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Underwater soft robotics is receiving growing popularity within the scientific community, thanks to its prospective capability of tackling challenges that are hard to deal with using traditional rigid technologies, especially while interacting with an unstructured environment. Recently, we proposed a multi-module underwater robotic system with deformable propellers, inspired by bacteria morphology [1]. Here, the same bio-inspired modular structure is employed to perform manipulation tasks, in order to design a multi-functional integrated system. Employing the Geometric Variable Strain Approach, we simulate a scenario where the flagellated robot moves towards a preferred target and, using the same soft appendages, it hooks to it, simulating a monitoring task. The modeling approach and the design allow the Embodied Intelligence principles to exploit the robot’s surrounding environment (water), the shape of the grip-target and the robot’s compliant nature to mediate effective navigation and safe interaction with the target, using few control inputs.

The soft capabilities of biological appendages like the arms of Octopus vulgaris and elephants' trunks have inspired roboticists to develop their robotic equivalents. Although there have been considerable efforts to replicate their morphology and behavior patterns, we are still lagging behind in replicating the dexterity and efficiency of these biological systems. This is mostly due to the lack of development and application of dynamic controllers on these robots which could exploit the morphological properties that a soft-bodied manipulator possesses. The complexity of these high-dimensional nonlinear systems has deterred the application of traditional model-based approaches. This paper provides a machine learning-based approach for the development of dynamic models for a soft robotic manipulator and a trajectory optimization method for predictive control of the manipulator in task space. To the best of our knowledge this is the first demonstration of a learned dynamic model and a derived task space controller for a soft robotic manipulator. The validation of the controller is carried out on an octopus-inspired soft manipulator simulation derived from a piecewise constant strain approximation and then experimentally on a pneumatically actuated soft manipulator. The results indicate that such an approach is promising for developing fast and accurate dynamic models for soft robotic manipulators while being applicable on a wide range of soft manipulators.

Soft actuators are receiving increasing attention from the engineering community, not only in research but even for industrial applications. Among soft actuators, fibre-reinforced Bending Fluidic Actuators (BFAs) became very popular thanks to features such as robustness and easy design and fabrication. However, an accurate modelling of these smart structures, taking into account all the nonlinearities involved, is a challenging task. In this effort, we propose an analytical mechanical model to capture the quasi-static response of fibre-reinforced BFAs. The model is fully 3D and for the first time includes the effect of the pressure on the lateral surface of the chamber as well as the non-constant torque produced by the pressure at the tip. The presented model can be used for design and control, while providing information about the mechanics of these complex actuators., Comment: 12 pages, 10 figures. Submitted to Smart Materials and Structures