Soft Robotic Arm Research Articles

A Soft Mimic Robotic Arm Powered by Dielectric Elastomer Actuator

AbstractGiven the great importance of human‐robot collaboration (HRC), the development of robotic arms tends to prioritize high flexibility, safety as well as low energy consumption. Dielectric elastomer actuators (DEAs), with their high energy density and lightweight characteristics, present a promising alternative for the design of soft robotic arms suitable for HRC setting. Hence, a soft mimic robotic arm powered by dual cone DEA (DCDEA) driver unit is developed that demonstrates high energy density and low energy consumption. The resultant elaborate DCDEA driver unit exhibits a significant bi‐direction actuation stroke with a high load‐to‐weight ratio up to 1,300, ensuring sufficient mechanical power output for powering the robotic arm. The well‐designed efficient transmission mechanism and control system together with DCDEA allow the robotic arm to conduct lifting and twisting action, mimicking the movements of a human arm. During the transportation of a 10 g weight, the robotic arm consumes only ≈35 mJ of energy, while achieving an energy density of 42.1 J kg−1. Encased in soft outer shells, the robotic arm displays full flexibility, high safety, and excellent impact resistance, underscoring its potential as a next‐generation soft robotic arm for HRC setting.

Silver oxide integrated ionic polymer composite for wearable sensing and water purification

Integrating metal nanoparticles (NPs) with ionic polymer blends/composites showed immense interest for their potential in wearable sensors, soft robotic arms, flexible man-machine interfaces of biomedical devices, and water purification applications. However, conventional NPs attached ionic polymer composites exhibit limitations such as low sensitivity (ΔR/R), low total dissolved solids (TDS) reduction, and low phosphate (PO4-P) removal rate. Herein, ionic polymer composites (IPCs) using flower-shaped silver oxide (Ag2O) attached Poly (vinylidene fluoride) (PVDF)/ polyvinylpyrrolidone (PVP)/ionic liquid (IL) were designed and developed for wearable sensing and water purification. The IPCs demonstrated remarkably high ΔR/R values of 50, 10, and 3.5 corresponding to the wrist movement of 50°, finger movement of 180°, and chin movements respectively. The Ag2O-based IPC recorded a significant reduction of TDS from sewage water from 3405 ppm to 1035 ppm, elevated the dissolved oxygen (DO) levels in the sewage water from 1.2 mg/l to 6.8 mg/l, and removed approximately 87.38 % phosphate from sewage water. Due to the uniform distribution of Ag2O within pores of IPC, it demonstrated enhanced performance for wearable and wastewater treatment applications.

A Koopman-based residual modeling approach for the control of a soft robot arm

Soft robots are challenging to model and control due to their poorly defined kinematics and nonlinear dynamics. Recently, Koopman operator theory has been shown capable of constructing control-oriented soft robot models from data. However, building these models requires extensive data collection and they do not necessarily generalize well outside of the training observations. This paper presents a more data-efficient and generalizable approach to soft robot modeling that first identifies a physics-based Koopman model then supplements it with a data-driven residual Koopman model. The resulting combined model is linear and thus compatible with real-time model-based control techniques such as Model Predictive Control (MPC). The efficacy of the approach is demonstrated on several simulated systems and on a real soft robot arm, where it is shown to generate models that are more accurate than purely physics-based models and require less data to construct than purely data-driven models. Using a model-based controller, the soft arm is able to successfully track end effector trajectories, perform a pick-and-place task, and write on a dry-erase board, showcasing the applicability of this framework to increase the capabilities of soft robotic systems.

Using a Guidance Virtual Fixture on a Soft Robot to Improve Ureteroscopy Procedures in a Phantom

Manipulating a flexible ureteroscope is difficult, due to its bendable body and hand–eye coordination problems, especially when exploring the lower pole of the kidney. Though robotic interventions have been adopted in various clinical scenarios, they are rarely used in ureteroscopy. This study proposes a teleoperation system consists of a soft robotic endoscope together with a Guidance Virtual Fixture (GVF) to help users explore the kidney’s lower pole. The soft robotic arm was a cable-driven, 3D-printed design with a helicoid structure. GVF was dynamically constructed using video streams from an endoscopic camera. With a haptic controller, GVF can provide haptic feedback to guide the users in following a trajectory. In the user study, participants were asked to follow trajectories when the soft robotic arm was in a retroflex posture. The results suggest that the GVF can reduce errors in the trajectory tracking tasks when the users receive the proper training and gain more experience. Based on the NASA Task Load Index questionnaires, most participants preferred having the GVF when manipulating the robotic arm. In conclusion, the results demonstrate the benefits and potential of using a robotic arm with a GVF. More research is needed to investigate the effectiveness of the GVFs and the robotic endoscope in ureteroscopic procedures.

Design and motion analysis of soft robotic arm with pneumatic-network structure

Design and motion analysis of soft robotic arm with pneumatic-network structure

Augmented Bodily Self in Performing a Button-Touching Task with Soft Supernumerary Robotic Arms

Extra or supernumerary robotic limbs are actively exploited in the field of body augmentation. The science of self-recognition of additional body parts is an interesting subject. Although the field is primarily led by psychological studies in virtual reality, which facilitate flexible experimental designs, we believe that assessments employing real robots are also essential. In this study, we investigated the sense of body ownership and agency of a dual-arm wearable robotic arm using an inexpensive and safe inflatable structure. We report the results of functional near-infrared spectroscopy (fNIRS) analysis of brain activity during the use of the robotic arm. The questionnaire results from the experiment, which involved a button-touching task, revealed that both the sense of ownership and sense of agency were significantly higher in the goal-oriented collaborative experience compared to the non-goal-oriented condition. This indicates that humans feel ownership of and agency toward an autonomous robot or a remote-controlled robotic arm operated by another person. The analysis of the fNIRS data included a two-factor analysis of variance for the learning and trial phases. While there was no main effect for the conditions within each phase, a significant interaction was observed between the two brain regions of the right angular gyrus and right postcentral gyrus.

Soft Robots: Computational Design, Fabrication, and Position Control of a Novel 3-DOF Soft Robot

This paper presents the computational design, fabrication, and control of a novel 3-degrees-of-freedom (DOF) soft parallel robot. The design is inspired by a delta robot structure. It is engineered to overcome the limitations of traditional soft serial robot arms, which are typically low in structural stiffness and blocking force. Soft robotic systems are becoming increasingly popular due to their inherent compliance match to that of human body, making them an efficient solution for applications requiring direct contact with humans. The proposed soft robot consists of three soft closed-loop kinematic chains, each of which includes a soft actuator and a compliant four-bar arm. The complex nonlinear dynamics of the soft robot are numerically modeled, and the model is validated experimentally using a 6-DOF electromagnetic position sensor. This research contributes to the growing body of literature in the field of soft robotics, providing insights into the computational design, fabrication, and control of soft parallel robots for use in a variety of complex applications.

Design and Nonlinear Modeling of a Modular Cable-Driven Soft Robotic Arm

Design and Nonlinear Modeling of a Modular Cable-Driven Soft Robotic Arm

Design, modeling, and experiment of a multi-degree-of-freedom pneumatic soft bionic actuator

Drawing on the driving mechanism of biological muscles and combining the nonlinear hyperelastic characteristics of silicone rubber, a multi-degree-of-freedom pneumatic soft bionic actuator is designed, which can be used as the executing mechanism for soft robots and robotic arms. Using response surface analysis and numerical simulation algorithms, the optimal combination of structural dimensions parameters is determined with the maximum bending and torsion angles output by the actuator as the optimization objectives. Based on the idea of flexible mechanism Piecewise Constant Curvature (PCC) modeling, the kinematics equation of the actuator is derived. Analyze the equivalent motion structure of the actuator and establish a dynamic model of the actuator based on the Lagrange method. The physical model of the actuator is manufactured using rapid prototyping technology. Finally, experimental testing and analysis are conducted on the physical model to obtain the motion and dynamic characteristics curves and empirical formulas of the actuator, which are compared with theoretical and simulation results to verify that the pneumatic soft bionic actuator is feasible and effective. The above research methods, processes, and results can provide reference and inspiration for the research and implementation of pneumatic and hydraulic soft robots and gripping actuators for soft robotic arms.

Pushing with Soft Robotic Arms via Deep Reinforcement Learning

Soft robots can adaptively interact with unstructured environments. However, nonlinear soft material properties challenge modeling and control. Learning‐based controllers that leverage efficient mechanical models are promising for solving complex interaction tasks. This article develops a closed‐loop pose/force controller for a dexterous soft manipulator enabling dynamic pushing tasks using deep reinforcement learning. Force tests investigate the mechanical properties of a soft robot module, resulting in orthogonal forces of N. Then, the policy is trained in simulation leveraging a dynamic Cosserat rod model of the soft robot. Domain randomization mitigate the sim‐to‐real gap while careful reward engineering induced pose and force control even without explicit force inputs. Despite the approximate simulation, the sim‐to‐real transfer achieved an average reaching distance of mm (), an average orientation error of rad () and applied pushing forces up to N. Such performance is reasonable for the intended assistive tasks of the manipulator. The experiments uncovered that the soft robot interacting with the environment exhibited torsional and counter‐balancing movements. Although not explicitly enforced, they emerged from the mechanical intelligence of the manipulator. The results demonstrate the potential of soft robotic manipulation via reinforcement learning.

Vision‐Based Online Key Point Estimation of Deformable Robots

The precise control of soft and continuum robots requires knowledge of their shape, which has, in contrast to classical rigid robots, infinite degrees of freedom. To partially reconstruct the shape, proprioceptive techniques use built‐in sensors, resulting in inaccurate results and increased fabrication complexity. Exteroceptive methods so far rely on expensive tracking systems with reflective markers placed on all components, which are infeasible for deformable robots interacting with the environment due to marker occlusion and damage. Here, a regression approach is presented for three‐dimensional key point estimation using a convolutional neural network. The proposed approach uses data‐driven supervised learning and is capable of online markerless estimation during inference. Two images of a robotic system are captured simultaneously at 25 Hz from different perspectives and fed to the network, which returns for each pair the parameterized key point or piecewise constant curvature shape representations. The proposed approach outperforms markerless state‐of‐the‐art methods by a maximum of 4.5% in estimation accuracy while being more robust and requiring no prior knowledge of the shape. Online evaluations on two types of soft robotic arms and a soft robotic fish demonstrate the method's accuracy and versatility on highly deformable systems.

Classification and Evaluation of Octopus-Inspired Suction Cups for Soft Continuum Robots.

The emergence of the field of soft robotics has led to an interest in suction cups as auxiliary structures on soft continuum arms to support the execution of manipulation tasks. This application poses demanding requirements on suction cups with respect to sensorization, adhesion under non-ideal contact conditions, and integration into fully soft systems. The octopus can serve as an important source of inspiration for addressing these challenges. This review aims to accelerate research in octopus-inspired suction cups by providing a detailed analysis of the octopus sucker, determining meaningful performance metrics for suction cups on the basis of this analysis, and evaluating the state-of-the-art in suction cups according to these performance metrics. In total, 47 records describing suction cups are found, classified according to the deployed actuation method, and evaluated on performance metrics reflecting the level of sensorization, adhesion, and integration. Despite significant advances in recent years, the octopus sucker outperforms all suction cups on all performance metrics. The realization of high resolution tactile sensing in suction cups and the integration of such sensorized suction cups in soft continuum structures are identified as two major hurdles toward the realization of octopus-inspired manipulation strategies in soft continuum robot arms.

Geometrically exact 3D arbitrarily curved rod theory for dynamic analysis: Application to predicting the motion of hard-magnetic soft robotic arm

Magnetorheological elastomers are active materials which can be actuated by the applied magnetic field. Hard magnetic soft (HMS) materials, a type of magnetorheological elastomers, show great potential in the fields of biomedical engineering and soft robotics, due to their short response time, remote operation, and shape programmability. To exploit its potential, a series of theoretical frameworks of HMS rods have been developed, but they are mainly limited to the static rod models or classical curved rod models that fail to consider the effect of the “initial curvature” on the distribution of the stress. In this work, we develop a curved rod theory to predict the 3D dynamic motion of the rod-like HMS robotics under large deformation. Based on the geometrically exact rod theory, we include the heterogeneous initial length of the longitudinal fiber caused by “initial curvature” into our model and obtain the reduced balance equations of the HMS robotics. As a result, the “tension-bending” and “shear-torsion” coupling effects of curved rods emerge in the present model. A numerical implementation of our model based on the classical Newmark algorithm is presented. To validate our model, three numerical examples, including the dynamic snap-through behavior of a bistable arch, are performed and compared with the simulation or experiment results reported in literatures, which show a good agreement. Finally, we experimentally study the 2D and 3D static and dynamic motion of a quarter arc HMS robotic arm under an applied magnetic field of 10 mT, and our model gives a satisfactory prediction, especially for static deformation.

Adaptive iterative learning control of soft robot for beating heart tracking

PurposeSoft robots are known for their excellent safe interaction ability and promising in surgical applications for their lower risks of damaging the surrounding organs when operating than their rigid counterparts. To explore the potential of soft robots in cardiac surgery, this paper aims to propose an adaptive iterative learning controller for tracking the irregular motion of the beating heart.Design/methodology/approachIn continuous beating heart surgery, providing a relatively stable operating environment for the operator is crucial. It is highly necessary to use position-tracking technology to keep the target and the surgical manipulator as static as possible. To address the position tracking and control challenges associated with dynamic targets, with a focus on tracking the motion of the heart, control design work has been carried out. Considering the lag error introduced by the material properties of the soft surgical robotic arm and system delays, a controller design incorporating iterative learning control with parameter estimation was used for position control. The stability of the controller was analyzed and proven through the construction of a Lyapunov function, taking into account the unique characteristics of the soft robotic system.FindingsThe tracking performance of both the proportional-derivative (PD) position controller and the adaptive iterative learning controller are conducted on the simulated heart platform. The results of these two methods are compared and analyzed. The designed adaptive iterative learning control algorithm for position control at the end effector of the soft robotic system has demonstrated improved control precision and stability compared with traditional PD controllers. It exhibits effective compensation for periodic lag caused by system delays and material characteristics.Originality/valueTracking the beating heart, which undergoes quasi-periodic and complex motion with varying accelerations, poses a significant challenge even for rigid mechanical arms that can be precisely controlled and makes tracking targets located at the surface of the heart with the soft robot fraught with considerable difficulties. This paper originally proposes an adaptive interactive learning control algorithm to cope with the dynamic object tracking problem. The algorithm has theoretically proved its convergence and experimentally validated its performance at the cable-driven soft robot test bed.

A Soft Continuum Robotic Arm with a Climbing Plant‐Inspired Adaptive Behavior for Minimal Sensing, Actuation, and Control Effort

A Soft Continuum Robotic Arm with a Climbing Plant‐Inspired Adaptive Behavior for Minimal Sensing, Actuation, and Control Effort

Planar soft space robotic manipulators: Dynamic modeling and control

Space robots have proven their ability to accomplish many space missions impossible or dangerous for the human. However, the conventional space robots suffer from the low adaptability, heavy structures, and the low safety. In this regard, the bio-inspired soft robots, composed of the soft material, have been emerged to overcome these shortcomings and to expand the space missions’ diversity. The potential of soft robots for space applications has recently been investigated in the literature. Although the dynamic modeling and control of soft robotic arms are complicated due to their infinite degrees of freedom, soft manipulators are anticipated to provide the adaptive capture of targets, confined space operations, and the long-distance handling of space facilities. In this regard, a general novel dynamic modeling algorithm is presented here for soft space robotic manipulators (SSRM) in a specified orientation based on the generalized Jacobian method. The proposed algorithm could be employed for different numbers of soft manipulator constant-curvature elements and avoids numerical singularities using the Taylor expansion as well. In addition, the generalized Jacobian matrix is defined to compute the velocity of any arbitrary point on the soft manipulator for contact dynamics objectives. Accuracy of the proposed dynamic model has been verified via comparison to the simulation results of the existing fixed-base soft manipulators. Moreover, performance of the proposed model has been more investigated through two numerical simulation case studies. The first case verifies that the dynamic responses of the floating SSRM follows the physical principles. While the second one addresses the configuration tracking problem of the floating SSRM exposing a computed torque control scheme. The obtained results indicate the effectiveness of the presented dynamic modeling algorithm and the proposed control scheme.

Evidence for tactile 3D shape discrimination by octopus.

Octopuses integrate visual, chemical and tactile sensory information while foraging and feeding in complex marine habitats. The respective roles of these modes are of interest ecologically, neurobiologically, and for development of engineered soft robotic arms. While vision guides their foraging path, benthic octopuses primarily search "blindly" with their arms to find visually hidden prey amidst rocks, crevices and coral heads. Each octopus arm is lined with hundreds of suckers that possess a combination of chemo- and mechanoreceptors to distinguish prey. Contact chemoreception has been demonstrated in lab tests, but mechanotactile sensing is less well characterized. We designed a non-invasive live animal behavioral assay that isolated mechanosensory capabilities of Octopus bimaculoides arms and suckers to discriminate among five resin 3D-printed prey and non-prey shapes (all with identical chemical signatures). Each shape was introduced inside a rock dome and was only accessible to the octopus' arms. Octopuses' responses were variable. Young octopuses discriminated the crab prey shape from the control, whereas older octopuses did not. These experiments suggest that mechanotactile sensing of 3D shapes may aid in prey discrimination; however, (i) chemo-tactile information may be prioritized over mechanotactile information in prey discrimination, and (ii) mechanosensory capability may decline with age.

Piston-like particle jamming for enhanced stiffness adjustment of soft robotic arm

PurposeStiffness adjusting ability is essential for soft robotic arms to perform complex tasks. A soft state enables dexterous operation and safe interaction, while a rigid state enables large force output or heavy weight carrying. However, making a compact integration of soft actuators with powerful stiffness adjusting mechanisms is challenging. This study aims to develop a piston-like particle jamming mechanism for enhanced stiffness adjustment of a soft robotic arm.Design/methodology/approachThe arm has two pairs of differential tendons for spatial bending, and a jamming core consists of four jamming units with particles sealed inside braided tubes for stiffness adjustment. The jamming core is pushed and pulled smoothly along the tendons by a piston, which is then driven by a motor and a ball screw mechanism.FindingsThe tip displacement of the arm under 150 N jamming force and no more than 0.3 kg load is minimal. The maximum stiffening ratio measured in the experiment under 150 N jamming force is up to 6–25 depends on the bending direction and added load of the arm, which is superior to most of the vacuum powered jamming method.Originality/valueThe proposed robotic arm makes an innovative compact integration of tendon-driven robotic arm and motor-driven piston-like particle jamming mechanism. The jamming force is much larger compared to conventional vacuum-powered systems and results in a superior stiffening ability.

Elephant Trunk Inspired Multimodal Deformations and Movements of Soft Robotic Arms

AbstractElephant trunks are capable of complex, multimodal deformations, allowing them to perform task‐oriented high‐degree‐of‐freedom (DOF) movements pertinent to the field of soft actuators. Despite recent advances, most soft actuators can only achieve one or two deformation modes, limiting their motion range and applications. Inspired by the elephant trunk musculature, a liquid crystal elastomer (LCE)‐based multi‐fiber design strategy is proposed for soft robotic arms in which a discrete number of artificial muscle fibers can be selectively actuated, achieving multimodal deformations and transitions between modes for continuous movements. Through experiments, finite element analysis (FEA), and a theoretical model, the influence of LCE fiber design on the achievable deformations, movements, and reachability of trunk‐inspired robotic arms is studied. Fiber geometry is parametrically investigated for 2‐fiber robotic arms and the tilting and bending of these arms is characterized. A 3‐fiber robotic arm is additionally studied with a simplified fiber arrangement analogous to that of an actual elephant trunk. The remarkably broad range of deformations and the reachability of the arm are discussed, alongside transitions between deformation modes for functional movements. It is anticipated that this design and actuation strategy will serve as a robust method to realize high‐DOF soft actuators for various engineering applications.

A Dynamic Modeling Method for Soft Pneumatic Robotic Arms Considering Both Geometric Nonlinearity and Visco-Hyperelasticity

This work proposes a new dynamic modeling approach that integrates the principle of virtual power and a spring–damper–fluid equivalent method. It is able to simultaneously consider the geometric and material nonlinearity including hyperelasticity and viscoelasticity of the soft robotic arm. Meanwhile, the nonuniform deformation of the soft arm wall can be introduced into the model based on the equivalent model. A general prototype of soft robotic arms is fabricated and validation experiments of three pneumatic actuation modes are carried out. A maximum position error of 3.64[Formula: see text]mm at the end of the soft arm is obtained with an eight-segment theoretical model for the 280[Formula: see text]mm long and 30[Formula: see text]mm thick prototype in an approximate step actuation test with a maximum pneumatic pressure of 11.5[Formula: see text]kPa. The comparisons between the theoretical prediction and experimental results demonstrate a high accuracy of the proposed modeling method. Besides, simulations of dynamic motions under in-plane and out-of-plane actuation are carried out respectively, illustrating that the proposed method has the ability to describe a variety of actuation forms. The proposed dynamic modeling method provides a new way for modeling the soft robotic arms and has guiding significance for the design and control of soft robotic arms.