Augmented Reality (AR)-based programming approaches hold great promise for addressing the challenges of flexible automation by facilitating fast and intuitive programming processes. Pose estimation of novel objects enhances the program-ming experience by bridging the real and virtual environments. However, a prerequisite for pose estimation is to perform a 2D segmentation to determine the region of interest (ROI). In this work, we present an AR-based approach that enables point-and-click ROI detection through human interaction. Our proof of concept investigates how the achievable accuracy varies with the quality of the user input. The results show that the accuracy of the ROI estimation has a minimal impact on the overall accuracy. Existing limitations can be addressed by other approaches presented.

Some applications, such as surgical interventions, require that potential soft robots have the capability to alter their shape and enhance their force output on demand. This paper presents an antagonistic stiffening mechanism combining pneumatic actuation with tendon locking to achieve configuration- and stiffness control. Elongation of a soft pneumatic section, resulting from air actuation, is opposed by constraining the length of integrated tendons. These tendons can be locked in length by pneumatically activated levers at the base of each segment. Hence, tendon locking will not affect the configuration of other segments of a multi-segment manipulator. Our concept achieves a stiffness increase of up to 201.7% and a larger, more uniform radial workspace compared to the widely used pneumatic actuation concept while maintaining the low technical effort required for actuation. We also demonstrate how our actuation concept enables independent control of stiffness levels for individual segments of a multi-segment manipulator and their MR compatibility.

The extreme conditions of the deep sea require the use of large and expensive diving robots designed to withstand the high pressure in these depths. In order to reduce the costs for sediment sampling in the deep sea and thus facilitate the explorations of rare deep-sea ecosystems, the goal of this research is to design an alternative manipulator for deep-sea suction sampling. Instead of relying on heavy hydraulic rigid manipulators that deep-sea diving robots are commonly equipped with, we introduce a new concept for a lightweight actuation system that can be used in combination with a traditional diving robot and a suction sampling system. The proposed concept consists of a series of rigid links connected by angled swivel joints. Each segment is actuated by tendons, which allows for continuous bending. The system can be adapted to various sizes of host systems, and the links and joints are printed in place, simplifying the manufacturing process.

Deep-sea research represents invaluable opportunities to unravel hidden ecosystems, uncover unknown biodiversity, and provide critical insights into the Earth’s history and the impacts of climate change. Due to the extreme conditions, exploring the deep-sea traditionally requires costly equipment, such as specific diving robots, engineered to withstand the high pressure. Our research aims to reduce the costs of deep-sea sediment sampling by introducing a novel actuation system for suction samplers, that capitalises the advantages of soft material actuators. At first glance, soft material actuators may not appear suitable for the harsh conditions that prevail in the deep-sea, but when combined with a rigid, bistable mechanism there is great potential for improving the accessibility of sampling and research in this challenging environment. The binary actuation system that results from this combination, is modular, scalable, lightweight, and low cost in comparison to existing solutions.

Soft Robotics has established itself as an integral field in the broader discipline of general robotics through multiple advantages like inherent safety, adaptable morphology, and energy- and weight efficiency. Especially in environments hostile to humans and classical robots like the deep sea, soft robotic structures made out of silicone and actuated by seawater have numerous advantages. An application with a huge scientific and commercial potential for soft robotic solutions is suction sampling for marine geology in depths of up to 6000 m. In this paper, we propose a single channel soft robotic actuator that is able to bend into six directions while absorbing process forces. By embedding a low melting point alloy (LMPA) acting as switchable strain-limiting structures, the actuator is capable of hexa-planar bending of up to 40° and elongation of 30 % with only one valve used for actuation. In addition, the LMPA chambers enable a stiffening factor of 4.1 and locking the actuator in its bending state for energy efficient usage in robotic deep-sea suction sampling.

Compared to their rigid counterparts, soft material robotic systems offer great advantages when it comes to flexibility and adaptability. Despite their advantages, modeling of soft systems is still a challenging task, due to the continuous and often highly nonlinear nature of deformation these systems exhibit. Tasks like motion planning or design optimization of soft robots require computationally cheap models of the system's behavior. In this paper we address this need by deriving operational point dependent Cosserat rod models from detailed volume finite element models (FEM). While the latter offer detailed simulations, they generally come with high computational burden that hinders them from being used in time critical model-based methods like motion planning or control. Basic Cosserat rod models promise to provide computationally efficient mechanical models of soft continuum robots. By using a detailed FE model in an offline stage to identify operational point dependent Cosserat rod models, we bring together the accuracy of volumetric FEM with the efficiency of Cosserat rod models. We apply the approach to a fiber reinforced soft pneumatic bending actuator module (SPA module) and evaluate the model's predictive capabilities for a single module as well as a two-module robot.

Soft material robotics is a rather young research field in the robotics and material science communities. A popular design is the soft pneumatic actuator (SPA) which, if connected serially, becomes a highly compliant manipulator. This high compliance makes it possible to adapt to the environment and in the future might be very useful for manipulation tasks in narrow and wound environments. A central topic is the modelling of the manipulators. While comparatively rigid continuum robots are build of metal or other materials, that conduct a linear behaviour, the material used in soft material robotics often exhibits a nonlinear stress-strain relationship. In this paper we contribute an identification method for material parameters and data-based approach within the constitutive equations of a Cosserat rod model. We target bending and extension stiffness, consider shear and neglect torsional strains. The proposed method is applicable to any continuum robot which can be modelled by the classic theory of special Cosserat rods, including constraint models, and shows great improvement in experimental results with mean position errors of 0.59% reference length.

Soft material robots offer a number of advantages over traditional rigid robots in applications including human-robot interaction, rehabilitation and surgery. These robots can navigate around obstacles, elongate, squeeze through narrow openings or be squeezed - and they are considered to be inherently safe. The ability to stiffen compliant soft actuators has been achieved by embedding various mechanisms that are generally decoupled from the actuation principle. Miniaturisation becomes challenging due to space limitations which can in turn result in diminution of stiffening effects. Here, we propose to hydraulically actuate soft manipulators with low-melting-point material and, at the same time, be able to switch between a soft and stiff state. Instead of allocating an additional stiffening chamber within the soft robot, one chamber only is used for actuation and stiffening. Low Melting Point Alloy is integrated into the actuation chamber of a single-compartment soft robotic manipulator and the interfaced robotic syringe pump. Temperature change is enabled through embedded nichrome wires. Our experimental results show higher stiffness factors, from 9-12 opposing the motion of curvature, than those previously found for jamming mechanisms incorporated in separate additional chambers, in the range of 2-8 for the same motion.

Soft material robotic systems provide increased adaptability and flexibility compared to conventional rigid metal robots. The soft systems benefit from their inherent compliance, which enables them to be used in applications that require safe interaction between humans and robots or manipulation in cluttered environment. Despite advancements in recent years research on soft material robots still needs to make progress in terms of modeling for model based control or path planning. The high nonlinearity of soft material robots makes efficient and accurate modeling difficult. In this work we introduce a kinematic modeling approach based on cubic hermite splines. The method is applied to a soft pneumatic actuator and evaluated against the widely used constant curvature approach. The hermite spline offers the possibility of accurate shape reconstruction from simulated or measured deformation data. Both the shape of a robot's segment and its orientation can be approximated this way. In this paper a machine learning approach is used to train the kinematic relation between actuating pressure and configuration parameters.

Robots made from soft materials have recently captured the interest of researchers from a range of fields including engineers, material scientists, and chemists, biologists, and computer scientists. This new paradigm of soft robotics aims to develop more adaptable, more capable and safer robots that can interact with unstructured environments or permit close cooperation with humans. Current, commercially-available solutions in industrial robotics tend to take a software-centered approach to safe human-robot interaction or focus on the design of lightweight structures consisting of conventional rigid materials. Even though these solutions provide a higher safety level than traditional industrial robots, robots that comprise a sufficiently large amount of soft structures will bring about a certain degree of inherent safety. One of the major challenges in today's soft robotics research is to design robots that are inherently safe and adaptable but are also capable of bearing limited loads. Nature has solved this predicament in larger animals by incorporating stiff endoskeletons, which support the weight of the otherwise predominantly soft bodies. This insight has led to the development of a novel spine-like manipulator (SpineMan) comprised of rigid elements made from polypropylene and soft elements consisting of polyvinyl alcohol (PVA) borax hydrogels that are enveloped in a silicone skin. The initial design, material selection, synthesis, and characterization of this robot are presented in this article.

Miniaturized robots become more and more common for handling and assembly of miniaturized products. This miniaturization usually refers to the actuators, the kinematics and the sensors, but also the control electronics and their computational resources are affected. Facing these restrictions, the present article discusses patterns of robot control software that can be transferred from architectures proposed previously to microcontroller platforms. It is proposed to organize the software components according to two aspects: timing context and organizational context. Results from a case study are reported, where the architecture was implemented for a parallel kinematic five-bar robot. This study concludes that the underlying patterns help to implement control software of miniaturized robots in handling and assembly efficiently, even if hardware resources are limited.

This article presents advances in optimal experiment design, which are intended to improve the parameter identification of nonlinear state space models. Instead of using a sequence of samples from one or just a few coherent sequences, the idea of identifying nonlinear dynamic models at distinct points in the state space is considered. In this way, the placement of the experiment points is fully flexible with respect to the set of reachable points. Also, a method for model-based generation of prediction errors is proposed, which is used to compute an a-priori estimate of the sample covariance of the prediction error. This covariance matrix may be used to approximate the Fisher information matrix a-priori. The availability of the Fisher matrix a-priori is a prerequisite for experiment optimization with respect to covariance in the parameter estimates. This work is driven by the problem of parameter identification of hydraulic models. There are methods for hydraulic systems regarding the estimation of parameters from experimental data, but the choice of experiments has not been treated adequately yet. A hydraulic servo system actuating a stewart platform serves as an illustrative example to which the methods above are applied.

Force guided assembly is attractive but the implementation is challenging when uncertainties are present. In these cases, contact models that guide the assembly process are attractive. This article elaborates the usage of static contact models, which map displacements to force-torque vectors of particular contact situations (force-torque map). This model, a map of discrete points, contains force and torque values that correspond to position errors. The inversion of this map, using forces and torques measured from an assembly attempt, yields correction movements in order to accomplish the assembly iteratively. A hypothesis stating the dependency of the model's quality on its injectivity is investigated. This aspect is studied thoroughly in so-called redundancy maps, which reveal regions of considerable ambiguity of the model. Experimental results are presented, which validate the hypothesis about the dependency of the convergence of the assembly process on the ambiguity of the initial position. In addition to the peg-in-hole problem, which has become a standard scenario to validate force guided assembly, the scope of this article also covers force guided assembly of more complex parts. Here, the analysis gives evidence to believe that it is unlikely that the implementation convergences acceptably, which is validated by experimental results.

This article aderesses aspects of the a-priori computation of the Fisher information matrix. The aim of the type of Fisher matrix discussed is the experiment design for parameter estimation of nonlinear state space models. A definition for the sampling of experiments is stated, which is convenient for software implementation. Both, measurement samples that belong to a coherent sequence and samples from independent experiments, may be stored together in one single data structure. Since the covariance matrix of the prediction error is required in the Fisher matrix, a method for its a-priori prediction is proposed. A servo-hydraulic positioning system serves as a validation example. The comparison of the a-priori and a-posteriori Fisher matrices of this example show that the method proposed is suitable to provide an approximation of an optimality criterion for experiment design.

Robots based on parallel kinematic structures are known to have a small workspace compared to their installation space. To tackle this drawback a workspace enlargement approach using several workspaces going along with different working and assembly modes has been introduced in earlier publications. Robot structures designed for this approach are able to change their assembly modes. Using the drives' position feedback systems these assembly modes cannot be distinguished, which is necessary at least after startup of the robot control to correctly solve the structure's kinematic models. In this paper different approaches for a detection of the actual assembly mode of a kinematic structure are presented. Using the example of a Triglide-robot one of the approaches is demonstrated in more detail. It is based on a comparison of measured drive forces, necessary to hold the structure against gravity, with theoretical holding forces, calculated for all possible assembly modes. Experimental results show the effectiveness of the approach.

A sophisticated and advanced strategy for kinematic calibration of parallel manipulators has been reported. The strategy relies on passing type two singularities (i.e., direct kinematic singularities) and is therefore referred to as singularity-based calibration. Within the parameter estimation process, the singularity-based calibration technique requires to solve a so-called singular kinematic problem (SKP). That is, it involves finding the position of one actuator for which the structure is in a singular configuration of type two under the condition that all other actuators are held at known constant positions. So far, this problem could only be solved for some simple manipulators and structure classes with a maximum of three degree of freedom and thereby limiting the applicability of the singularity-based calibration approach. This article contributes a general approach to close this gap making singularity-based calibration widely applicable. The technique demonstrated by means of exemplarily chosen parallel kinematic structures.