Digitalisierung
Refine
Year of publication
Document Type
Has Fulltext
- no (32)
Is part of the Bibliography
- no (32)
Keywords
- Simulation (5)
- Compliant tensegrity structure (2)
- Logistik (2)
- Production (2)
- fMRI (2)
- 3D depth camera (1)
- 3D scanning (1)
- Blind source separation (1)
- Brain Mapping (1)
- Brain/diagnostic imaging (1)
Institute
- Fakultät Maschinenbau (32) (remove)
Begutachtungsstatus
- peer-reviewed (26)
Background and objectiveDue to the high prevalence of dental caries, fixed dental restorations are regularly required to restore compromised teeth or replace missing teeth while retaining function and aesthetic appearance. The fabrication of dental restorations, however, remains challenging due to the complexity of the human masticatory system as well as the unique morphology of each individual dentition. Adaptation and reworking are frequently required during the insertion of fixed dental prostheses (FDPs), which increase cost and treatment time. This article proposes a data-driven approach for the partial reconstruction of occlusal surfaces based on a data set that comprises 92 3D mesh files of full dental crown restorations.MethodsA Generative Adversarial Network (GAN) is considered for the given task in view of its ability to represent extensive data sets in an unsupervised manner with a wide variety of applications. Having demonstrated good capabilities in terms of image quality and training stability, StyleGAN-2 has been chosen as the main network for generating the occlusal surfaces. A 2D projection method is proposed in order to generate 2D representations of the provided 3D tooth data set for integration with the StyleGAN architecture. The reconstruction capabilities of the trained network are demonstrated by means of 4 common inlay types using a Bayesian Image Reconstruction method. This involves pre-processing the data in order to extract the necessary information of the tooth preparations required for the used method as well as the modification of the initial reconstruction loss.ResultsThe reconstruction process yields satisfactory visual and quantitative results for all preparations with a root mean square error (RMSE) ranging from 0.02 mm to 0.18 mm. When compared against a clinical procedure for CAD inlay fabrication, the group of dentists preferred the GAN-based restorations for 3 of the total 4 inlay geometries.ConclusionsThis article shows the effectiveness of the StyleGAN architecture with a downstream optimization process for the reconstruction of 4 different inlay geometries. The independence of the reconstruction process and the initial training of the GAN enables the application of the method for arbitrary inlay geometries without time-consuming retraining of the GAN.
Generative deep learning approaches for the design of dental restorations: A narrative review
(2024)
Objectives:
This study aims to explore and discuss recent advancements in tooth reconstruction utilizing deep learning (DL) techniques. A review on new DL methodologies in partial and full tooth reconstruction is conducted.
Data/Sources:
PubMed, Google Scholar, and IEEE Xplore databases were searched for articles from 2003 to 2023.
Study selection:
The review includes 9 articles published from 2018 to 2023. The selected articles showcase novel DL approaches for tooth reconstruction, while those concentrating solely on the application or review of DL methods are excluded. The review shows that data is acquired via intraoral scans or laboratory scans of dental plaster models. Common data representations are depth maps, point clouds, and voxelized point clouds. Reconstructions focus on single teeth, using data from adjacent teeth or the entire jaw. Some articles include antagonist teeth data and features like occlusal grooves and gap distance. Primary network architectures include Generative Adversarial Networks (GANs) and Transformers. Compared to conventional digital methods, DL-based tooth reconstruction reports error rates approximately two times lower.
Conclusions:
Generative DL models analyze dental datasets to reconstruct missing teeth by extracting insights into patterns and structures. Through specialized application, these models reconstruct morphologically and functionally sound dental structures, leveraging information from the existing teeth. The reported advancements facilitate the feasibility of DL-based dental crown reconstruction. Beyond GANs and Transformers with point clouds or voxels, recent studies indicate promising outcomes with diffusion-based architectures and innovative data representations like wavelets for 3D shape completion and inference problems.
Clinical significance:
Generative network architectures employed in the analysis and reconstruction of dental structures demonstrate notable proficiency. The enhanced accuracy and efficiency of DL-based frameworks hold the potential to enhance clinical outcomes and increase patient satisfaction. The reduced reconstruction times and diminished requirement for manual intervention may lead to cost savings and improved accessibility of dental services.
In the context of production and factory planning, the expansion of the factory must already be taken into account during initial planning. This results in an increase in planning complexity, as the involved planners have to know the expansion stages of the factory in the different time periods and have to evaluate concept modifications across all time periods. This paper presents an idea for a planning tool, which takes expansion stages into consideration. The data model contains all relevant information to generate a simulation model of the factory in an almost automated way. The aim is to enable factory planners to quickly investigate concept changes with the help of simulation, for example, to identify bottlenecks.
One major goal of green-field factory planning is to decide on space requirements in the plant. In this phase, detailed information about the supply chain network (e.g. which suppliers deliver which parts) is often unavailable. Nevertheless, typical decisions in supply chain management, like the definition of replenishment processes and quantities or make-or-buy decisions, impact space requirements in the factory and should therefore be considered in the factory planning. This research article proposes a simulation approach for a factory simulation in which raw material replenishment is integrated to evaluate the space requirements for raw materials.
To evaluate the performance of a ropeway in an urban environment, simulations of the dynamic passenger transport characteristics are required. Therefore, a modular simulation model for urban ropeway networks was developed, which can be flexibly adapted to any city and passenger volume. This simulation model was used to analyze the ropeway network concept of the German city Regensburg and to determine the expected operating conditions. The passenger volume, different types of persons, their occurrence probability and their destination distribution is depending on the location and daytime and can be defined for each individual station. In an initial analysis, the number of passengers currently occurring in bus traffic were projected onto the ropeway network. To enable climate-friendly and efficient operation, different strategies were developed to significantly reduce the number of gondolas. The best fitting strategies resulted in significant cost savings while passenger comfort, as represented by queue time, remained unchanged.
Simulation Based Approach for Reconfiguration and Ramp up Scenario Analysis in Factory Planning
(2022)
Structural changes in production entail a potential economic risk for manufacturing companies. It is necessary to identify a suitable strategy for the reconfiguration process and to continue to meet the demand during the change in the factory structure and ramp-up phase. A simulation offers the possibility to analyze different ramp-up scenarios for the factory structure and to select a suitable concept for the reconfiguration process. A discrete event simulation approach is presented that can be used to evaluate variants of structural changes and serves as a basis for deciding on a reconfiguration strategy. This approach is demonstrated using a specific production step of a plant producing hydrogen electrolyzers, the results and generalized conclusions are discussed.
Internal transport systems are an essential part of intralogistics in production and distribution facilities. These are characterized by a variety of technologies as well as a multitude of interactions with other processes, such as warehouse, picking, and production processes. Therefore, resource planning and control of these systems is complex, especially for discontinuous conveyors. In this task, users can be supported by Digital Twins for decision-making, as they are suitable for investigating both future system states and possible actions. However, relevant use cases that are generally applicable across sectors as well as a generic system architecture for Digital Twins for resource planning and process control of in-plant transport systems have not yet been sufficiently investigated. In this paper, use cases are presented, relevant functions defined, and, finally, a generic functional and a logical reference architecture described. This is conducted with the design science in information systems research method together with a Systems Engineering approach. The use cases are determined at industrial partners of the research project TwInTraSys, which explores Digital Twins for the planning and control of internal transport systems. They are generalized and, thus, also applicable to other production and distribution facilities in different sectors. Further, the reference architecture can provide a basis for the successful implementation of the Digital Twin.
This paper addresses the problem of properly placing a given task in the manipulator workspace by a heuristic and numeric approach. Thus, the task is placed relatively to the manipulator for each element of the discretized workspace and the required joint torques are determined. The results are are by a torque-based optimization criterion. The modularity of this approach ensures general applicability on various systems and tasks while the high computational effort is treated by GPU parallelization. The method is presented for a given 6DOF manipulator and a highly dynamic trajectory. The resulting interactive map of the manipulator workspace gives an overview of the task dependent dynamic performance, detailed evaluation of certain solutions will show the dexterity of the proposed approach.
EMDLAB: A toolbox for analysis of single-trial EEG dynamics using empirical mode decomposition
(2015)
Background:
Empirical mode decomposition (EMD) is an empirical data decomposition technique. Recently there is growing interest in applying EMD in the biomedical field.
New method:
EMDLAB is an extensible plug-in for the EEGLAB toolbox, which is an open software environment for electrophysiological data analysis.
Results:
EMDLAB can be used to perform, easily and effectively, four common types of EMD: plain EMD, ensemble EMD (EEMD), weighted sliding EMD (wSEMD) and multivariate EMD (MEMD) on EEG data. In addition, EMDLAB is a user-friendly toolbox and closely implemented in the EEGLAB toolbox.
Comparison with existing methods:
EMDLAB gains an advantage over other open-source toolboxes by exploiting the advantageous visualization capabilities of EEGLAB for extracted intrinsic mode functions (IMFs) and Event-Related Modes (ERMs) of the signal.
Conclusions:
EMDLAB is a reliable, efficient, and automated solution for extracting and visualizing the extracted IMFs and ERMs by EMD algorithms in EEG study.
In order to better understand the mechanisms of gas transport during High Frequency Oscillatory Ventilation (HFOV) Magnetic Resonance Imaging (MRI) with contrast gases and numerical flow simulations based on Computational Fluid Dynamics(CFD) methods are performed. Validation of these new techniques is conducted by comparing the results obtained with simplified models of the trachea and a first lung bifurcation as well as in a cast model of the upper central airways with results achieved from conventional fluid mechanical measurement techniques like e.g. Laser Doppler Anemometry (LDA). Further it is demonstrated that MRI of experimental HFOV is feasible and that Hyperpolarized 3He allows for imaging the gas re-distribution inside the lung. Finally, numerical results of oscillatory flow in a 3rd generation model of the lung as well as the impact of endotracheal tubes on the flow regime development in a trachea model are presented.
Simultaneous EEG-fMRI provides an increasingly attractive research tool to investigate cognitive processes with high temporal and spatial resolution. However, artifacts in EEG data introduced by the MR scanner still remain a major obstacle. This study, employing commonly used artifact correction steps, shows that head motion, one overlooked major source of artifacts in EEG-fMRI data, can cause plausible EEG effects and EEG–BOLD correlations. Specifically, low-frequency EEG (< 20 Hz) is strongly correlated with in-scanner movement. Accordingly, minor head motion (< 0.2 mm) induces spurious effects in a twofold manner: Small differences in task-correlated motion elicit spurious low-frequency effects, and, as motion concurrently influences fMRI data, EEG–BOLD correlations closely match motion-fMRI correlations. We demonstrate these effects in a memory encoding experiment showing that obtained theta power (~ 3–7 Hz) effects and channel-level theta–BOLD correlations reflect motion in the scanner. These findings highlight an important caveat that needs to be addressed by future EEG-fMRI studies.
This paper briefly presents the challenges for order control and release of multi-zone order picking systems. On the one hand, the order control must ensure that all orders are processed on time, and on the other hand, the space requirements (buffer) and the utilisation of the zones must be considered.
Within the framework of a case study, different strategies for order release were developed. The paper shortly describes the ideas of the strategies and presents results of a case-based simulative evaluation of the strategies. The findings of the simulation study are the basis for the development of a digital twin for the operational control of multi-zone picking systems.
Der Digitale Zwilling (DZ) ist ein wichtiger Bestandteil der Industrie 4.0 und ermöglicht Anwendungen wie Predictive Maintenance, virtuelles Prototyping oder die Steuerung von Produktions- und Logistikprozessen. Herausforderungen bei der Entwicklung des Digitalen Zwillings entstehen durch fehlende Struktur und Standards. Mit diesem Beitrag soll ein Vorgehensmodell für die Erstellung eines Digitalen Zwillings im Bereich der Produktion und Logistik aufgezeigt werden. Das Vorgehensmodell hilft bei der Einordnung, für welche Anwendungsfälle ein Digitaler Zwilling entwickelt werden kann, welche Schritte bei einer Umsetzung erfolgen müssen, und gibt einen Überblick über die Voraussetzungen und Komplexität bei der Entwicklung. Das zentrale Element bildet dabei die zielgerichtete Aufbereitung und Analyse der zugrunde liegenden Daten mittels des in der Industrie etablierten Vorgehensmodell CRISP-DM.
The method of loci is one, if not the most, efficient mnemonic encoding strategy. This spatial mnemonic combines the core cognitive processes commonly linked to medial temporal lobe (MTL) activity: spatial and associative memory processes. During such processes, fMRI studies consistently demonstrate MTL activity, while electrophysiological studies have emphasized the important role of theta oscillations (3–8 Hz) in the MTL. However, it is still unknown whether increases or decreases in theta power co-occur with increased BOLD signal in the MTL during memory encoding. To investigate this question, we recorded EEG and fMRI separately, while human participants used the spatial method of loci or the pegword method, a similarly associative but nonspatial mnemonic. The more effective spatial mnemonic induced a pronounced theta power decrease source localized to the left MTL compared with the nonspatial associative mnemonic strategy. This effect was mirrored by BOLD signal increases in the MTL. Successful encoding, irrespective of the strategy used, elicited decreases in left temporal theta power and increases in MTL BOLD activity. This pattern of results suggests a negative relationship between theta power and BOLD signal changes in the MTL during memory encoding and spatial processing. The findings extend the well known negative relation of alpha/beta oscillations and BOLD signals in the cortex to theta oscillations in the MTL.
Independent component analysis (ICA), being a data-driven method, has been shown to be a powerful tool for functional magnetic resonance imaging (fMRI) data analysis. One drawback of this multivariate approach is that it is not, in general, compatible with the analysis of group data. Various techniques have been proposed to overcome this limitation of ICA. In this paper, a novel ICA-based workflow for extracting resting-state networks from fMRI group studies is proposed. An empirical mode decomposition (EMD) is used, in a data-driven manner, to generate reference signals that can be incorporated into a constrained version of ICA (cICA), thereby eliminating the inherent ambiguities of ICA. The results of the proposed workflow are then compared to those obtained by a widely used group ICA approach for fMRI analysis. In this study, we demonstrate that intrinsic modes, extracted by EMD, are suitable to serve as references for cICA. This approach yields typical resting-state patterns that are consistent over subjects. By introducing these reference signals into the ICA, our processing pipeline yields comparable activity patterns across subjects in a mathematically transparent manner. Our approach provides a user-friendly tool to adjust the trade-off between a high similarity across subjects and preserving individual subject features of the independent components.
Independent component analysis (ICA), as a data driven method, has shown to be a powerful tool for functional magnetic resonance imaging (fMRI) data analysis. One drawback of this multivariate approach is, that it is naturally not convenient for analysis of group studies. Therefore various techniques have been proposed in order to overcome this limitation of ICA. In this paper a novel ICA based work-flow for extracting resting state networks from fMRI group studies is proposed. An empirical mode decomposition (EMD) is used to generate reference signals in a data driven manner, which can be incorporated into a constrained version of ICA (cICA), what helps to overcome the inherent ambiguities. The results of the proposed workflow are then compared to those obtained by a widely used group ICA approach. It is demonstrated that intrinsic modes, extracted by EMD, are suitable to serve as references for cICA to obtain typical resting state patterns, which are consistent over subjects. This novel processing pipeline makes it transparent for the user, how comparable activity patterns across subjects emerge, and also the trade-off between similarity across subjects and preserving individual features can be well adjusted and adapted for different requirements in the new work-flow.
Background and objective
The study follows the proposal of decomposing a given data matrix into a product of independent spatial and temporal component matrices. A multi-variate decomposition approach is presented, based on an approximate diagonalization of a set of matrices computed using a latent space representation.
Methods
The proposed methodology follows an algebraic approach, which is common to space, temporal or spatiotemporal blind source separation algorithms. More specifically, the algebraic approach relies on singular value decomposition techniques, which avoids computationally costly and numerically instable matrix inversion. The method is equally applicable to correlation matrices determined from second order correlations or by considering fourth order correlations.
Results
The resulting algorithms are applied to fMRI data sets either to extract the underlying fMRI components or to extract connectivity maps from resting state fMRI data collected for a dynamic functional connectivity analysis. Intriguingly, our algorithm shows increased spatial specificity compared to common approaches, while temporal precision stays similar.
Conclusion
The study presents a novel spatiotemporal blind source separation algorithm, which is both robust and avoids parameters that are difficult to fine tune. Applied on experimental data sets, the new method yields highly confined and focused areas with least spatial extent in the retinotopy case, and similar results in the dynamic functional connectivity analyses compared to other blind source separation algorithms. Therefore, we conclude that our novel algorithm is highly competitive and yields results, which are superior or at least similar to existing approaches.
Investigating temporal variability of functional connectivity is an emerging field in connectomics. Entering dynamic functional connectivity by applying sliding window techniques on resting-state fMRI (rs-fMRI) time courses emerged from this topic. We introduce frequency-resolved dynamic functional connectivity (frdFC) by means of multivariate empirical mode decomposition (MEMD) followed up by filter-bank investigations. In general, we find that MEMD is capable of generating time courses to perform frdFC and we discover that the structure of connectivity-states is robust over frequency scales and even becomes more evident with decreasing frequency. This scale-stability varies with the number of extracted clusters when applying k-means. We find a scale-stability drop-off from k = 4 to k = 5 extracted connectivity-states, which is corroborated by null-models, simulations, theoretical considerations, filter-banks, and scale-adjusted windows. Our filter-bank studies show that filter design is more delicate in the rs-fMRI than in the simulated case. Besides offering a baseline for further frdFC research, we suggest and demonstrate the use of scale-stability as a possible quality criterion for connectivity-state and model selection. We present first evidence showing that connectivity-states are both a multivariate, and a multiscale phenomenon. A data repository of our frequency-resolved time-series is provided.
Background:
Brain lesions in language-related cortical areas remain a challenge in the clinical routine. In recent years the resting-state fMRI (rs-fMRI) was shown to be a feasible method for preoperative language assessment. The aim of this study was to examine whether language-related resting-state components, which have been obtained using a data-driven independent-component-based identification algorithm, can be supportive in determining language dominance in the left or right hemisphere.
Methods:
Twenty patients suffering from brain lesions close to supposed language relevant cortical areas were included. Rs-fMRI and task-based (tb-fMRI) were performed for the purpose of preoperative language assessment. Tb-fMRI included a verb generation task with an appropriate control condition (a syllable switching task) to decompose language critical and language supportive processes. Subsequently, the best fitting ICA component for the resting-state language network (RSLN) referential to general linear models (GLMs) of the tb-fMRI (including models with and without linguistic control conditions) was identified using an algorithm based on the Dice-index.
Results:
The RSLNs associated with GLMs using a linguistic control condition led to significantly higher laterality indices than GLM baseline contrasts. LIs derived from GLM contrasts with and without control conditions alone did not differ significantly.
Conclusion:
In general, the results suggest that determining language dominance in the human brain is feasible both with tb-fMRI and rs-fMRI, and in particular, the combination of both approaches yields a higher specificity in preoperative language assessment. Moreover, we can conclude that the choice of the language mapping paradigm is crucial for the mentioned benefits.
This short survey reviews the recent literature on the relationship between the brain structure and its functional dynamics. Imaging techniques such as diffusion tensor imaging (DTI) make it possible to reconstruct axonal fiber tracks and describe the structural connectivity (SC) between brain regions. By measuring fluctuations in neuronal activity, functional magnetic resonance imaging (fMRI) provides insights into the dynamics within this structural network. One key for a better understanding of brain mechanisms is to investigate how these fast dynamics emerge on a relatively stable structural backbone. So far, computational simulations and methods from graph theory have been mainly used for modeling this relationship. Machine learning techniques have already been established in neuroimaging for identifying functionally independent brain networks and classifying pathological brain states. This survey focuses on methods from machine learning, which contribute to our understanding of functional interactions between brain regions and their relation to the underlying anatomical substrate.
The use of compliant tensegrity structures in robotic applications offers several advantageous properties. In this work the dynamic behaviour of a planar tensegrity structure with multiple static equilibrium configurations is analysed, with respect to its further use in a two-finger-gripper application. In this application, two equilibrium configurations of the structure correspond to the opened and closed states of the gripper. The transition between these equilibrium configurations, caused by a proper selected actuation method, is essentially dependent on the actuation parameters and on the system parameters. To study the behaviour of the dynamic system and possible actuation methods, the nonlinear equations of motion are derived and transient dynamic analyses are performed. The movement behaviour is analysed in relation to the prestress of the structure and actuation parameters.
As camera and projector hardware gets more and more affordable and software algorithms more sophisticated, the area of application for camera-projector configurations widens its scope. Unlike for sole camera calibration, only few comparative surveys for projector calibration methods exist. Therefore, in this paper, two readily available algorithms for the calibration of those arrays are studied and methods for the evaluation of the results are proposed. Additionally, statistical evaluations under consideration of different influencing factors like the hardware arrangement, the number of input images or the calibration target characteristics on the accuracy of the calibration results are performed. Ground truth comparison data is realized through a robotic system and structured light 3D scanning.
Modeling, identification and control of an antagonistically actuated joint for telerobotic systems
(2015)
Within this paper a modeling, identification and control technique for an antagonistically actuated joint consisting of two pneumatically actuated muscles is presented. The antagonistically actuated joint acts as a test bench for control architectures which are going to be used to control an exoskeleton within a telerobotic system. A static and dynamic model of the muscle and the joint is derived and the parameters of the models are identified using a least-squares algorithm. The control architecture, consisting of a inner pressure and an outer position controller is presented. The pressure controller is evaluated using switching valves compared against proportional valves.
Tightening quality requirements of industrial products involving manual assembly lead to the development of assisting workbenches with integrated functions to support workers performing these manual tasks. This contribution discusses a new approach to learning transitions of a finite state automaton representing the sequence of work tasks based on the video stream of a 3D depth camera. Preprocessed video data is fed into a three-stage classification scheme based on support vector machines. The results of the classification are then related to the state automation to trigger state transitions indicating the completion of a specific work task and the start of the next one. The proposed approach has been evaluated at an industrial assembly process of moderate complexity and shows very robust results with respect to disturbances caused by inaccurate object classification.
Steigende Anforderungen an die Qualität von zum Teil manuell gefertigten Produkten führen dazu, dass Handarbeitsplätze mit Assistenzsystemen für die Unterstützung der am Arbeitsplatz arbeitenden Mitarbeiterinnen und Mitarbeiter ausgestattet werden. Der Beitrag beschreibt einen neuen Ansatz, um mittels Verfahren des maschinellen Lernens die Objekterkennung sowie die Transitionen eines, den Arbeitsprozess repräsentierenden Zustandsautomaten eines solchen Systems einzulernen. Hierfür werden nach einer Vorverarbeitung Daten aus einer Tiefenkamera in drei Stufen durch Support Vector Machines (SVM) klassifiziert und das Ergebnis mit dem Zustandsautomaten verknüpft. Das Konzept wird an einem industriellen Montageprozess überschaubarer Komplexität evaluiert; es zeigt gute Ergebnisse hinsichtlich der Robustheit gegenüber Fehlern bei der Objektklassifikation.
Rodents use their mystacial vibrissae, e.g., to recognize the shape or determine the surface texture of an object. The vibrissal sensory system consists of two components: the hair shaft and the follicle-sinus complex (FSC). Both components affect the collection of information, but the impacts of the different properties are not completely clear. Borrowing the natural example, the goal is to design a powerful artificial sensor. The influence of a continuous visco-elastic support is analyzed for an artificial sensor following hypotheses about the FSC. Starting with a theoretical treatment of this scenario, the vibrissa is modeled as an Euler-Bernoulli bending beam with a partially continuous visco-elastic support. The numerical simulations are validated by experiments. Using a steel strip as a technical vibrissa and a magneto-sensitive elastomer (MSE) as representation of the artificial continuous visco-elastic support, FSC respectively, the first resonance frequency is determined.
Often, the operating range of mobile robots is limited by environ- mental circumstances like obstacles or gaps. Therefore, an adaptation of the motion principle is required to enable an operating continuation of such robots. A jumping motion is a promising approach. This motion type allows to cross gaps or to overcome obstacles where common motion principles which bases on wheels or legs fail. However, especially during landing large forces occur as a consequence of the impact with the ground. This issue encourages the use of compliant tensegrity structures which feature a great shock resistance. In this paper a tensegrity structure with multiple equilibrium configurations is considered. The two-dimensional structure is equipped with two actuators to vary the prestress of the system. The tensegrity structure is in contact to a horizontal plane due to gravity. Two actuation strategies are derived. Beside varying the prestress state of the structure, a jump can be realized by changing the equilibrium configuration. Both actuation strategies and the corresponding motion characteristics are evaluated by numeric simulations. The results emphasize the advantageous properties of tensegrity structures for a jumping motion system. In particular, the multistabilty of the structure allows a simple actuation strategy for a reliable jumping motion.
This paper describes the development of a tilting locomotion system based on a compliant tensegrity structure with multiple stable equilibrium configurations. A tensegrity structure featuring 4 stable equilibrium states is considered. The mechanical model of the structure is presented and the according equations of motion are derived. The variation of the length of selected structural members allows to influence the prestress state and the corresponding shape of the tensegrity structure. Based on bifurcation analyses a reliable actuation strategy to control the current equilibrium state is designed. In this work, the tensegrity structure is assumed to be in contact with a horizontal plane due to gravity. The derived actuation strategy is utilized to generate tilting locomotion by successively changing the equilibrium state. Numerical simulations are evaluated considering the locomotion characteristics. In order to validate this theoretical approach a prototype is developed. Experiments regarding to the equilibrium configurations, the actuation strategy and the locomotion characteristics are evaluated using image processing tools and motion capturing. The results verify the theoretical data and confirm the working principle of the investigated tilting locomotion system. This approach represents a feasible actuation strategy to realize a reliable tilting locomotion utilizing the multistability of compliant tensegrity structures.
In this work, a tensegrity structure with spatially curved members is applied as rolling locomotion system. The actuation of the structure allows a variation of the originally cylindrical shape to a conical shape. Moreover, the structure is equipped with internal movable masses to control the position of the center of mass of the structure. To control the locomotion system a reliable actuation strategy is required. Therefore, the kinematics of the system considering the nonholonomic constraints are derived in this paper. Based on the resulting insight in the locomotion behavior a feasible actuation strategy is designed to control the trajectory of the system. To verify this approach kinematic analyses are evaluated numerically. The simulation data confirm the path following due to an appropriate shape change of the tensegrity structure. Thus, this system enables a two-dimensional rolling locomotion.
In this paper compliant multistable tensegrity structures with discrete variable stiffness are investigated. The different stiffness states result from the different prestress states of these structures corresponding to the equilibrium configurations. Three planar tensegrity mechanisms with two stable equilibrium configurations are considered exemplarily. The overall stiffness of these structures is characterized by investigations with regard to their geometric nonlinear static behavior. Dynamical analyses show the possibility of the change between the equilibrium configurations and enable the derivation of suitable actuation strategies.
The use of mechanically prestressed compliant structures in soft robotics is a recently discussed topic. Tensegrity structures, consisting of a set of rigid disconnected compressed members connected to a continuous net of prestressed elastic tensioned members build one specific class of these structures. Robots based on these structures have manifold shape changing abilities and can adapt their mechanical properties reversibly by changing of their prestress state according to specific tasks.
In the paper selected aspects on the potential use of elastomer materials in these structures are discussed with the help of theoretical analysis. Therefore, a selected basic tensegrity structure with elastomer members is investigated focusing on the stiffness and shape changing ability in dependence of the nonlinear hyperelastic behavior of the used elastomer materials. The considered structure is compared with a conventional tensegrity structure with linear elastic tensioned members. Finally, selected criterions for the advantageous use of elastomer materials in compliant tensegrity robots are discussed.