Universität Stuttgart
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Item Open Access Engineered bioinspired natural dynamics and their synergy with control and learning in legged robots(2022) Ruppert, Felix; Schmitt, Syn (Prof. Dr.)The performance of legged locomotion relies on the successful mitigation of unstructured, rough terrain in the presence of sparse information and neurosensory delays. Bioinspired walking systems benefit from carefully engineered passive compliant behavior that models the inherent elastic behavior of muscle-tendon structures in animals. To leverage the passive behavior that provides energy efficiency, passive stability as well as simplified control and learning tasks to the system, locomotion control and learning algorithms have to be designed and coordinated with the natural system dynamics in mind to achieve similar locomotion behavior we see in animals. The major contribution of this thesis is the synergy of a bio-inspired leg design with biarticular muscle-tendon structures, a wearable force and pressure sensor design for closed-loop control in legged locomotion, a biologically inspired closed-loop central pattern generator with reflex-like feedback and a learning approach that enables the locomotion controller to leverage the carefully engineered natural dynamics of the robot to learn convincing locomotion skills and increase energy efficiency. The first contribution is a biologically inspired leg design focusing on the biarticular lower leg muscle-tendon structure in vertebrate animals. The biarticular elasticity provides two-dimensional passive impedance to the leg and allows the storage of energy orthogonal to the leg axis direction. The leg blueprint is characterized in its capability to store and release elastic energy in the biarticular structure. The stored energy can be recuperated back into the system and increases the energy efficiency of the leg. This leg design achieves the lowest relative cost of transport documented for all dynamically hopping and running robots. The second contribution introduces the concept of training wheels, temporary mechanical modifications to the system dynamics that shape the learning reward landscape and simplify learning locomotion directly in hardware. Through deliberate changes to the system dynamics, in this case, reduced gravity, the reward landscape can be shaped to simplify the learning process. Learning with this training wheel is safer due to smoother reward landscapes with shallower gradients. Also, the initial guess for initiating the machine learning algorithm is simplified, because the salient gradient set of viable reward signals is bigger. During the learning process, the training wheel influence can be gradually reduced and the learning algorithm converges to the solution of the initial learning problem without training wheels. The third contribution presents a rugged, lightweight force and pressure sensor for feedback information and biomechanical analysis. The sensor can be mounted on a robotic foot and provides continuous force and pressure feedback during locomotion in unstructured and soft terrain. The sensor is based on a pressure sensor, encapsulated in a polyurethane dome with an air cavity around the pressure sensor. External forces deform the sensor dome and the rising pressure in the air cavity is measured by the pressure sensor. Based on the dome geometry and material, the sensor range can be adjusted for different load cases. The sensor can be used in arrays to measure pressure distributions as well as a wearable force sensor in wet or granular media where classical force plates can not be utilized. The final contribution synergizes the individual contributions into a neuroinspired learning approach that matches a bioinspired closed-loop central pattern generator with reflex-like neuroelastic feedback to the natural dynamics of a quadruped robot with biarticular legs. Through sparse contact feedback from the foot sensor, the bioinspired central pattern generator can neuroelastically mitigate short-term perturbations to adapt the robot to its environment. Because the robot dynamics and the control task dynamics initially do not match, the controller uses the neuroelastic feedback to minimize the discrepancy between commanded and measured robot behavior. The amount of feedback activity during level walking can be used as a proxy to estimate the amount of dynamics mismatching. By minimizing the amount of required neuroelastic feedback the robot learns to neuroplastically match its control task dynamics to its natural dynamics through Bayesian optimization. Through the synergy of mechanics and control the biomechatronic system benefits from both the individual functionality of its components as well as their interplay during locomotion. The designed natural dynamics provide advantageous passive behavior to the robot and the bioinspired controller learns to leverage the natural dynamics to achieve convincing locomotion.Item Open Access Engineering and evaluating naturalistic vibrotactile feedback for telerobotic assembly(2024) Gong, Yijie; Kuchenbecker, Katherine J. (Hon.-Prof. Dr.)Teleoperation allows workers on a construction site to assemble pre-fabricated building components by controlling powerful machines from a safe distance. However, teleoperation's primary reliance on visual feedback limits the operator's efficiency in situations with stiff contact or poor visibility, compromising their situational awareness and thus increasing the difficulty of the task; it also makes construction machines more difficult to learn to operate. To bridge this gap, we propose that reliable, economical, and easy-to-implement naturalistic vibrotactile feedback could improve telerobotic control interfaces in construction and other application areas such as surgery. This type of feedback enables the operator to feel the natural vibrations experienced by the robot, which contain crucial information about its motions and its physical interactions with the environment. This dissertation explores how to deliver naturalistic vibrotactile feedback from a robot's end-effector to the hand of an operator performing telerobotic assembly tasks; furthermore, it seeks to understand the effects of such haptic cues. The presented research can be divided into four parts. We first describe the engineering of AiroTouch, a naturalistic vibrotactile feedback system tailored for use on construction sites but suitable for many other applications of telerobotics. Then we evaluate AiroTouch and explore the effects of the naturalistic vibrotactile feedback it delivers in three user studies conducted either in laboratory settings or on a construction site. The primary contribution of this dissertation is the clear explanation of details that are essential for the effective implementation of naturalistic vibrotactile feedback. We demonstrate that our accessible, audio-based approach can enhance user performance and experience during telerobotic assembly in construction and other application domains. These findings lay the foundation for further exploration of the potential benefits of incorporating haptic cues to enhance user experience during teleoperation.Item Open Access Linear response theory for equilibrium and nonequilibrium systems perturbed by nonconservative forces: the role of symmetries(2020) Asheichyk, Kiryl; Dietrich, Siegfried (Prof. Dr.)Item Open Access Deposition and characterization of multi-functional, complex thin films using atomic layer deposition for copper corrosion protection(2022) Dogan, Gül; Schütz, Gisela (Prof. Dr.)This thesis focuses on ALD thin film protection properties against corrosion of copper to develop an understanding of material interface properties and to develop novel thin films processes. This understanding is then applied to enhance materials with potential use in semiconductor devices. The main research objectives are listed below: Understanding corrosion protection properties of ALD thin films: - Development of protective thin films by combining different oxide layers - To characterize the protection properties at high temperatures and in aggressive environments, - To understand the interaction of copper and ALD protection layers when exposed to high temperatures, - Finding the optimum deposition parameters to achieve defect-free thin layers for best corrosion protection Application of ALD oxide thin films for copper corrosion protection in semiconductor devices: - Structuring the ALD thin films to make reliable interface for copper-copper interconnects with micromachining methods such as laser drilling and plasma etching - To remove ALD layers in a localized, selective way without degradation of the underlying copper layerItem 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.Item Open Access Chemically active micromotors(2021) Yu, Tingting; Fischer, Peer (Prof. Dr.)Item Open Access Experimental investigation on hydrogen isotope separation in nanoporous materials(2020) Zhang, Linda; Schmitz, Guido (Prof. Dr. Dr. h.c.)Item Open Access Electrolyte solutions at heterogeneously charged surfaces(2020) Mußotter, Maximilian; Dietrich, Siegfried (Prof. Dr.)Item Open Access Static and dynamic investigation of magnonic systems : materials, applications and modeling(2023) Schulz, Frank; Schütz, Gisela (Prof. Dr.)Item Open Access Light-driven microswimmers and their applications(2021) Sridhar, Varun; Sitti, Metin (Prof. Dr.)