Control Of Soft Robots Research Articles

Spiking neural networks-based generation of caterpillar-like soft robot crawling motions

Robots have been widely used in daily life in recent years. Unlike conventional robots made of rigid materials, soft robots utilize stretchable and flexible materials, allowing flexible movements similar to those of living organisms, which are difficult for traditional robots. Previous studies have used periodic signals to control soft robots, which lead to repetitive motions and make it challenging to generate environment-adapted motions. To address this issue, control methods can be learned through deep reinforcement learning to enable soft robots to select appropriate actions based on observations, improving their adaptability to environmental changes. In addition, as mobile robots have limited onboard resources, it is necessary to conserve battery consumption and achieve low-power control. Therefore, the use of spiking neural networks (SNNs) with neuromorphic chips enables low-power control of soft robots. In this study, we investigated the learning methods for SNNs aimed at controlling soft robots. Experiments were conducted using a caterpillar-like soft robot model based on previous studies, and the effectiveness of the learning method was evaluated.

Simplified discrete model for axisymmetric dielectric elastomer membranes with robotic applications

Soft robots utilizing inflatable dielectric membranes can realize intricate functionalities through the application of non-mechanical fields. However, given the current limitations in simulations, including low computational efficiency and difficulty in dealing with complex external interactions, the design and control of such soft robots often require trial and error. Thus, a novel one-dimensional (1D) discrete differential geometry (DDG)-based numerical model is developed for analyzing the highly nonlinear mechanics in axisymmetric inflatable dielectric membranes. The model captures the intricate dynamics of these membranes under both inflationary pressure and electrical stimulation. Comprehensive validations using hyperelastic benchmarks demonstrate the model’s accuracy and reliability. Additionally, the focus on the electro-mechanical coupling elucidates critical insights into the membrane’s behavior under varying internal pressures and electrical loads. The research further translates these findings into innovative soft robotic applications, including a spherical soft actuator, a soft circular fluid pump, and a soft toroidal gripper, where the snap-through of electroelastic membrane plays a crucial role. Our analyses reveal that the functional ranges of soft robots are amplified by the snap-through of an electroelastic membrane upon electrical stimuli. This study underscores the potential of DDG-based simulations to advance the understanding of the nonlinear mechanics of electroelastic membranes and guide the design of electroelastic actuators in soft robotics applications.

Vision-based reinforcement learning control of soft robot manipulators

Purpose This study aims to tackle control challenges in soft robots by proposing a visually-guided reinforcement learning approach. Precise tip trajectory tracking is achieved for a soft arm manipulator. Design/methodology/approach A closed-loop control strategy uses deep learning-powered perception and model-free reinforcement learning. Visual feedback detects the arm’s tip while efficient policy search is conducted via interactive sample collection. Findings Physical experiments demonstrate a soft arm successfully transporting objects by learning coordinated actuation policies guided by visual observations, without analytical models. Research limitations/implications Constraints potentially include simulator gaps and dynamical variations. Future work will focus on enhancing adaptation capabilities. Practical implications By eliminating assumptions on precise analytical models or instrumentation requirements, the proposed data-driven framework offers a practical solution for real-world control challenges in soft systems. Originality/value This research provides an effective methodology integrating robust machine perception and learning for intelligent autonomous control of soft robots with complex morphologies.

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.

High-Performance Hydraulic Soft Robotic Control Using Continuous Flow Regulation and Partial Feedback

High-Performance Hydraulic Soft Robotic Control Using Continuous Flow Regulation and Partial Feedback

A multi-chamber soft robot for transesophageal echocardiography: continuous kinematic matching control of soft medical robots.

This research investigates designing a continuum soft robot and proposing a kinematic matching control to enable the robot to perform a specified medical task, which in this paper is the transesophageal echocardiography (TEE). A multi-chamber soft robot was designed and fabricated based on the molding of separate layers. The method of transformation matrices was used to develop the kinematic models, and a control method using Jacobian matrices was proposed to manipulate the robot. A prototype was made based on a multi-chamber multi-layer design. The system contains three segments that can be actuated independently to mimic the active bending part of the respective probe. Kinematic models were developed. Negative pressure (vacuum) was used as actuation input. An open-loop controller inspired by a redundancy resolution technique was proposed to make the soft robot tip follow the desired path, i.e.the path of the rigid ultrasound probe. It is concluded that the soft solution can perform the required task as the reachable points of the TEEtip cover the proposed robot workspace and the proposed control can be used for maneuvering in arbitrary trajectories.

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.

Visuo-dynamic self-modelling of soft robotic systems.

Soft robots exhibit complex nonlinear dynamics with large degrees of freedom, making their modelling and control challenging. Typically, reduced-order models in time or space are used in addressing these challenges, but the resulting simplification limits soft robot control accuracy and restricts their range of motion. In this work, we introduce an end-to-end learning-based approach for fully dynamic modelling of any general robotic system that does not rely on predefined structures, learning dynamic models of the robot directly in the visual space. The generated models possess identical dimensionality to the observation space, resulting in models whose complexity is determined by the sensory system without explicitly decomposing the problem. To validate the effectiveness of our proposed method, we apply it to a fully soft robotic manipulator, and we demonstrate its applicability in controller development through an open-loop optimization-based controller. We achieve a wide range of dynamic control tasks including shape control, trajectory tracking and obstacle avoidance using a model derived from just 90 min of real-world data. Our work thus far provides the most comprehensive strategy for controlling a general soft robotic system, without constraints on the shape, properties, or dimensionality of the system.

Discrete-Time Impedance Control for Dynamic Response Regulation of Parallel Soft Robots.

Accurately controlling the dynamic response and suppression of undesirable dynamics such as overshoots and vibrations is a vital requirement for soft robots operating in industrial environments. Pneumatically actuated soft robots usually undergo large overshoots and significant vibrations when deactuated because of their highly flexible bodies. These large vibrations not only decrease the reliability and accuracy of the soft robot but also introduce undesirable characteristics in the system. For example, it increases the settling time and damages the body of the soft robot, compromising its structural integrity. The dynamic behavior of the soft robots on deactuation needs to be accurately controlled to increase their utility in real-world applications. The literature on pneumatic soft robots still does not sufficiently address the issue of suppressing undesirable vibrations. To address this issue, we propose the use of impedance control to regulate the dynamic response of pneumatic soft robots since the superiority of impedance control is already established for rigid robots. The soft robots are highly nonlinear systems; therefore, we formulated a nonlinear discrete sliding mode impedance controller to control the pneumatic soft robots. The formulation of the controller in discrete-time allows efficient implementation for a high-order system model without the need for state-observers. The simplification and efficiency of the proposed controller enable fast implementation of an embedded system. Unlike other works on pneumatic soft robots, the proposed controller does not require manual tuning of the controller parameters and automatically calculates the parameters based on the impedance value. To demonstrate the efficacy of the proposed controller, we used a 6-chambered parallel soft robot as an experimental platform. We presented the comparative results with an existing state-of-the-art controller in SMC control of pneumatic soft robots. The experiment results indicate that the proposed controller can effectively limit the amplitude of the undesirable vibrations.

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.

Stiffness Change for Reconfiguration of Inflated Beam Robots.

Abstract Active control of the shape of soft robots is challenging. Despite having an infinite number of passive degrees of freedom (DOFs), soft robots typically only have a few actively controllable DOFs, limited by the number of degrees of actuation (DOAs). The complexity of actuators restricts the number of DOAs that can be incorporated into soft robots. Active shape control is further complicated by the buckling of soft robots under compressive forces; this is particularly challenging for compliant continuum robots due to their long aspect ratios. In this study, we show how variable stiffness enables shape control of soft robots by addressing these challenges. Dynamically changing the stiffness of sections along a compliant continuum robot selectively "activates" discrete joints. By changing which joints are activated, the output of a single actuator can be reconfigured to actively control many different joints, thus decoupling the number of controllable DOFs from the number of DOAs. We demonstrate embedded positive pressure layer jamming as a simple method for stiffness change in inflated beam robots, its compatibility with growing robots, and its use as an "activating" technology. We experimentally characterize the stiffness change in a growing inflated beam robot and present finite element models that serve as guides for robot design and fabrication. We fabricate a multisegment everting inflated beam robot and demonstrate how stiffness change is compatible with growth through tip eversion, enables an increase in workspace, and achieves new actuation patterns not possible without stiffening.

Toward Onboard Proportional Control of Multi-Chamber Soft Pneumatic Robots: A Magnetorheological Elastomer Valve Array.

Soft pneumatic actuators (SPAs) are commonly used in various applications because of their structural compliance, low cost, ease of manufacture, high adaptability, and safe human-robot interaction. The traditional approach for achieving proportional control of soft pneumatic robots requires the use of industrial proportional valves or syringe drivers, which are not only rigid and bulky but also hard to be integrated into the body of soft robots. In our previous research, we developed a Magnetorheological elastomer (MRE)-based soft valve that showed advantages for controlling SPAs due to its compliance, compactness, robustness, and compatibility for continuous pressure modulation. Modern soft robots with multiple chambers require more MRE valves onboard for their control. However, merely packing more MRE valves for soft robots can cause problems like magnetic interference, flow rate deviation, and overheating. Therefore, in this study, we proposed a two-dimensional MRE valve array design to solve issues of magnetic interference and overheating when expanding from a single MRE proportional valve into an integrated array. The magnetic interference and the overheating problem were investigated through multiphysics simulation, bringing the optimal choice of valve spacing (1.2 times the single valve diameter), magnetic coil pole arrangement (same pole), and the cooling system design (internal cooling chamber with flowing water). Physical experiments showed that our MRE valve array maintained its original flowrate performance with low magnetic interference (0.89 mT) and low coil temperature (under 73.9°C for 5 min).

Soft crawling robot integrated with liquid metal-based flexible strain sensor and closed-loop feedback control

Due to the inherent softness and adaptability, soft robots have shown great application prospects in medical rehabilitation, environmental exploration, and other fields. However, the unpredictable and complex behaviors of the elastic materials greatly increased the difficulty in the control of soft robots. This study proposed an integrated soft crawling robot (ISCR) with flexible strain sensors to achieve the closed-loop feedback control of motion. First, a flexible strain sensor was fabricated by injecting liquid metal (LM) into an elastomer with a wavy structure. The key performances of the sensor were investigated. The developed flexible strain sensor can achieve a high gauge factor of 2.07, a low hysteresis of 1.902%, a wide detection range of 150% strain, a low detection limitation as low as 0.4% strain change, and an excellent stability and durability, which fully satisfy the requirements for the proprioception of soft robots. We built a closed-loop feedback control system to achieve closed-loop control of the ISCR. Finally, we successfully realized the continuous motion of the ISCR under closed-loop control. We believe this work will be helpful for the development of automation and intelligence of soft crawling robots.

Dynamic modeling for continuous locomotion of bionic soft worm-like robot

In the nature, worm-like robot can bend its slender body into an arch bridge when it moves, and it will move quickly in this deformation mode to nimbly escape from enemies once encountering danger. Inspired by this locomotion mechanism, a plenty of bionic soft worm-like robots have been devised and studied adopting quasi-static method. Nevertheless, how to describe the stick-slip transition and the continuity of the entire locomotion process of soft worm-like robots is still a challenging problem. To settle the above issue, a simplified curved beam model is developed to depict a type of soft worm-like robot move forward continuously by controlling its initial curvature in this paper. Based on the curved beam model, a novel dynamic modeling method is further proposed to solve stick-slip nonlinear switchover problem of the soft robot. Since the curved beam is divided into finite segments, an angular variable of an arbitrary point is introduced to elaborate the deformed shape of the curved beam, and then the dimensionless discretized equations of motion can be established via theoretical derivation and solved by implicit integration method. Through dynamic simulation and analysis, a continuous forward locomotion and net displacement of the robot can be easily observed, and several laws of the locomotion are uncovered, which reveals the stick-slip dynamic behaviors. Distinguished from the normal quasi-static modeling approach, the law of motion of the soft worm-like robot is clarified from the dynamic point of view, which will provide important reference for structural design and optimization control of similar soft robots.

Dynamics and Control of Soft Robots With Implicit Strain Parametrization

Dynamics and Control of Soft Robots With Implicit Strain Parametrization

Research on space attitude recognition and control method for soft robotics based on neural networks

In the modern field of biomedical robotics, there is increasing attention on the use of soft robots as carriers for surgical devices. Compared to rigid robots, soft robots exhibit more complex postures and work environments, requiring new methods for recognizing and controlling their postures within biological organisms. This paper investigates the recognition and control of the posture of soft robots using a neural network learning approach, utilizing a soft robot equipped with resistive sensors. By establishing the relationship between changes in resistance values and posture variations, the study successfully achieves the identification and control of the soft robot's posture. The obtained posture data based on resistance values are validated for reliability. Thus, by combining resistive sensors with soft robots and employing neural network analysis, the recognition and control of soft robots postures within biological organisms can be achieved.

Interference Morphology of Free-Growing Tendrils and Application of Self-Locking Structures.

Organisms can adapt to various complex environments by obtaining optimal morphologies. Plant tendrils evolve an extraordinary and stable spiral morphology in the free-growing stage. By combining apical and asymmetrical growth strategies, the tendrils can adjust their morphology to wrap around and grab different supports. This phenomenon of changing tendril morphology through the movement of growth inspires a thoughtful consideration of the laws of growth that underlie it. In this study, tendril growth is modeled based on the Kirchhoff rod theory to obtain the exact morphological equations. Based on this, the movement patterns of the tendrils are investigated under different growth strategies. It is shown that the self-interference phenomenon appears as the tendril grows, allowing it to hold onto its support more firmly. In addition, a finite element model is constructed using continuum media mechanics and following the finite growth theory to simulate tendril growth. The growth morphology and self-interference phenomenon of tendrils are observed visually. Furthermore, an innovative class of fluid elastic actuators is designed to verify the growth phenomena of tendrils, which can realize the wrapping and locking functions. Several experiments are conducted to measure the end output force and the smallest size that can be clamped, and the output efficiency of the elastic actuator and the optimal working pressure are verified. The results presented in this study could reveal the formation law of free tendril spiral morphology and provide an inspiring idea for the programmability and motion control of bionic soft robots, with promising applications in the fields of underwater rescue and underwater picking.

Kinematics, dynamics and control of stiffness-tunable soft robots

Modeling and control methods for stiffness-tunable soft robots (STSRs) have received less attention compared to standard soft robots. A major challenge in controlling STSRs is their infinite degrees of freedom, similar to standard soft robots. In this paper, demonstrate a novel STSR by combing a soft-rigid hybrid spine-mimicking actuator with a stiffness-tunable module. Additionally, we introduce a new kinematic and dynamic modeling methodology for the proposed STSR. Based on the STSR characteristics, we model it as a series of PRP segments, each composed of two prismatic joints(P) and one revolute joint(R). This method is simpler, more generalizable, and more computationally efficient than existing approaches. We also design a multi-input multi-output (MIMO) controller that directly adjusts the pressure of the STSR’s three pneumatic chambers to precisely control its posture. Both the novel modeling methodology and MIMO control system are implemented and validated on the proposed STSR prototype.

Sorotoki: A Matlab Toolkit for Design, Modeling, and Control of Soft Robots

Sorotoki: A Matlab Toolkit for Design, Modeling, and Control of Soft Robots

Robust control of electrohydraulic soft robots.

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.