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.