Soft robots have desirable qualities for use in underwater environments thanks to their inherent compliance and lack of need for exposed hardware. Nevertheless, these advantages come at the cost of considerable control challenges. Data-driven model predictive control (MPC) is an approach that has shown promise in controlling soft robots. However, manually tuning the many hyperparameters in the learned dynamics model and the optimizer can be extremely tedious. In this work, we explore using data-driven MPC to control an underwater soft robot, and employ the AutoMPC method to automatically tune the hyperparameters and generate the controller. In the process, we extend AutoMPC's capabilities to handle multi-task tuning and we add a barrier cost function to enforce actuator constraints. Our experiments show that the AutoMPC controller reaches targets with significantly higher accuracy and reliability than state-of-the-art baselines both in- and out-of-distribution of the training data. 

Automatically-Tuned Model Predictive Control for an Underwater Soft Robot

Soft robots grew to prominence in large part because they promised a new and exciting means for engineers to develop robots that are both highly adaptable and safe for direct human interaction. However, despite showing substantial promise in this area, soft robots have not yet seen widespread adoption. Two major factors that have prevented the development of soft robots are the fundamental challenges of both the modeling and control of soft structures. While traditional robots enjoy a myriad of theoretical and computational tools for modeling and control, the options for soft robots are far more limited. In this work, we introduce a physics-based finite element simulation platform, Kraken, that can be used to accurately model the dynamic and oscillatory motions of soft robots. After a brief theoretical introduction to hyperelastic modeling and the finite element method, we show the utility of our approach by simulating the oscillations of a 1D hyperelastic actuator, the dynamics of a 2D hyperelastic pendulum, and a 3D spherical hyperelastic pendulum. We then demonstrate the accuracy of our approach by presenting the agreement of the simulated results with those obtained via physical experiments for three materials with different hyperelastic properties, with percent errors as low as 1%. Taken together, these results demonstrate that the aforementioned simulation platform is a critical step towards the fast and accurate simulation, prototyping, and control of soft robots.

Physics-Based Finite Element Simulation of the Dynamics of Soft Robots

AutoMPC is a Python package that automates and optimizes data-driven model predictive control. However, it can be computationally expensive and unstable when exploring large search spaces using pure Bayesian Optimization (BO). To address these issues, this paper proposes to employ a meta-learning approach called Portfolio that improves AutoMPC's efficiency and stability by warmstarting BO. Portfolio optimizes initial designs for BO using a diverse set of configurations from previous tasks and stabilizes the tuning process by fixing initial configurations instead of selecting them randomly. Experimental results demonstrate that Portfolio outperforms the pure BO in finding desirable solutions for AutoMPC within limited computational resources on 11 nonlinear control simulation benchmarks and 1 physical underwater soft robot dataset.

Efficient Automatic Tuning for Data-driven Model Predictive Control via Meta-Learning

Soft-robot designs are manifold, but only a few are publicly available. Often, these are only briefly described in their publications. This complicates reproduction, and hinders the reproducibility and comparability of research results. If the designs were uniform and open source, validating researched methods on real benchmark systems would be possible. To address this, we present two variants of a soft pneumatic robot with antagonistic bellows as open source. Starting from a semi-modular design with multiple cables and tubes routed through the robot body, the transition to a fully modular robot with integrated microvalves and serial communication is highlighted. Modularity in terms of stackability, actuation, and communication is achieved, which is the crucial requirement for building soft robots with many degrees of freedom and high dexterity for real-world tasks. Both systems are compared regarding their respective advantages and disadvantages. The robots' functionality is demonstrated in experiments on airtightness, gravitational influence, position control with mean tracking errors of<3 deg, and long-term operation of cast and printed bellows. All soft- and hardware files required for reproduction are provided.

SPONGE: Open-Source Designs of Modular Articulated Soft Robots

Seaweeds are large algae that grow on rocky shorelines, in shallow coastal waters, and in marine environments. It plays a major role in marine ecosystems and is abundantly available in the Gulf of Mannar Biosphere region. However, some species of seaweed clash with coral reefs and damage them severely due to the release of hydrophobic allelochemicals. Conventional methods are often labor-intensive, which results in adverse environmental effects. The solution to this problem is to involve underwater vehicles for seaweed farming. But the existing underwater vehicles need some soft materials to handle the targeted seaweeds carefully, without affecting the coral reefs and other species in the sea.This study aims to develop a soft robotic gripper with pneumatic to grasp seaweed, which is abundantly available under the sea water nearer to coral reefs. The novel soft pneumatic robotic gripper design has been done for 8 chambers using Ansys Finite Element Analysis software. Gripper deformation analysis is carried out for different pressure levels to understand the behavior of the hyperelastic material.

FEM Based Soft Robotic Gripper Design For Seaweed Farming

Many soft-body organisms found in nature flourish underwater. Similarly, soft robots are potentially well-suited for underwater environments partly because the problematic effects of gravity, friction, and harmonic oscillations are less severe underwater. However, it remains a challenge to design, fabricate, waterproof, model, and control underwater soft robotic systems. Furthermore, submersible robots usually do not have configurable components because of the need for sealed electronics and mechanical elements. This work presents the development of a modular and submersible soft robotic arm driven by hydraulic actuators which consists of mostly 3D printable parts and can be assembled or modified in a relatively short amount of time. Its modular design enables multiple shape configurations and easy swapping of soft actuators. As a first step to exploring machine learning control algorithms on this system, we also present preliminary forward and inverse kinematics models developed using deep neural networks.

Development of a Modular and Submersible Soft Robotic Arm and Corresponding Learned Kinematics Models

Soft robots offer an exciting and novel alternative to traditional robots composed of rigid bodies. Many of the primary benefits soft robots have over more traditional robots result from their inherent compliance and their potential for low force interactions with their environments. Therefore, modeling soft robots requires the ability to accurately simulate contact mechanics. In this work, we present the solution of contact mechanics finite element problems specifically for soft robots in a MOOSE-based multiphysics simulation platform we developed, Kraken. The primary contributions of this work are threefold. Firstly, our implementations enable the modeling of additional types of contact critical to the simulation of soft robots. Next, we demonstrate how our new self contact method can be used to dramatically decrease the computational cost of contact modeling. Finally, we demonstrate the abilities of Kraken as a platform to simulate the complex interactions of soft robots and the environment.

An Efficient Framework for the Solution of Contact Mechanics Problems in Soft Robotics

Shlyun N.V., Zaiets Yu. O. About the internal mechanism of thermal damage in reinforced composites with thermomechanical incompatibility of their phases. Visnyk National Transport University. Series

/pdf/about-the-internal-mechanism-of-thermal-damage-in-reinforced-1fowiqib.pdf

About the internal mechanism of thermal damage in reinforced composites with thermomechanical incompatibility of their phases

The paper considers the problem of the transformation of the fronts of weak shock waves in inhomogeneous elastic media with an almost layered structure. The research object is nonstationary wave with strong discontinuity generated in in inhomogeneous transversely isotropic elastic media The aim of this work is to study the effects of bifurcations of discontinuous waves, in particular, on the tasks of propagation in inhomogeneous rocks of seismic waves provoked by explosions, as well as mountain shocks. The research method is the zeroth approximation of the ray method. The paper considers the issue of restructuring of ray systems and fronts of weak shock waves of elastic media. The heterogeneity of which is caused by the variable characteristics of the medium for the regularity of a continuous function, selected in such a way as to ensure that the medium has acquired the properties of a layered structure, but the properties are described by continuous differentiated functions. The work results can be inculcated in research and projection institutes for design of construction of structures, transport and mine destination with the aim of their seismic protection and their screening from action of discontinuous waves generated by different technical failures. KEYWORDS: SHIELDING, DISCONTINUOUS WAVES, ANISOTROPIC LAYERED MEDIA, ELASTIC MEDIUM, RAY METHOD.

/pdf/bifurcation-of-discontinuous-wave-fronts-in-elastic-layered-1bkkux9y.pdf

Bifurcation of discontinuous wave fronts in elastic layered media

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Papers

Development of a Modular and Submersible Soft Robotic Arm and Corresponding Learned Kinematics Models

An Efficient Framework for the Solution of Contact Mechanics Problems in Soft Robotics

About the internal mechanism of thermal damage in reinforced composites with thermomechanical incompatibility of their phases

Bifurcation of discontinuous wave fronts in elastic layered media