05 Fakultät Informatik, Elektrotechnik und Informationstechnik
Permanent URI for this collectionhttps://elib.uni-stuttgart.de/handle/11682/6
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Item Open Access Analytical and numerical investigations of form-finding methods for tensegrity structures(2007) Gomez Estrada, Giovani; Bungartz, Hans-Joachim (Prof. Dr.)The analysis of statically indeterminate structures requires the calculation of an initial equilibrium geometry. Tensegrity structures are one of such statically indeterminate structures, with the additional constraint of holding their equilibrium configuration with the action of internal forces and without any anchorage point or external forces. The only source of balance is the state of self-stress held among tensile and compression forces. Tensegrity structures are thus statically indeterminate structures in a stable state of self-stressed self-equilibrium. The basic problem with the modelling of statically indeterminate structures is that there is no unique solution for the forces or geometry that equilibrate a structure. This is where form-finding comes into play. The process of determining their three-dimensional equilibrium shape is commonly called form-finding. This dissertation presents two investigations, one analytical and one numerical on the form-finding of tensegrity structures. Both are in fact complementary. The main results from these investigations appear in [77, 78, 79, 80]. The analytical form-finding for a class of highly symmetric structures with cylindrical shape is first presented, while the numerical procedure for general structures is given in the second part. A thorough analysis of tensegrity cylinders, e.g., the triplex and the quadruplex, is presented in analytical form. Moreover, the numerical procedure here presented is able to reproduce the results obtained with other form-finding methods with great accuracy. The versatility of the novel numerical form-finding procedure is nonetheless demonstrated by solving not only cylindrical and spherical but also new tensegrity structures.Item Open Access Learning-based control and localization of magnetic soft millirobots(2024) Demir, Sinan Özgün; Sitti, Metin (Prof. Dr.)Soft millirobots have promising biomedical applications due to mechanical compliance, absorbing excess forces without additional computational effort, and multifunctionality. Especially with wireless multimodal locomotion capabilities, magnetic soft millirobots (i.e., ≤1 cm) have emerged as potential minimally invasive medical robotic platforms as they can access confined and hard-to-reach spaces in the human body (e.g., distal vascular regions), and carry out medical applications, such as on-demand drug delivery, sensing, and embolization, in a target location. For such potential biomedical applications, the adaptivity of the robot control is essential to ensure the continuity of the operations, as task environment conditions show dynamic variations that can alter the robot's performance. However, fabrication-, material-, physical-interaction-dependent variations, and complex kinematics with virtually infinite degrees of freedom arising from the nature of their soft material structure limit the applicability of the conventional modeling and control methods. The main objective of this dissertation is to establish a data-efficient adaptive multimodal locomotion framework for the targeted application scenarios of magnetic soft millirobots. To this end, a probabilistic learning approach leveraging Bayesian optimization (BO) and Gaussian processes (GPs) is introduced to address the controller adaptation challenge. First, the efficacy of the BO to fabrication variabilities is shown on three different robots fabricated following the same steps. Next, through augmented tests on benchmark datasets, it is shown that transferring the posterior mean learned by one robot as the prior mean to the other robots and test cases improves the learning performance of BO by achieving quicker gait adaptation. Afterward, the controller adaptation method employing the proposed transfer learning approach is demonstrated in various task spaces with varying surface adhesion, surface roughness, and medium viscosity properties. To further improve the adaptation performance by including multimodal locomotion, the sim-to-real transfer learning method is developed in the third study. In this regard, a data-driven simulation environment is designed, and its accuracy is demonstrated by comparing the simulated results to the physical experiments. Leveraging the simulated experience and BO based transfer learning, it is demonstrated that sim-to-real transfer learning provides efficient locomotion learning. Furthermore, the adequacy of the automated locomotion adaptation through the Kullback-Leibler divergence-based domain recognition approach is shown to changing environmental conditions. As the secondary objective, a new localization method using electrical impedance tomography is introduced. The applicability of the proposed approach is demonstrated for stationary and moving cases in environments with and without any obstacles. With these contributions, this thesis proposes a domain-adaptive locomotion learning framework enabling the soft millirobot locomotion to quickly and continuously adapt to environmental changes while exploring actuation space.