Fakultät für Elektrotechnik und Informationstechnik

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https://open-science.ub.ovgu.de/handle/684882692/114

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    On the influence of head motion on the swimming kinematics of robotic fish
    (2022) Abbaszadeh, Shokoofeh; Kiiski, Yanneck; Leidhold, Roberto; Hoerner, Stefan;
    Up to now bio-inspired fish-mimicking robots fail when competing with the swimming performance of real fish. While tail motion has been studied extensively, the influence of the head motion is still not fully understood and its active control is challenging. In this experimental study, we show that head yawing strongly impacts on the propulsion force and determines the optimal fin actuation amplitude and tail beat frequency when aiming for a maximal propulsion force. In a parametric experimental study on a tethered 367 mm long fish robot the pivot point location of the head yaw has been varied along with tail beat frequency and actuation amplitude. The experiments took place in a still water tank and the swimming force has been measured with a single axis load cell. The robot is actuated with non-conventional area actuators based on micro fiber composites. 105 parameter sets have been investigated while the highest pivot point distance of roughly 0.36 body length from the nose tip provided the highest propulsion force of 500~mN with the lowest actuation frequency of 2.5 Hz and the highest head motion amplitude of a magnitude of 0.18 body length. Even though the pivot point location on a free swimming robot is a consequence of the complex fluid-structure interactions of fish and fluid, the results provide valuable information for the design of fish mimicking robots and questions the paradigm that head yaw is a simple recoil effect from tail motion and has to be minimized for an effective propulsion.
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    The TapCorder Data Set
    (Otto-von-Guericke Universität Magdeburg, 2024) Seiffert, Udo; Jadhav, Ashish Shivajirao
    This study presents a novel approach for classifying oily or cream-like substances using diffraction data captured on a smartphone camera, applied specifically to assessing engine oil quality. Utilising the COMPOLYTICS(R) TapCorder approach, optical diffraction patterns were analysed with a tailored feature extraction method. The performance of three machine learning paradigms － Multilayer Perceptrons (MLP), Learning Vector Quantization (LVQ), and Radial Basis Function Networks (RBFN) － was analysed in classifying new and used oil samples. MLP achieved the highest accuracy, while LVQ required the least computation time, highlighting trade-offs relevant for consumer-focused applications. This work clearly demonstrates the feasibility of accessible, low-cost chemical substance analysis via smartphone-based systems.