Soft robots, made from compliant materials, exhibit complex dynamics due to their flexibility and high degrees of freedom. Controlling soft robots presents significant challenges, particularly underactuation, where the number of inputs is fewer than the degrees of freedom. This research aims to develop a digital twin for multi-fingered soft grippers to advance the development of underactuation algorithms. The digital twin is designed to capture key effects observed in soft robots, such as nonlinearity, hysteresis, uncertainty, and time-varying phenomena, ensuring it closely replicates the behavior of a real-world soft gripper. Uncertainty is simulated using the Monte Carlo method. With the digital twin, a Q-learning algorithm is preliminarily applied to identify the optimal motion speed that minimizes uncertainty caused by the soft robots. Underactuated motions are successfully simulated within this environment. This digital twin paves the way for advanced machine learning algorithm training.

The design of soft robots is still commonly driven by manual trial-and-error approaches, requiring the manufacturing of multiple physical prototypes, which in the end, is time-consuming and requires significant expertise. To reduce the number of manual interventions in this process, topology optimization can be used to assist the design process. The design is then guided by simulations and numerous prototypes can be tested in simulation rather than being evaluated through laborious experiments. To implement this simulation-driven design process, the possible design space of a slender soft pneumatic actuator is generalized to the design of the circular cross-section. We perform a black-box topology optimization using genetic algorithms to obtain a cross-sectional design of a soft pneumatic actuator that is capable of reaching a target workspace defined by the end-effector positions at different pressure values. This design method is evaluated for three different case studies and target workspaces, which were either randomly generated or specified by the operator of the design assistant. The black-box topology optimization based on genetic algorithms proves to be capable of finding good designs under given plausible target workspaces. We considered a simplified simulation model to verify the efficacy of the employed method. An experimental validation has not yet been performed. It can be concluded that the employed black-box topology optimization can assist in the design process for slender soft pneumatic actuators. It supports at searching for possible design prototypes that reach points specified by corresponding actuation pressures. This helps reduce the trial-and-error driven iterative manual design process and enables the operator to focus on prototypes that already offer a good viable solution.

Soft pneumatic actuators with integrated strain limiting layers have emerged as predominant components in the field of soft gripper technology for several decades. However, owing to their intrinsic strain-limiting layer design, these soft grippers possess a singular gripping functionality, rendering them incapable of adapting to diverse gripping tasks with different strategies. Based on our previous work, we introduce a novel soft gripper that offers variable stiffness, an adjustable gripping range, and multifunctionality. The MASH actuator based soft gripper can expand its gripping range up to threefold compared to the original configuration and ensures secure grip by enhancing stiffness when handling heavy objects. Moreover, it supports multitasking gripping through specific gripping strategy control.

This paper presents a novel approach to underactuated control of multiple soft actuators, specifically focusing on the synchronization of soft fingers within a soft gripper. Utilizing a single syringe pump as the actuation mechanism, we address the challenge of coordinating multiple degrees of freedom of a compliant system. The theoretical framework applies concepts from stable inversion theory, adapting them to the unique dynamics of the underactuated soft gripper. Through meticulous mechatronic system design and controller synthesis, we demonstrate both in simulation and experimentation the efficacy and applicability of our approach in achieving precise and synchronized manipulation tasks. Our findings not only contribute to the advancement of soft robot control but also offer practical insights into the design and control of underactuated systems for real-world applications.

Numerous soft actuators based on PneuNet design have already been proposed and extensively employed across various soft robotics applications in recent years. Despite their widespread use, a common limitation of most existing designs is that their action is pre-determined during the fabrication process, thereby restricting the ability to modify or alter their function during operation. To address this shortcoming, in this article the design of a Reconfigurable, Transformable Soft Pneumatic Actuator (RT-SPA) is proposed. The working principle of the RT-SPA is analogous to the conventional PneuNet. The key distinction between the two lies in the ability of the RT-SPA to undergo controlled transformations, allowing for more versatile bending and twisting motions in various directions. Furthermore, the unique reconfigurable design of the RT-SPA enables the selection of actuation units with different sizes to achieve a diverse range of three-dimensional deformations. This versatility enhances the potential of the RT-SPA for adaptation to a multitude of tasks and environments, setting it apart from traditional PneuNet. The paper begins with a detailed description of the design and fabrication of the RT-SPA. Following this, a series of experiments are conducted to evaluate the performance of the RT-SPA. Finally, the abilities of the RT-SPA for locomotion, gripping, and object manipulation are demonstrated to illustrate the versatility of the RT-SPA across different aspects.

Accurately modeling soft robots remains a challenge due to their inherent nonlinear behavior and parameter variations. This paper presents a novel approach to modeling soft pneumatic actuators using a nonlinear parameter-varying framework. The research begins by introducing Ludwick's Law, providing a more accurate representation of the complex mechanical behavior exhibited by soft materials. Three key material properties, namely Young's modulus, tensile stress, and mixed viscosity, are utilized to estimate the parameter inside the nonlinear model using the least squares method. Subsequently, a nonlinear dynamic model for soft actuators is constructed by applying Ludwick's Law. To validate the accuracy and effectiveness of the proposed method, experimental validations are performed. We perform several experiments, demonstrating the model's capabilities in predicting the dynamical behavior of soft pneumatic actuators. In conclusion, this work contributes to the advancement of soft pneumatic actuator modeling that represents their nonlinear behavior.

Soft pneumatic actuators (SPAs) are widely employed to drive soft robots. However, their inherent flexibility offers both benefits and challenges. This property reduces their output force/torque and makes them hard to control. This paper introduces a new design method that enhances the actuator's performance and controllability. The complex structure of the soft actuator is simplified by approximating it as a cantilever beam. This allows us to derive a mechanical equation between input pressure to output torque. Additionally, a dynamical model is explored to understand the correlation between the natural frequency and dimensional parameters of the SPA. The design problem is then transformed into an optimization problem, using the mechanical equation as the objective function and the dynamical equation as a constraint. By solving this optimization problem, the optimal dimensional parameters are determined. Prior to fabrication, preliminary tests are conducted using the finite element method. Six prototypes are manufactured to validate the proposed approach. The optimal actuator successfully generates the desired force/torque, while its natural frequency remains within the constrained range. This work highlights the potential of using approximated models and optimization formulation to boost the efficiency and dynamic performance of soft pneumatic actuators.

This paper introduces a full system modeling strategy for a syringe pump and soft pneumatic actuators(SPAs). The soft actuator is conceptualized as a beam structure, utilizing a second-order bending model. The equation of natural frequency is derived from Euler's bending theory, while the damping ratio is estimated by fitting step responses of soft pneumatic actuators. Evaluation of model uncertainty underscores the robustness of our modeling methodology. To validate our approach, we deploy it across four prototypes varying in dimensional parameters. Furthermore, a syringe pump is designed to drive the actuator, and a pressure model is proposed to construct a full system model. By employing this full system model, the Linear-Quadratic Regulator (LQR) controller is implemented to control the soft actuator, achieving high-speed responses and high accuracy in both step response and square wave function response tests. Both the modeling method and the LQR controller are thoroughly evaluated through experiments. Lastly, a gripper, consisting of two actuators with a feedback controller, demonstrates stable grasping of delicate objects with a significantly higher success rate.

Soft pneumatic actuators (SPAs) produce motions for soft robots with simple pressure input, however they require to be appropriately designed to fit the target application. Available design methods employ kinematic models and optimization to estimate the actuator response and the optimal design parameters, to achieve a target actuator's shape. Within SPAs, Bellow-SPAs excel in rapid prototyping and large deformation, yet their kinematic models often lack accuracy due to the geometry complexity and the material nonlinearity. Furthermore, existing shape-matching algorithms are not providing an end-to-end solution from the desired shape to the actuator. In addition, despite the availability of computational design pipelines, an accessible and user-friendly toolbox for direct application remains elusive. This paper addresses these challenges, offering an end-to-end shape-matching design framework for bellow-SPAs to streamline the design process, and the open-source toolbox SPADA (Soft Pneumatic Actuator Design frAmework) implementing the framework with a GUI for easy access. It provides a kinematic model grounded on a modular design to improve accuracy, Finite Element Method (FEM) simulations, and piecewise constant curvature (PCC) approximation. An Artificial Neural Network-trained surrogate model, based on FEM simulation data, is trained for fast computation in optimization. A shape-matching algorithm, merging 3D PCC segmentation and a surrogate model-based genetic algorithm, identifies optimal actuator design parameters for desired shapes. The toolbox, implementing the proposed design framework, has proven its end-to-end capability in designing actuators to precisely match 2D shapes with root-mean-square errors of 4.16, 2.70, and 2.51mm, and demonstrating its potential by designing a 3D deformable actuator.

Soft robotics hold promise in the development of safe yet powered assistive wearable devices for infants. Key to this is the development of closed-loop controllers that can help regulate pneumatic pressure in the device's actuators in an effort to induce controlled motion at the user's limbs and be able to track different types of trajectories. This work develops a controller for soft pneumatic actuators aimed to power a pediatric soft wearable robotic device prototype for upper extremity motion assistance. The controller tracks desired trajectories for a system of soft pneumatic actuators supporting two-degree-of-freedom shoulder joint motion on an infant-sized engineered mannequin. The degrees of freedom assisted by the actuators are equivalent to shoulder motion (abduction/adduction and flexion/extension). Embedded inertial measurement unit sensors provide real-time joint feedback. Experimental data from performing reaching tasks using the engineered mannequin are obtained and compared against ground truth to evaluate the performance of the developed controller. Results reveal the proposed controller leads to accurate trajectory tracking performance across a variety of shoulder joint motions.