    (Wiley, 2024) Eryilmaz, Merve; Yetisen, Ail K.; Ozcan, Aydogan; Department of Mechanical Engineering; Yığcı, Defne; Taşoğlu, Savaş; Department of Mechanical Engineering; School of Medicine; College of Engineering
    Large language models (LLMs) are artificial intelligence (AI) platforms capable of analyzing and mimicking natural language processing. Leveraging deep learning, LLM capabilities have been advanced significantly, giving rise to generative chatbots such as Generative Pre-trained Transformer (GPT). GPT-1 was initially released by OpenAI in 2018. ChatGPT's release in 2022 marked a global record of speed in technology uptake, attracting more than 100 million users in two months. Consequently, the utility of LLMs in fields including engineering, healthcare, and education has been explored. The potential of LLM-based chatbots in higher education has sparked significant interest and ignited debates. LLMs can offer personalized learning experiences and advance asynchronized learning, potentially revolutionizing higher education, but can also undermine academic integrity. Although concerns regarding AI-generated output accuracy, the spread of misinformation, propagation of biases, and other legal and ethical issues have not been fully addressed yet, several strategies have been implemented to mitigate these limitations. Here, the development of LLMs, properties of LLM-based chatbots, and potential applications of LLM-based chatbots in higher education are discussed. Current challenges and concerns associated with AI-based learning platforms are outlined. The potentials of LLM-based chatbot use in the context of learning experiences in higher education settings are explored. The use of large language models (LLMs) in higher education can facilitate personalized learning experiences, advance asynchronized learning, and support instructors, students, and researchers across diverse fields. The development of regulations and guidelines that address ethical and legal issues is essential to ensure safe and responsible adaptation of LLM-based tools in real-world educational settings.