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Item type:Publication, Access status: without files , Dynamic model inversion of a soft robot using servo-constraintsDue to their flexibility, soft robots offer several advantages over their rigid counterparts in applications such as sensitive manipula-tion and human-machine interaction. However, their use in agile tasks is currently limited because dynamic control of soft robots is usually dif-ficult due to large elastic deformations, underactuation, and redundant actuation. The piecewise constant curvature (PCC) model is commonly used for dynamic forward modeling of soft robots. However, analytical inversion of forward dynamics is typically infeasible due to the model’s complexity. For control purposes, though, inverse models are necessary. In this work, we propose obtaining t he inverse dynamic model of a soft robot from the forward dynamic PCC model using the servo-constraint approach. We analyze the system’s internal stability and validate the model using a physical soft robot. Experimental results demonstrate the effectiveness of our approach indynamictrajectorytracking.Publicationtype: Conference Paper83 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Combined feedforward and feedback dynamic trajectory tracking control of a soft robotSoft robots are an emerging and fast-growing field of research with applications in various areas. With the rise of new soft robotic applications, control requirements increase. Thus, control of dynamic motion is becoming more and more important. The main challenges in controlling soft robots stem from their large, continuous deformations, which lead to many degrees of freedom, as well as the difficulty of sensor integration, which leads to a limited availability of control feedback. Additionally, unknown material properties and comparatively large manufacturing inaccuracies hinder reliable modeling of soft robots. So far, in soft robotics mainly so-called quasi-static control approaches, which neglect the dynamics of the system, are used. However, modern soft robotic applications demand increasingly faster and more accurate movements. In these cases, quasi-static controllers are insufficient, and controllers which account for the robots dynamics are needed.In this work different feedforward and feedback control approaches for dynamic trajectory tracking control of soft robots are implemented and examined. A neural-network-based quasi-static feedforward controller, a dynamic feedforward controller based on the inversion of a dynamic piecewise constant curvature (PCC) model using servo-constraints, and the combination of the two different feedforward controllers with a linear quadratic regulator with integral action (LQRi) and gain scheduling are considered. For feedback control, only sensor feedback obtained from a curvature sensor integrated into the soft robot is used. The performance of these approaches is compared in simulations and experiments.Publicationtype: Conference Paper37 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Dynamic open-loop trajectory tracking control of a soft robot using servo-constraintsPublicationtype: Conference Paper76 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Experimental open-source framework for underwater pick-and-place studies with lightweight UVMS – an extensive quantitative analysisThe rise of lightweight, low-cost underwater vehicle-manipulator systems (UVMS) has made autonomous underwater manipulation increasingly accessible. Yet, most current research remains limited to isolated tasks, such as trajectory tracking or compensation of unknown payloads. Detailed experimental analyses that go beyond a proof-of-concept are particularly rare.We present a comprehensive open-source software framework for fully automated pick-and-place studies. We build upon our previous work on a task-priority control framework and extend it to enable fully autonomous manipulation. This includes a high-level decision-making process to coordinate the pick-and-place sequence and a grasp detection method to verify the successful pick-up of the object. We demonstrate this framework on the widely-used platform of a BlueROV2 and an Alpha 5 manipulator.Extensive quantitative experimental studies (100+ trials) show the picking and placing to be highly accurate, with mean position errors of <5 mm and <10 mm, respectively. We additionally validate our grasp detection approach and analyze trajectory tracking sensitivity to varying payloads and speeds. These results provide a baseline of what accuracy is currently achievable with state-of-the-art lightweight hardware under ideal research conditions. The code is available at https://github.com/HippoCampusRobotics/uvms.Publicationtype: Conference Paper127 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Dynamic end effector trajectory tracking for small-scale underwater vehicle-manipulator systems (UVMS): modeling, control, and experimental validation(IEEE, 2025-05); ; ; ; With the ongoing miniaturization, recently, lightweight, commercial underwater vehicle-manipulator systems (UVMSs) have emerged that massively lower the entry barrier into underwater manipulation. Within this research field, dynamic and accurate end effector trajectory tracking is a crucial first step in developing autonomous capabilities. In this context, coupling effects between the manipulator and vehicle dynamics are expected to pose a considerable challenge. However, UVMS control strategies analyzed in detailed experimental studies are particularly rare. We present a holistic approach based on task-priority control that we describe and discuss from modeling towards extensive experimental studies, which are crucial for the notoriously hard-to-simulate underwater domain. We demonstrate this framework on the widely used platform of a BlueROV2 and an Alpha 5 manipulator. The end effector trajectory tracking is shown to be highly accurate, with <4 cm median position error. Moreover, our experimental findings on the consideration of dynamic coupling within UVMS control motivate further research. The code is available at https://github.com/HippoCampusRobotics/uvms. A video of the results is available at https://youtu.be/IDM1I5KqlVI.Publicationtype: Conference Paper108 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , An optical curvature sensor for tip position trajectory tracking control of soft robotsAs soft robotics applications become more complex, control requirements increase. However, advanced control approaches require sensors which do not infer with the softness of the robot. In order to measure the current configuration of the robot, curvature measurements are of great importance, since bending is usually the main deformation of soft robots. This paper presents the design, fabrication, and trajectory tracking control of a novel low-cost fiber-optic soft curvature sensor that can measure the magnitude and direction of the curvature in two spatial directions. The curvature sensor can be seamlessly integrated into the silicone body of soft robots, preserving their inherent softness. The trajectory control of a beam-shaped soft robot is considered here as an example. For trajectory tracking control, an inverse kinematic feedforward controller combined with a simple feedback controller is investigated. Experiments show that the combined controller provides accurate trajectory tracking control with good disturbance rejection.Publicationtype: Conference Paper96 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Predicting against the Flow : Boosting Source Localization by Means of Field Belief Modeling using Upstream Source Proximity(IEEE, 2024-08-08); ; ; ; Time-effective and accurate source localization with mobile robots is crucial in safety-critical scenarios, e.g. leakage detection. This becomes particular challenging in realistic cluttered scenarios, i.e. in the presence of complex current flows or wind. Traditional methods often fall short due to simplifications or limited onboard resources. We propose to combine source localization with a Gaussian Markov Random Field (GMRF). This allows to improve source localization hypotheses by building on the GMRF's concentration and flow field belief that are continuously updated by gathered measurements. We introduce the upstream source proximity (USP) as a natural metric that exploits the joint knowledge represented in the field belief's concentration and flow field, i.e. predicting sources upstream. As a result, our method yields a computationally efficient source localization and field belief module providing substantially more stable gradients than conventional concentration gradient-based methods.We demonstrate the suitability of our approach in a series of numerical experiments covering complex source location scenarios. With regard to computational requirements, the method achieves update rates of 10Hz on a RaspberryPi4B.Publicationtype: Conference Paper34 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Data-driven generation of tailored wave sequences(American Society of Mechanical Engineers (ASME), 2024-06); ; ; ; This paper explores the applicability of machine learning techniques for the generation of tailored wave sequences. For this purpose, a fully convolutional neural network was implemented for relating the target wave sequence at the target location in time domain to the respective control signal of the wave board. The database was generated by means of extensive wave tank tests. The experimental campaign focused on the generation of very steep wave groups including wave breaking which cannot be covered by the simplified wave generation methods. The extensive experimental campaign was performed in a small wave tank with an fully automated approach including determination and control of the wave maker motion as well as data measurement. The training data set features wave groups of short duration based on JONSWAP spectra, where the parameters wave steepness, peak wave period and enhancement factor were systematically varied. At the end of the training process, the trained models are able to predict the wave maker control signal based on time series of the target wave defined for a specific target location in the wave tank. The accuracy of the trained models are evaluated by means of unseen validation data. In addition, the predictive accuracy of the trained models is compared with the classical linear transformation approach.Publicationtype: Conference Paper56 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Open Loop Dynamic Trajectory Tracking Control of a Soft Robot using Learned Inverse Kinematics combined with a Dynamic Model(2024-06); ; As new soft robotic applications emerge, control requirements increase. Therefore, precise control methods for soft robots are needed. The main challenge in controlling soft robots is that soft robots are often underactuated and redundantly actuated at the same time. In addition, modeling is usually difficult due to large elastic deformations, unknown material parameters, and manufacturing inaccuracies. In soft robotics, so-called kinematic controllers, which neglect the dynamics of the system, are mainly used. In particular, data-driven controllers are very popular. However, more advanced applications of soft robots require increasingly faster and more accurate movements. Here, kinematic controllers are not sufficient anymore. A direct extension of existing data-driven kinematic controllers to dynamic control is usually not practical due to the huge amount of training data required. This paper presents a new open-loop dynamic trajectory tracking control of a redundantly actuated soft robot. A combination of a kinematic data-driven controller based on neural networks and a dynamic model-based control approach based on model inversion with the servo-constraints approach is used. This combined approach preserves the advantages of learning-based kinematic controllers for the dynamic control of soft robots while keeping the amount of training data required low. Experimental results show the strength of this approach.Publicationtype: Conference Paper119 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Influence of Weighted Gradients in Topology Optimization of Flexible Multibody SystemsThis paper deals with a level set-based topology optimization of flexible bodies within flexible multibody systems. The required gradient is provided by an adjoint sensitivity analysis. In this work, a modified Solid Isotropic Material with Penalization (SIMP) method is applied, which helps to avoid numerical issues connected to spurious modes in dynamic problems. Nevertheless, the included parametrization factors lead to a weighted gradient. To test the validity of corresponding optimization results, differently weighted gradients are then used within the topology optimization of a flexible piston rod in a slider-crank mechanism. The obtained results show, among others, the range of appropriate weightings in the studied example.Publicationtype: Conference Paper67