Abstract:Fabricating robots from soft materials imposes major constraints on the integration and compatibility of embedded sensing, transmission, and actuation systems. Various soft materials present different challenges, but also new opportunities, for novel fabrication techniques, integrated soft sensors, and embedded actuators. For instance, extensive research on silicone elastomers has led to the development of soft sensors based on closed channels filled with liquid metal conductors, as well as corresponding fluid… Show more
“…While we choose a markerbased approach due to its ease of use and availability, the theory presented so far would equally apply to motion data reconstructed from any other capture system, as long as an objective function on the simulation state can be written in terms of the collected data. One interesting material with recent applications in soft robotics [Somm et al 2019] is flexible polyurethane foam. In general this material is relatively cheap and easy to work with.…”
Section: Methods and Details 41 Experimental Methodsmentioning
confidence: 99%
“…Finally, we show an application of our method to soft robotics design in Figure 1. We first fabricate a soft robot prototype from type-III foam using the technique described by [Somm et al 2019]. Then we capture short motion samples for this particular specimen and estimate material parameters for homogeneous Neohookean elasticity and Newtonian viscosity for a reasonably coarse mesh.…”
This paper presents a method for optimizing visco-elastic material parameters of a finite element simulation to best approximate the dynamic motion of real-world soft objects. We compute the gradient with respect to the material parameters of a least-squares error objective function using either direct sensitivity analysis or an adjoint state method. We then optimize the material parameters such that the simulated motion matches real-world observations as closely as possible. In this way, we can directly build a useful simulation model that captures the visco-elastic behaviour of the specimen of interest. We demonstrate the effectiveness of our method on various examples such as numerical coarsening, custom-designed objective functions, and of course real-world flexible elastic objects made of foam or 3D printed lattice structures, including a demo application in soft robotics.
“…While we choose a markerbased approach due to its ease of use and availability, the theory presented so far would equally apply to motion data reconstructed from any other capture system, as long as an objective function on the simulation state can be written in terms of the collected data. One interesting material with recent applications in soft robotics [Somm et al 2019] is flexible polyurethane foam. In general this material is relatively cheap and easy to work with.…”
Section: Methods and Details 41 Experimental Methodsmentioning
confidence: 99%
“…Finally, we show an application of our method to soft robotics design in Figure 1. We first fabricate a soft robot prototype from type-III foam using the technique described by [Somm et al 2019]. Then we capture short motion samples for this particular specimen and estimate material parameters for homogeneous Neohookean elasticity and Newtonian viscosity for a reasonably coarse mesh.…”
This paper presents a method for optimizing visco-elastic material parameters of a finite element simulation to best approximate the dynamic motion of real-world soft objects. We compute the gradient with respect to the material parameters of a least-squares error objective function using either direct sensitivity analysis or an adjoint state method. We then optimize the material parameters such that the simulated motion matches real-world observations as closely as possible. In this way, we can directly build a useful simulation model that captures the visco-elastic behaviour of the specimen of interest. We demonstrate the effectiveness of our method on various examples such as numerical coarsening, custom-designed objective functions, and of course real-world flexible elastic objects made of foam or 3D printed lattice structures, including a demo application in soft robotics.
“…Starfish is fabricated by creating an inverse mould into which silicone foam (SomaFoam 25, SmoothOn) is cast. Silicone foam, a material widely used for soft robotic fabrication [32], has been chosen as it allows for rapid fabrication, shows elastic properties, and has natural buoyancy. The "muscle fibers" or tendons can then be routed into the soft structure along the bottom of each of the legs of Starfish.…”
Underwater soft robots are challenging to model and control because of their high degrees of freedom and their intricate coupling with water. In this paper, we present a method that leverages the recent development in differentiable simulation coupled with a differentiable, analytical hydrodynamic model to assist with the modeling and control of an underwater soft robot. We apply this method to Starfish, a customized soft robot design that is easy to fabricate and intuitive to manipulate. Our method starts with data obtained from the real robot and alternates between simulation and experiments. Specifically, the simulation step uses gradients from a differentiable simulator to run system identification and trajectory optimization, and the experiment step executes the optimized trajectory on the robot to collect new data to be fed into simulation. Our demonstration on Starfish shows that proper usage of gradients from a differentiable simulator not only narrows down its simulation-to-reality gap but also improves the performance of an open-loop controller in real experiments.
“…There is also a control system known as a Proportional-Integral-Derivative (PID) controller, which is a control loop mechanism that utilizes feedback to keep a constant variable. Due to the fact that the PID controller only impacts one variable other control methods have been tested [59] A PID can be part of the control system, but there needs to be more hardware that effectively communicates to the servo motors or solenoids that activate the open or closed loops [60,61].…”
This paper focuses on the recent development of soft pneumatic actuators for soft robotics over the past few years, concentrating on the following four categories: control systems, material and construction, modeling, and sensors. This review work seeks to provide an accelerated entrance to new researchers in the field to encourage research and innovation. Advances in methods to accurately model soft robotic actuators have been researched, optimizing and making numerous soft robotic designs applicable to medical, manufacturing, and electronics applications. Multi-material 3D printed and fiber optic soft pneumatic actuators have been developed, which will allow for more accurate positioning and tactile feedback for soft robotic systems. Also, a variety of research teams have made improvements to soft robot control systems to utilize soft pneumatic actuators to allow for operations to move more effectively. This review work provides an accessible repository of recent information and comparisons between similar works. Future issues facing soft robotic actuators include portable and flexible power supplies, circuit boards, and drive components.
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