An all soft, electro-pneumatic controller for soft robots

More than 130,000 people have strokes each year in the United States [1]. Of these victims, 76% are left with disabilities that cost the nation over $54 billion in lost work and medical fees. One prominent disability is upper extremity hemiplegia, which occurs among approximately 50% of stroke sufferers [2]. Robotic technology has the potential to provide an automated platform for controlled rehabilitation and assisted, task-oriented therapy. Several systems have been designed to assist in patient articulation of an impaired hand using rigid robotic components. While these products have been successful in articulating the pure bending motions of the four fingers, the limited capabilities of rigid technologies fail to reproduce the complicated motion path [5] of the thumb during opposition grasp (see Figure 1). This is the most important articulation for normal hand function and specifically for picking up everyday objects. To date, robotic systems for thumb rehabilitation have not been widely investigated [3] apart from some recent work [4] that used a multi-joint rigid robot.

Fluidic soft actuators have been widely used for applications where compliance is desirable – such as in delicate robotic manipulation. As their operation is based on pressurization, fluidic actuators typically rely on bulky, rigid pumps, valves, and pressure regulators for control. This dependence on rigid components hinders the development of compact and fully-soft robots. Soft regulation systems designed for control of these actuators have been recently developed based on soft valves using dielectric elastomer actuators, but precise control has not been achieved. In this work, we leverage these valves to introduce a soft regulation system capable of precise closed-loop position control of a soft hydraulic actuator with multiple controllers. We also achieve open-loop trajectory tracking based on a data-driven model of the fluidic system. Finally, we combine the valve system with wearable strain sensors to create the first teleoperated fluidic circuit where the sensor, actuator, and regulation system are all soft. This work presents control strategies for fluid-driven actuators with soft sensors and regulation systems, showing the potential for future all-soft motion control of soft hydraulic robots.

Soft robots have recently drawn extensive attention thanks to their unique ability of adapting to complicated environments. Soft robots are designed in a variety of shapes of aiming for many different applications. However, accurate modelling and control of soft robots is still an open problem due to the complex robot structure and uncertain interaction with the environment. In fact, there is no unified framework for the modeling and control of generic soft robots. In this paper, we present a novel data-driven machine learning method for modeling a cable-driven soft robot. This machine learning algorithm, named deterministic learning (DL), uses soft robot motion data to train a radial basis function neural network (RBFNN). The soft robot motion dynamics are then guaranteed to be accurately identified, represented, and stored as an RBFNN model with converged constant neural network weights. To validate our method, We have built a simulated soft robot almost identical to our real inchworm soft robot, and we have tested the DL algorithm in simulation. Furthermore, a neural network weight combining technique is used which can extract and combine useful dynamics information from multiple robot motion trajectories.

Robots have been around us for quite some time now. Robots are machines capable of performing tasks on their own with high accuracy and precision and minimal supervision to reduce human effort. Our daily lives are touched by different forms of robots around us, be it a direct interaction in form of robotic helpers at the airport or indirectly in the case of industrial arm robots assembling the electronics and machines. Traditionally, there are several component systems to robotics and one of the prominent one is actuation, which is made from hard and rigid components in these conventional robots. However, this is responsible for their intermittent motion. A new field of research, soft robotics, makes of soft materials for their fabrication and draws inspiration from natural organisms and how they produce fluidic and complex motions. There are different ways by which these soft materials can be actuated and include techniques like applying pneumatic and hydraulic pressure, applying heat, making use of a chemical process like combustion, applying varying magnetic fields and applying electric fields. Among these, the class of actuators employing soft materials deformed by applying the electric field, known as Dielectric elastomer actuators, are preferred owing to their advantages of simple fabrication, large actuation strain and control by electrical stimulus. The architecture of these actuators resembles a variable capacitor with electrodes on either sides of the elastomer, and their performance depends on material properties like dielectric constant and mechanical stiffness. Generally, researchers have tried to modify either of these properties with addition of solid conductive or ceramic fillers, or chemical modification of the elastomer, and this has led to undesirable changes on the other material properties. Drawing inspiration from biological materials like skin, which is a soft material system filled with fluids, a novel composite is synthesized utilizing a solid polymer matrix and a high dielectric constant liquid filler, an ionic liquid. An increase in dielectric constant is observed accompanied with significant reduction in mechanical stiffness. The novel composites show significant improvement upon the actuation performance of the commonly used elastomeric system at considerably low electric fields. Owing to the judicious choice of material systems, the composites also show high stability and transparency. With emergence of new generation technologies such as display touchscreens, touchpads, virtual reality and augmented reality, human machine interaction and haptics has become ever more important. Soft actuators and dielectric elastomer actuators (in particular), owing to their inherent softness, have convincing advantage over the conventional rigid actuators. Additionally, state-of-the-art technologies for haptics primarily seek to simulate the perception of texture change with sensory manipulation. Furthermore, dielectric elastomer actuators in their current form suffer from visible obstructiveness and non-integrable architecture. A new device architecture for dielectric elastomer actuators, capable of producing on-demand surface texture change and local topographic features, is demonstrated. Owing to its unique architecture, a highly transparent, flexible and integrable configuration of the device is showed. Out-of-plane deformations and force feedback from the devices are shown to be well above the perceptual threshold of sensors, known as mechanoreceptors, present in our fingertips. Another attribute of the biological appendages in natural organisms is the ability to reversibly modulate its mechanical properties upon demand, which still eludes the field of soft robotics. One of the best examples of this is found in human body, where muscles modulate their stiffness in accordance to the load distribution and movement. Most of the current approaches mange to only demonstrate reversible stiffness and involves combination of a passive reversible stiffness component with the existing actuator. Hence, a novel low-temperature phase change material is identified for enabling reversible rigidity in the soft actuators owing to its low melting point, significant gap between melting and solidification, superior thermal stability, high mechanical strength and high dielectric constant. Thermal control is chosen the modulation of mechanical properties, owing to its lightweight design and scalability. The fabricated reversible rigidity composites show impressive variations in stretchability, bendability and hardness. Owing to its ability to melt and re-solidify, the composites also demonstrate a healing behaviour with the ability to regain complete structural integrity and mechanical strength. These novel ultra-soft, high-k liquid filler-based polymer composites, reversible rigidity composites and transparent and integrable device architectures for field-driven soft actuators could pave the way for next generation applications such as shape morphing, adaptive surfaces and transparent haptic devices.

The benefits of soft robotic actuation have been recognized and soft actuators are increasingly used. Soft robots are generally lightweight, inexpensive, and highly compliant. They are often used for interaction with small objects which only require relatively low force output. Nevertheless, large-scale applications can still benefit from the advantages of soft actuators if force output is sufficiently large. Some soft actuation has been employed in large-scale applications, but the area remains greatly underexplored. This article presents the Soft Torque-generating Actuation System (STAS) a lean soft actuation architecture capable of producing relatively large torque output while maintaining compliant interaction with the environment. Design and fabrication of the system are described, and a theoretical model is introduced for predicting torque output. The model is validated against measured torque output, and the system is further characterized in terms of power output. The model is shown to adequately predict torque output at low actuator angles. The system is shown to require only 30.0 kPa to achieve a large torque output of 10 N-m. The system also reaches a peak power output of 1.29 Watts. Finally, a versatile application of STAS is shown through a wearable joint-assistive device. The STAS is a promising architecture for a new type of high force actuation using soft actuators.

Conventional robots are constructed with rigid parts and materials, restricting their ability to deform freely and adapt to the external environment. Performing tasks such as handling delicate materials and navigating narrow pathways is challenging for these typical robots made of stiff materials. However, there is a growing demand for such functions in various industries. Recent advances in soft robotics have garnered considerable attention as solutions to these challenges. Soft robotic systems in various forms present a systematic solution for performing unique tasks. The goal of soft robotics research is to develop advanced technologies, incorporating innovative robotic mechanisms based on soft materials and structures to enhance adaptability. Soft robots find broad applications in diverse fields, including industry, healthcare, and field exploration. Soft component technology plays a crucial role in configuring soft robots. Soft actuators introduce new types of motion and operation mechanisms, while soft sensors measure the deformation of the actuators with a tiny stiffness change. These advancements in soft actuators and sensors encourage researchers to explore new robotic designs and applications. Additionally, the component technology enables the integration of soft and typical robots into hybrid B Youngsu Cha

Energy in Robotics: An Interdisciplinary Challenge

This article introduces a model-based robust control framework for electrohydraulic soft robots. The methods presented herein exploit linear system control theory as it applies to a nonlinear soft robotic system. We employ dynamic mode decomposition with control (DMDc) to create appropriate linear models from real-world measurements. We build on the theory by developing linear models in various operational regions of the system to result in a collection of linear plants used in uncertainty analysis. To complement the uncertainty analyses, we utilize ("H Infinity") synthesis techniques to determine an optimal controller to meet performance requirements for the nominal plant. Following this methodology, we demonstrate robust control over a multi-input multi-output (MIMO) hydraulically amplified self-healing electrostatic (HASEL)-actuated system. The simplifications in the proposed framework help address the inherent uncertainties and complexities of compliant robots, providing a flexible approach for real-time control of soft robotic systems in real-world applications.

The development of accurate sensing strategies for soft robotic systems represents an ongoing challenge. In general, it is difficult to integrate a sensory system that preserves the robot's compliance while simultaneously providing accurate measurements. Dielectric elastomer (DE) transducers offer a potential solution to this problem owing to their ability to function as soft actuators and sensors at the same time, thus enabling the realization of self-sensing soft robotic systems. In this work, we propose a bi-modal sensory scheme for a DE-based soft robot, which allows simultaneous estimation of the robot's own configuration and detection of external contacts without relying on any external sensors. Self-sensing proprioception and contact detection are performed at the same time while actuating the robot, based on online processing of electrical signals measured directly at the actuators' level, i.e., DEs' voltage and current. Both estimation tasks are achieved in real time by means of artificial neural networks. After describing the system and the bi-modal self-sensing architecture, the approach is validated experimentally on a DE soft robot.

Soft robots demonstrate superior flexibility and maneuverability than traditional rigid robots in many emerging applications. However, it is challenging to have a general modeling and control methodology to deal with soft body dynamics and its interactions with environment. We present a spline-based modeling and control framework for soft robotic systems. The dynamic model is built on non-uniform rational Bsplines (NURBS) that captures material and physical properties of soft body, while preserving exact geometric dynamics with environmental interactions. Using the NURBS-based dynamic model, the robotic optimal control based on general predictive control is designed through coordination among the finite number of control points. Therefore, the infinite-dimensional motion of soft body can be realized by significantly reduced finite particle motion control. We demonstrate the performance of the modeling and motion control framework using the snakeinspired robot simulations and experiments.

Soft continuum robots are more and more used in various applications due to the multiple advantages offered by the properties of hyper-elastic soft materials, such as resilience, flexibility, etc. Despite all these essential properties that make the soft robots good candidates for some real-life needs (form enclosure grasping, obstacle-free navigation, etc.), controlling their shape remains challenging because of their hyper-redundancies. This letter investigates an inverse dynamics model-based shape control of soft continuum robots in the presence and absence of external efforts. To that end, modeling based on Pythagorean Hodograph (PH) curves with prescribed lengths is combined with Euler-Bernoulli modeling to reconstruct the robot shapes and then calculate the actuator inputs in the case of external efforts. Thus, a relationship between the actuator inputs and the PH control points is obtained. With the above strategy, the actuator inputs can be computed accordingly, and therefore, real-time shape control of the soft robots for various tasks becomes possible. The results of the proposed approach are validated both numerically and experimentally using two classes of soft continuum robots - a Fluidic Elastomeric Actuator (FEA), describing a 2D soft finger robot, with a single and multiple phalanges, and a 3D continuum manipulator, namely the Compact Bionic Handling Assistant (CBHA).

Distinguished from traditional robots made of rigid materials, soft robots have great potential in fields such as human–robot interaction and biomedical applications due to their mechanical pliability. Soft actuators are one of the most important components in soft robotics systems and can be activated by external stimuli such as heat, light, humidity, electric fields, or magnetic fields. Among them, magnetically responsive soft actuators composed of strong magnetic composite can achieve multi‐degree‐of‐freedom wireless remote control and reversible driving action, which have considerable application potential. In this perspective, we first discuss the properties and actuation mechanisms of the strong‐magnetic flexible composites. Then, the preparation method of magnetic actuators by strong‐magnetic flexible composites are briefly introduced. Next, various applications of magnetically responsive soft actuators, such as grasping, walking, swimming, and sensing control, are highlighted. Finally, current opportunities and challenges for stimulus‐responsive soft robots are discussed. This review provides scientists with the latest technological advances and information needed to design and develop the next generation of soft bionic robotic systems, and further demonstrates the future applications of these robots are vast and promising.

Ultrasound imaging procedures are deemed as one of the most convenient and least invasive medical diagnostic imaging modalities and have been widely utilized in health care providers, which are expecting semiautomatic or fully-automatic imaging systems to reduce the current clinical workloads. This paper presents a portable and wearable soft robotic system which has been designed with the purpose of replacing the manual operation to cooperatively steer the ultrasound probe. This human-compliant soft robotic system, which is equipped with four separated parallel soft pneumatic actuators and is able to achieve movements in three directions. Vacuum suction force is introduced to attach the robot onto the intended body location. The design and fabrication of this soft robotic system are illustrated. To our knowledge, this is the first body-attached soft robot for compliant ultrasound imaging. The feasibility of the system is demonstrated through proof-of-concept experiments.

The fabrication of soft hydrogel actuators that rapidly respond to multiple stimuli in ambient environments is a challenge. This article describes a method—based on surface molding—to generate multi-stimuli-responsive soft actuators consisting of arrays of geometrically patterned hydrogels on elastomeric supports. The stimuli-responsive properties of these actuators were derived from functional fillers that were added directly to the prepolymer or synthesized within the gels using simple reactions. These actuators respond rapidly and can be rationally controlled by a variety of stimuli, including humidity, magnetic fields, or light, depending on the filler type. The reported fabrication strategies will be useful to the design and production of “programmable” soft actuator systems useful to, for example, soft robotics.

Soft actuators have received extensive attention in the fields of soft robotics, biomedicine, and intelligence systems owing to their advantages of pliancy, silence, and essential safety. However, most existing soft actuators have only single actuation elements and lack sensing. Therefore, it is difficult for them to perform complex motions with multiple degrees of freedom (multi-DOFs) and high precision. This article reports a miniature columnar dielectric elastomer actuator (DEA) with multi-DOF actuation and sensing, which was fabricated with an electroactive polymer acrylic film (Very High Bond [VHB] acrylic film by 3M Company) and carbon black grease electrodes. The arrangement of the simulation electrodes on the VHB was optimized to realize multi-DOF actuation, and the sensing electrodes were configured on the outer part of the DEA to realize real-time sensing. The results showed that the soft actuator can achieve all-round actuation through the selective power of the stimulation electrodes with a controllable voltage. The maximum bending angle and axial strain of the actuator reached 50° and 13%, respectively. Moreover, the deformation modes, direction, and quantity could be precisely measured using the integrative sensing function. In addition, to demonstrate the advantages of the proposed actuator, a manipulator with multiple actuators was designed and controlled to realize different actions of screwing and grasping with sensing. This research is useful not only for the design of multifunctional soft actuators but also for the development of soft robots with flexible, complex, and precisely controllable motions.