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Publication Open Access 3D microprinting of iron platinum nanoparticle-based magnetic mobile microrobots(Wiley, 2021) Giltinan, Joshua; Sridhar, Varun; Bozüyük, Uğur; Sheehan, Devin; Department of Mechanical Engineering; Sitti, Metin; Faculty Member; Department of Mechanical Engineering; School of Medicine; College of Engineering; 297104Wireless magnetic microrobots are envisioned to revolutionize minimally invasive medicine. While many promising medical magnetic microrobots are proposed, the ones using hard magnetic materials are not mostly biocompatible, and the ones using biocompatible soft magnetic nanoparticles are magnetically very weak and, therefore, difficult to actuate. Thus, biocompatible hard magnetic micro/nanomaterials are essential toward easy-to-actuate and clinically viable 3D medical microrobots. To fill such crucial gap, this study proposes ferromagnetic and biocompatible iron platinum (FePt) nanoparticle-based 3D microprinting of microrobots using the two-photon polymerization technique. A modified one-pot synthesis method is presented for producing FePt nanoparticles in large volumes and 3D printing of helical microswimmers made from biocompatible trimethylolpropane ethoxylate triacrylate (PETA) polymer with embedded FePt nanoparticles. The 30 mu m long helical magnetic microswimmers are able to swim at speeds of over five body lengths per second at 200Hz, making them the fastest helical swimmer in the tens of micrometer length scale at the corresponding low-magnitude actuation fields of 5-10mT. It is also experimentally in vitro verified that the synthesized FePt nanoparticles are biocompatible. Thus, such 3D-printed microrobots are biocompatible and easy to actuate toward creating clinically viable future medical microrobots.Publication Open 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; 125489Physical 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.
Publication Metadata only Are tutor robots for everyone? the influence of attitudes, anxiety, and personality on robot-led language learning(Springer, 2022) Kumkale, G. Tarcan; Department of Psychology; Department of Psychology; Department of Psychology; N/A; Department of Psychology; Küntay, Aylin C.; Kanero, Junko; Oranç, Cansu; Koşkulu, Sümeyye; Göksun, Tilbe; Faculty Member; Researcher; Researcher; Master Student; Faculty Member; Department of Psychology; College of Social Sciences and Humanities; College of Social Sciences and Humanities; College of Social Sciences and Humanities; Graduate School of Social Sciences and Humanities; College of Social Sciences and Humanities; 178879; N/A; N/A; N/A; 47278Do some individuals benefit more from social robots than others? Using a second language (L2) vocabulary lesson as an example, this study examined how individual differences in attitudes toward robots, anxiety in learning L2, and personality traits may be related to the learning outcomes. One hundred and two native Turkish-speaking adults were taught eight English words in a one-on-one lesson either with the NAO robot (N = 51) or with a human tutor (N = 51). The results in both production and receptive language tests indicated that, following the same protocol, the two tutors are fairly comparable in teaching L2 vocabulary. Negative attitudes toward robots and anxiety in L2 learning impeded participants from learning vocabulary in the robot tutor condition whereas the personality trait of extroversion negatively predicted vocabulary learning in the human tutor condition. This study is among the first to demonstrate how individual differences can affect learning outcomes in robot-led sessions and how general attitudes toward a type of device may affect the ways humans learn using the device.Publication Metadata only BirdBot achieves energy-efficient gait with minimal control using avian-inspired leg clutching(American Association for the Advancement of Science (AAAS), 2022) Badri-Sprowitz, Alexander; Sarvestani, Alborz Aghamaleki; Daley, Monica A.; N/A; Department of Mechanical Engineering; Sitti, Metin; Faculty Member; Department of Mechanical Engineering; School of Medicine; College of Engineering; 297104Designers of legged robots are challenged with creating mechanisms that allow energy-efficient locomotion with robust and minimalistic control. Sources of high energy costs in legged robots include the rapid loading and high forces required to support the robot's mass during stance and the rapid cycling of the leg's state between stance and swing phases. Here, we demonstrate an avian-inspired robot leg design, BirdBot, that challenges the reliance on rapid feedback control for joint coordination and replaces active control with intrinsic, mechanical coupling, reminiscent of a self-engaging and disengaging clutch. A spring tendon network rapidly switches the leg's slack segments into a loadable state at touchdown, distributes load among joints, enables rapid disengagement at toe-off through elastically stored energy, and coordinates swing leg flexion. A bistable joint mediates the spring tendon network's disengagement at the end of stance, powered by stance phase leg angle progression. We show reduced knee-flexing torque to a 10th of what is required for a nonclutching, parallel-elastic leg design with the same kinematics, whereas spring-based compliance extends the leg in stance phase. These mechanisms enable bipedal locomotion with four robot actuators under feedforward control, with high energy efficiency. The robot offers a physical model demonstration of an avian-inspired, multiarticular elastic coupling mechanism that can achieve self-stable, robust, and economic legged locomotion with simple control and no sensory feedback. The proposed design is scalable, allowing the design of large legged robots. BirdBot demonstrates a mechanism for self-engaging and disengaging parallel elastic legs that are contact-triggered by the foot's own lever-arm action.Publication Metadata 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 EngineeringElectroadhesion (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.Publication Metadata only 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 EngineeringLarge 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.Publication Metadata only Light-driven carbon nitride microswimmers with propulsion in biological and ionic media and responsive on-demand drug delivery(American Association for the Advancement of Science (AAAS), 2022) Sridhar, Varun; Podjaski, Filip; Alapan, Yunus; Kroeger, Julia; Grunenberg, Lars; Kishore, Vimal; Lotsch, Bettina, V; N/A; Department of Mechanical Engineering; Sitti, Metin; Faculty Member; Department of Mechanical Engineering; School of Medicine; College of Engineering; 297104We propose two-dimensional poly(heptazine imide) (PHI) carbon nitride microparticles as light-driven microswimmers in various ionic and biological media. Their high-speed (15 to 23 micrometer per second; 9.5 +/- 5.4 body lengths per second) swimming in multicomponent ionic solutions with concentrations up to 5 M and without dedicated fuels is demonstrated, overcoming one of the bottlenecks of previous light-driven microswimmers. Such high ion tolerance is attributed to a favorable interplay between the particle's textural and structural nano porosity and optoionic properties, facilitating ionic interactions in solutions with high salinity. Biocompatibility of these microswimmers is validated by cell viability tests with three different cell lines and primary cells. The nanopores of the swimmers are loaded with a model cancer drug, doxorubicin (DOX), resulting in a high (185%) loading efficiency without passive release. Controlled drug release is reported under different pH conditions and can be triggered on-demand by illumination. Light-triggered, boosted release of DOX and its active degradation products are demonstrated under oxygen-poor conditions using the intrinsic, environmentally sensitive and light-induced charge storage properties of PHI, which could enable future theranostic applications in oxygen deprived tumor regions. These organic PHI microswimmers simultaneously address the current light-driven microswimmer challenges of high ion tolerance, fuel-free high-speed propulsion in biological media, biocompatibility, and controlled on-demand cargo release toward their biomedical, environmental, and other potential applications.Publication Metadata only Magnetically actuated soft capsule endoscope for fine-needle biopsy(Mary Ann Liebert, Inc, 2020) Son, Donghoon; Gilbert, Hunter; N/A; Department of Mechanical Engineering; Sitti, Metin; Faculty Member; Department of Mechanical Engineering; School of Medicine; College of Engineering; 297104Wireless capsule endoscopes have revolutionized diagnostic procedures in the gastrointestinal (GI) tract by minimizing discomfort and trauma. Biopsy procedures, which are often necessary for a confirmed diagnosis of an illness, have been incorporated recently into robotic capsule endoscopes to improve their diagnostic functionality beyond only imaging. However, capsule robots to date have only been able to acquire biopsy samples of superficial tissues of the GI tract, which could generate false-negative diagnostic results if the diseased tissue is under the surface of the GI tract. To improve their diagnostic accuracy for submucosal tumors/diseases, we propose a magnetically actuated soft robotic capsule robot, which takes biopsy samples in a deep tissue of a stomach using the fine-needle biopsy technique. We present the design, control, and human-machine interfacing methods for the fine-needle biopsy capsule robot. Ex vivo experiments in a porcine stomach show 85% yield for the biopsy of phantom tumors located underneath the first layers of the stomach wall.Publication Metadata only Microrobotic locomotion in blood vessels: a computational study on the performance of surface microrollers in the cardiovascular system(Wiley, 2023) Bozuyuk, Ugur; Ozturk, Hakancan; Department of Mechanical Engineering; Sitti, Metin; Department of Mechanical Engineering; College of Engineering; School of MedicineSurface microrollers have emerged as a promising microrobotic platform for navigation in the circulatory system as future drug/gene delivery applications. The circulatory system comprises various vessels with different dimensions, blood flow velocities, and flow regimes. Therefore, the performance of surface microrollers would vary in blood vessels. Herein, the performance of surface microrollers, with diameters between 5 and 50 mu m, inside vessels of the systemic circulation including veins, venules, capillaries, arterioles, and arteries is investigated with computational fluid dynamics simulations. The simulation environment consists of a simplified fluid with the viscosity and density of blood, without red blood cells, in a cylindrical pipe. The microrollers demonstrate successful upstream locomotion ability in veins and partially in arteries but fail to perform in smaller blood vessels due to significant confinement and flow effects. Overall, the results presented here establish a preliminary result for the future in vivo use of surface microrollers.Publication Metadata only Object and relation centric representations for push effect prediction(Elsevier, 2024) Tekden, Ahmet E.; Asfour, Tamim; Uğur, Emre; Department of Computer Engineering; Erdem, Aykut; Department of Computer Engineering; College of EngineeringPushing is an essential non -prehensile manipulation skill used for tasks ranging from pre -grasp manipulation to scene rearrangement, reasoning about object relations in the scene, and thus pushing actions have been widely studied in robotics. The effective use of pushing actions often requires an understanding of the dynamics of the manipulated objects and adaptation to the discrepancies between prediction and reality. For this reason, effect prediction and parameter estimation with pushing actions have been heavily investigated in the literature. However, current approaches are limited because they either model systems with a fixed number of objects or use image -based representations whose outputs are not very interpretable and quickly accumulate errors. In this paper, we propose a graph neural network based framework for effect prediction and parameter estimation of pushing actions by modeling object relations based on contacts or articulations. Our framework is validated both in real and simulated environments containing different shaped multi -part objects connected via different types of joints and objects with different masses, and it outperforms image -based representations on physics prediction. Our approach enables the robot to predict and adapt the effect of a pushing action as it observes the scene. It can also be used for tool manipulation with never -seen tools. Further, we demonstrate 6D effect prediction in the lever -up action in the context of robot -based hard -disk disassembly.