A Material-Based Model for the Simulation and Control of Soft Robot Actuator (original) (raw)
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Modeling, Design, and Development of Soft Pneumatic Actuators with Finite Element Method
This work presents a comprehensive open-source simulation and design tool for Soft pneumatic actuators (SPAs) using finite element method, compatible and extensible to a diverse range of soft materials and design parameters. Thorough characterization of the hyperelastic and viscoelastic behavior is illustrated using a sample soft material (Ecoflex 00_30), and an appropriate material constitutive law. SPA performance (displacement and blocked-force) are simulated for two types of SPA and validated with experimental testing. Real-world case studies are presented in which SPA designs are iteratively optimized through simulation to meet specified performance criteria and geometric constraints.
Modelling and Simulation of Pneumatic Sources for Soft Robotic Applications
2020 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), 2020
The mathematical models for two widely used pneumatic systems in the soft robotics community are presented: syringe pumps and compressed air systems. These models enable prediction and optimisation of performance of soft actuators under pressurisation, allowing the user to select pneumatic components for a desired behaviour. Analytical models are confirmed with simulations developed using SimScape Fluids and SimScape Electrical within Simulink/MATLAB. By using a polytropic law, the models show agreement with the simulations with less than 10% discrepancy for the typical pressures used with soft actuators. Syringe pumps are shown to be much slower compared to the compressed air systems. In the latter, the addition of an air receiver allows very short actuation time.
Design and Control of Pneumatic Systems for Soft Robotics: A Simulation Approach
IEEE Robotics and Automation Letters, 2021
Pressure control plays a major role in the overall performance of fluid-driven soft robots. Due to the increasing demand for higher speed actuation and precision, a need exists for a practical design methodology that converts actuator performance specifications to a set of minimum pneumatic specifications, such as receiver volume and pressure, and valve conductance. This article presents a systematic parameter selection approach for pneumatic soft robotic systems by taking into consideration the desired closed-loop pressure responses. The two controllers under evaluation here are the PI controller with anti-windup and the on-off controller with hysteresis. Simulations are developed within Simscape Fluids to evaluate the effect of physical components and controller parameters on the actuator performance. The proposed parameter selection procedures are then applied on three soft actuators and their closed-loop pressure responses are experimentally evaluated. The measured pressure responses are in close agreement with the simulations and satisfy the rise time specifications.
Fluid-Structure Interaction Modelling of a Soft Pneumatic Actuator
Actuators
This paper presents a fully coupled fluid-structure interaction (FSI) simulation model of a soft pneumatic actuator (SPA). Previous research on modelling and simulation of SPAs mostly involves finite element modelling (FEM), in which the fluid pressure is considered as pressure load uniformly acting on the internal walls of the actuator. However, FEM modelling does not capture the physics of the fluid flow inside an SPA. An accurate modelling of the physical behaviour of an SPA requires a two-way FSI analysis that captures and transfers information from fluid to solid and vice versa. Furthermore, the investigation of the fluid flow inside the flow channels and chambers of the actuator are vital for an understanding of the fluid energy distribution and the prediction of the actuator performance. The FSI modelling is implemented on a typical SPA and the flow behaviour inside the actuator is presented. Moreover, the bending behaviour of the SPA from the FSI simulation results is compar...
Finite Element Modeling of Soft Fluidic Actuators: Overview and Recent Developments
Advanced Intelligent Systems, 2020
Soft robotics has experienced an exponential growth in publications in the last two decades. [1] Unlike rigid conventional manipulators, [2,3] soft robots based on hydrogels, [4,5] electroactive polymers, [6,7] and elastomers [7-9] are physically resilient and can adapt to delicate objects and environments due to their conformal deformation. [10,11] They also show increased safety and dexterity can be lightweight and used within constrained environments with restricted access. [12,13] Many soft robots have a biologically inspired design coming from snakes, [14-17] worms, [18-20] fishes, [21-24] manta rays, [25,26] and tentacles. [27-29] The scope of applications includes minimally invasive surgery, [30,31] rehabilitation, [32,33] elderly assistance, [34] safe human-robot interaction, [35,36] and handling of fragile materials. [37,38] Important features of soft robotics design, fabrication, modeling, and control are covered in the soft robotics toolkit. [39,40] The building blocks of soft robots are the soft actuators. The most popular category of soft actuator is the soft fluidic actuator (SFA), where actuation is achieved using hydraulics or pneumatics. [8,41] These actuators are usually fabricated with silicone rubbers following a 3D molding process, [42] although directly 3D printing the soft actuators is also possible. [43,44] Silicone rubber is a highly flexible/extensible elastomer with high-temperature resistance, lowtemperature flexibility, and good biocompatibility. [45] Elastomers can withstand very large strains over 500% with no permanent deformation or fracture. [46] For relatively small strains, simple linear stress-strain relationships can be used, and two of the following parameters can be used to describe the elastic properties: bulk compressibility, shear modulus, tensile modulus (Young's modulus of elasticity), or Poisson's ratio. [45] For large deformations, nonlinear solid mechanic models using hyperelasticity should be considered. [8,32,47-50] Due to the strong nonlinearities in SFAs and their complex geometries, analytical modeling is challenging. [51] A brief review of the analytical methods for modeling of soft robotic structures is provided in the following. 1.1. Analytical Modeling of Soft Actuators The majority of soft/continuum robots with bending motion can be approximated as a series of mutually tangent constant curvature sections, i.e., piecewise constant curvature. [52] This is a result of the fact that the internal potential energy is uniformly distributed along each section for pressure-driven robots. [53] This approach has also been validated using Hamilton's principle by Gravagne et al. [54] As discussed by Webster and Jones, [52] the kinematics of continuum robots can be separated into robotspecific and robot-independent components in this approach. The robot specific mapping transforms the input pressures P or actuator space q to the configuration space κ, ϕ, l, and the robot-independent mapping goes from the configuration space to the task space x. The actuator space contains the length of tubes or bellows. The configuration space consists of the curvature κ, the angle of the plane containing the arc ϕ (also called
Soft fluidic rotary actuator with improved actuation properties
2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2017
The constantly increasing amount of machines operating in the vicinity of humans makes it necessary to rethink the design approach for such machines to ensure that they are safe when interacting with humans. Traditional mechanisms are rigid and heavy and as such considered unsuitable, even dangerous when a controlled physical contact with humans is desired. A huge improvement in terms of safe human-robot interaction has been achieved by a radically new approach to robotics-soft material robotics. These new robots are made of compliant materials that render them safe when compared to the conventional rigid-link robots. This undeniable advantage of compliance and softness is paired with a number of drawbacks. One of them is that a complex and sophisticated controller is required to move a soft robot into the desired positions or along a desired trajectory, especially with external forces being present. In this paper we propose an improved soft fluidic rotary actuator composed of silicone rubber and fiber-based reinforcement. The actuator is cheap and easily manufactured providing near linear actuation properties when compared to pneumatic actuators presented elsewhere. The paper presents the actuator design, manufacturing process and a mathematical model of the actuator behavior as well as an experimental validation of the model. Four different actuator types are compared including a square-shaped and three differently reinforced cylindrical actuators.
A Control and Drive System for Pneumatic Soft Robots: PneuSoRD
2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2021
This article describes an open-source hardware platform for controlling pneumatic soft robotic systems and presents the comparison of control schemes with on-off and proportional valves. The Pneumatic Soft Robotics Driver (PneuSoRD) can be used with up to one pump and pressure accumulator, 26 on-off valves, and 5 proportional valves, any of which can be operated in open or closed-loop control using up to 12 sensor inputs, which allows for the simultaneous control of a large number of soft actuators. The electronic driver connects to a National Instruments myRIO controller or an Arduino Due with the use of an adapter shield. A library of pressure control algorithms in both LabVIEW and Simulink is provided that includes bang-bang control, hysteresis control and PID control using on-off or proportional valves. Lab-VIEW and Simulink provide user-friendly interfaces for rapid prototyping of control algorithms and real-time evaluation of pressure dynamics. The characteristics and performance of these control methods and pneumatic setups are evaluated to simplify the choice of valves and control algorithm for a given application.
Hyperelastic Modeling and Validation of Hybrid-Actuated Soft Robot with Pressure-Stiffening
Micromachines
Soft robots have gained popularity, especially in intraluminal applications, because their soft bodies make them safer for surgical interventions than flexures with rigid backbones. This study investigates a pressure-regulating stiffness tendon-driven soft robot and provides a continuum mechanics model for it towards using that in adaptive stiffness applications. To this end, first, a central single-chamber pneumatic and tri-tendon-driven soft robot was designed and fabricated. Afterward, the classic Cosserat’s rod model was adopted and augmented with the hyperelastic material model. The model was then formulated as a boundary-value problem and was solved using the shooting method. To identify the pressure-stiffening effect, a parameter-identification problem was formulated to identify the relationship between the flexural rigidity of the soft robot and internal pressure. The flexural rigidity of the robot at various pressures was optimized to match theoretical deformation and exper...
IEEE Access
Soft robotics is a rapidly evolving field where robots are fabricated using highly deformable materials and usually follow a bioinspired design. Their high dexterity and safety make them ideal for applications such as gripping, locomotion, and biomedical devices, where the environment is highly dynamic and sensitive to physical interaction. Pneumatic actuation remains the dominant technology in soft robotics due to its low cost and mass, fast response time, and easy implementation. Given the significant number of publications in soft robotics over recent years, newcomers and even established researchers may have difficulty assessing the state of the art. To address this issue, this article summarizes the development of soft pneumatic actuators and robots up until the The scope of this article includes the design, modeling, fabrication, actuation, characterization, sensing, control, and applications of soft robotic devices. In addition to a historical overview, there is a special emphasis on recent advances such as novel designs, differential simulators, analytical and numerical modeling methods, topology optimization, data-driven modeling and control methods, hardware control boards, and nonlinear estimation and control techniques. Finally, the capabilities and limitations of soft pneumatic actuators and robots are discussed and directions for future research are identified. INDEX TERMS Soft robotics, soft pneumatic actuator, design, modeling, sensing, control. MATHEUS S. XAVIER (Graduate Student Member, IEEE) received the B.S. degree in science and technology and the B.Eng. degree in control and automation engineering from the Federal
An active compliant control mode for interaction with a pneumatic soft robot
Bionic soft robots offer exciting perspectives for more flexible and safe physical interaction with the world and humans. Unfortunately, their hardware design often prevents analytical modeling, which in turn is a prerequisite to apply classical automatic control approaches. On the other hand, also modeling by means of learning is hardly feasible due to many degrees of freedom, high-dimensional state spaces and the softness properties like e.g. mechanical elasticity, which cause limited repeatability and complex dynamics. Nevertheless, the realization of basic control modes is important to leverage the potential of soft robots for applications. We therefore propose a hybrid approach combining classical and learning elements for the realization of an interactive control mode for an elastic bionic robot. It superimposes a low-gain feedback control with a feed-forward control based on a learned simplified model of the inverse dynamics which considers only equilibria of the robot's dynamics. We demonstrate on the Bionic Handling Assistant how a respective inverse equilibrium model can be learned and effectively exploited for quick and agile control. In a second step, the control scheme is extended to an active compliant control mode. It implements a kind of gravitation compensation to allow for kinesthetic teaching of the robot based on the implicit knowledge of gravitational and mechanical forces that are encoded in the learned equilibrium model. We finally discuss that this control scheme may be implemented also on other soft robots to provide the avenue towards their applications in general manipulation tasks.