Soft Continuum Research Articles

Closed-Loop Magnetic Control of Medical Soft Continuum Robots for Deflection

Closed-Loop Magnetic Control of Medical Soft Continuum Robots for Deflection

Integral passivity-based control of an underactuated hydraulic soft manipulator with uncertain nonlinear stiffness

This work investigates the integral passivity-based control of an underactuated soft continuum manipulator that bends on a horizontal plane, that is actuated by pressurized fuid, and that is characterized by uncertain nonlinear stiffness. The main contribution of this work is the application of the integral passivity-based control methodology to a system that is not in affine form and that is subject to parameter uncertainty resulting in multiple position-dependent disturbances. The effectiveness of the controller is demonstrated with numerical simulations.

Deep CNN-Based Static Modeling of Soft Robots Utilizing Absolute Nodal Coordinate Formulation.

Soft continuum robots, inspired by the adaptability and agility of natural soft-bodied organisms like octopuses and elephant trunks, present a frontier in robotics research. However, exploiting their full potential necessitates precise modeling and control for specific motion and manipulation tasks. This study introduces an innovative approach using Deep Convolutional Neural Networks (CNN) for the inverse quasi-static modeling of these robots within the Absolute Nodal Coordinate Formulation (ANCF) framework. The ANCF effectively represents the complex non-linear behavior of soft continuum robots, while the CNN-based models are optimized for computational efficiency and precision. This combination is crucial for addressing the complex inverse statics problems associated with ANCF-modeled robots. Extensive numerical experiments were conducted to assess the performance of these Deep CNN-based models, demonstrating their suitability for real-time simulation and control in statics modeling. Additionally, this study includes a detailed cross-validation experiment to identify the most effective model architecture, taking into account factors such as the number of layers, activation functions, and unit configurations. The results highlight the significant benefits of integrating Deep CNN with ANCF models, paving the way for advanced statics modeling in soft continuum robotics.

Variable Curvature Modeling Method of Soft Continuum Robots with Constraints

The inherent compliance of continuum robots holds great promise in the fields of soft manipulation and safe human–robot interaction. This compliance reduces the risk of damage to the manipulated object and its surroundings. However, continuum robots possess theoretically infinite degrees of freedom, and this high flexibility usually leads to complex deformations when subjected to external forces and positional constraints. Describing these complex deformations is the main challenge in modeling continuum robots. In this study, we investigated a novel variable curvature modeling method for continuum robots, considering external forces and positional constraints. The robot configuration curve is described using the developed mechanical model, and then the robot is fitted to the curve. A ten-section continuum robot prototype with a length of 1 m was developed in order to validate the model. The feasibility and accuracy of the model were verified by the ability of the robot to reach target points and track complex trajectories with a load. This work was able to serve as a new perspective for the design analysis and motion control of continuum robots.

Variable Stiffness Linear Actuator Based on Differential Drive Fiber Jamming

Variable stiffness technologies have been widely adopted in soft robotics, also combining them with different actuation technologies and demonstrating the potential to increase the range of possible applications of soft systems, such as soft continuum manipulators. However, in most cases, the variable stiffness capabilities of these soft system are far from satisfying the application requirements. With the aim to explore new possibilities to fill this gap, in this work, we present a novel variable stiffness linear actuator (VSLA) based on the combination of composite fiber jamming and the inverse pneumatic artificial muscles working principle. The differential pressure driving approach adopted to control the VSLA decouples the actuator deformation from the variable stiffness capabilities, and this makes the VSLA a suitable candidate for the realization of new kind of continuous arms. Moreover, the VSLA novel architecture introduced in this article was analytically modeled considering the interaction among constituent elements and performances in different loading conditions have been computed. The results obtained from the experimental tests validated the model goodness in terms of assumptions made and showed remarkable variable stiffness capabilities, with a maximum jamming ratio that reaches 21.3.

Stiffness modelling and analysis of soft fluidic-driven robots using Lie theory

Soft robots have been investigated for various applications due to their inherently superior deformability and flexibility compared to rigid-link robots. However, these robots struggle to perform tasks that require on-demand stiffness, that is, exerting sufficient forces within allowable deflection. In addition, the soft and compliant materials also introduce large deformation and non-negligible nonlinearity, which makes the stiffness analysis and modelling of soft robots fundamentally challenging. This paper proposes a modelling framework to investigate the underlying stiffness and the equivalent compliance properties of soft robots under different configurations. Firstly, a modelling and analysis methodology is described based on Lie theory. Here, we derive two sets (the piecewise constant curvature and Cosserat rod model) of compliance models. Furthermore, the methodology can accommodate the nonlinear responses (e.g., bending angles) resulting from elongation of robots. Using this proposed methodology, the general Cartesian stiffness or compliance matrix can be derived and used for configuration-dependent stiffness analysis. The proposed framework is then instantiated and implemented on fluidic-driven soft continuum robots. The efficacy and modelling accuracy of the proposed methodology are validated using both simulations and experiments.

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

A key challenge in designing soft continuum robotic arms is the realization of intelligent behavior while minimizing sensing, actuation, and control effort. This work investigates how soft continuum arms can benefit from mimicking the distribution of flexural rigidity of searcher stems in climbing plants to accomplish this goal. A modeling approach is presented to tune both the structural design and the tactile sensor design of a soft continuum arm inspired by the flexural rigidity distribution of Mandevilla cf. splendens’ searcher stems. The resulting soft continuum arm, named Mandy, can detect suitable supports along its length and twining around them using a single sensor and actuator. Through simulations and experiments, it is shown such behavior cannot be achieved with a soft continuum arm possessing uniform structural stiffness and a standard tactile sensor design. Thus, the significance of investing greater effort in structural design, leveraging biological data, to improve the design of soft continuum arms with more compact actuation and sensing hardware, is highlighted.

Design and Nonlinear Error Compensation of a Multi-Segment Soft Continuum Robot for Pulmonary Intervention

Design and Nonlinear Error Compensation of a Multi-Segment Soft Continuum Robot for Pulmonary Intervention

Nonlinear dynamic modeling and model-based AI-driven control of a magnetoactive soft continuum robot in a fluidic environment

In recent years, magnetoactive soft continuum robots (MSCRs) with multimodal locomotion capabilities have emerged for various biomedical applications. Developments in nonlinear dynamic models and effective control methods for MSCRs are deemed vital not only to gain a better understanding of their coupled magneto-mechanical behavior but also to accurately steer the MSCRs inside the human body. This study presents a novel dynamic model and model-based AI-driven control method to guide an MSCR in a fluidic environment. The MSCR is fully exposed to fluid flows at different rates to simulate the biofluidic environment within the body. A novel nonlinear dynamic model considering the effect of damping and drag force attributed to fluidic flows is first developed to accurately and efficiently predict the response of the MSCR under varying magnetic and mechanical loading. Fairly accurate correlations were observed between the theoretical responses based on the developed magneto-viscoelastic model and the experimental data for various scenarios. A novel model-based control algorithm based on a fractional-order sliding surface and deep reinforcement learning algorithm (DRL-FOSMC) is subsequently developed to accurately steer the magnetoactive soft robot on predefined trajectories considering varying fluid flow rates. A fractional-order sliding surface and a compensator, trained using the deep deterministic policy gradient algorithm, are designed to mitigate the amount of chattering and enhance the tracking performance of the closed-loop system. The stability proof of the developed control algorithm is also presented. A hardware-in-the-loop experimental framework has been designed to assess the effectiveness of the proposed control algorithm through various case studies. The performance of the proposed DRL-FOSMC algorithm is rigorously assessed and found to be superior when compared with other control methods.

Design and Kinematic Modeling of a Soft Continuum Telescopic Arm for the Self-Assembly Mechanism of a Modular Robot.

In recent years, the development of mining robots has grown significantly, offering improved efficiency and safety in hazardous environments. However, there is still room for improvement in adaptability, scalability, and overall performance. The ROBOMINERS project, funded by the European Union's Horizon 2020 Research and Innovation Program, aims to facilitate Europe's access to mineral resources applying disruptive robotic concepts. One such concept is resilience, which can be achieved providing modular mining robots with the ability to reconfigure during operation. To address this challenge, this article presents the development and kinematic modeling of a soft, telescopic, continuum arm integrated into a modular robot. The arm serves as a mechanical interface for coupling different robotic modules or tools following the principle of the car crane. With a fully 3D-printed design, the arm features two sections of variable length that are driven by an innovative actuation method based on soft racks. It provides a 6 degrees of freedom (DoF) motion. The arm kinematic models are obtained by backbone parameterization assuming constant curvature and independent bending between sections for forward kinematics and applying a machine learning-based approach for inverse kinematics. The models are validated through the evaluation of two trajectories, measuring the deviation in each DoF and rack extension. Furthermore, a demonstration of the arm's coupling procedure between two robotic modules and one possible configuration of the robotic system showcases its functionality.

Design, Performance Analysis, and Experiments of a Soft Robot for Rescue

Abstract In the narrow and irregular environment of the ruins, the existing rescue robots are struggling to achieve their performance. Inspired by the process of termite predation by giant anteaters, we propose a soft rescue robot that utilizes motion propulsion similar to gear meshing and the adaptability of a continuum manipulator. The robot, consisting of a soft continuum manipulator and driving equipment, has the characteristics of fast propulsion and adaptation to unstructured environments. The driving device can give the manipulator a maximum speed of 14.67 cm/s and a propulsive force of 19.20 N. With the flexibility of the soft robot, the soft manipulator can adapt to the environment under propulsion to pass obstacles. The experiments of self-adaptability performance tests under different conditions show that the robot can pass over obstacles with an angle of up to 80.57 deg between its axis and the contact surface. In the actual ruin experiment, the robot could penetrate 1.3 m deep in the narrow passage formed by the bricks with the mode. The experiment indicates the presented rescue robot design's feasibility. Our work could contribute to the research on the interaction of soft robots with their environment.

Evolution of Elemental Abundances in Hot Active Region Cores from Chandrayaan-2 XSM Observations

The first ionization potential (FIP) bias, whereby elemental abundances for low-FIP elements in different coronal structures vary from their photospheric values and may also vary with time, has been widely studied. In order to study the temporal variation and understand the physical mechanisms giving rise to the FIP bias, we have investigated the hot cores of three active regions (ARs) using disk-integrated soft X-ray spectroscopic observations with the Solar X-ray Monitor on board Chandrayaan-2. Observations for periods when only one AR was present on the solar disk were used to ensure that the AR was the principal contributor to the total X-ray intensity. The average values of temperature and emission measure were ∼3 MK and 3 × 1046 cm−3, respectively. Regardless of the AR’s age or activity, the elemental abundances for the low-FIP elements Al, Mg, and Si with respect to the soft X-ray continuum were consistently higher than their photospheric values. The average FIP bias for Mg and Si was 2–2.5, whereas the FIP bias for the mid-FIP element, S, was almost unity. However, the FIP bias for the lowest-FIP element, Al, was observed to be a factor of 2 higher than Si, which, if real, suggests a dependence of the FIP bias of low-FIP elements on their FIP value. Another major result from our analysis is that the FIP bias of these elements is established within ∼10 hr of emergence of the AR and remains almost constant throughout its lifetime.

Axis-space framework for cable-driven soft continuum robot control via reinforcement learning

Cable-driven soft continuum robots are important tools in minimally invasive surgery (MIS) to reduce the lesions, pain and risk of infection. The feasibility of employing deep reinforcement learning (DRL) for controlling cable-driven continuum robots has been investigated; however, a considerable gap between simulations and the real world exists. Here we introduce a deep reinforcement learning-based method, the Axis-Space (AS) framework, which accelerates the computational speed and improves the accuracy of robotic control by reducing sample complexity (SC) and the number of training steps. In this framework, the SC was reduced through the design of state space and action space. We demonstrate that our framework could control a cable driven soft continuum robot with four tendons per section. Compared with the Double Deep Q-learning Network (DDQN) controller, the proposed controller increased the convergence speed by more than 11-fold, and reduced the positioning error by over 10-fold. This framework provides a robust method for soft robotics control.

Impact of materials on performance of Vine Robot

Vine Robots are soft continuum robots designed with low-cost fabrication and for the navigation of difficult environments. Due to their movement patterns resembling those of natural vines, these robots also known as “everting vine robots” grow by pressure-driven eversion. This paper presents the impact of different materials on the performance of Vine Robot. Three of the most easily accessible materials namely Transparent LDPE (125 Microns), Black LDPE (150 Microns) and Tarpaulin HDPE (250 Microns) were acquired and utilized in fabricating the body of Vine Robot. Under the application of these materials, difference in various properties of the robot were studied. By systematic study, the suitability of one material over the other was determined in specific environmental scenarios.

High-stretchability and low-hysteresis strain sensors using origami-inspired 3D mesostructures.

Stretchable strain sensors are essential for various applications such as wearable electronics, prosthetics, and soft robotics. Strain sensors with high strain range, minimal hysteresis, and fast response speed are highly desirable for accurate measurements of large and dynamic deformations of soft bodies. Current stretchable strain sensors mostly rely on deformable conducting materials, which often have difficulties in achieving these properties simultaneously. In this study, we introduce capacitive strain sensor concepts based on origami-inspired three-dimensional mesoscale electrodes formed by a mechanically guided assembly process. These sensors exhibit up to 200% stretchability with 1.2% degree of hysteresis, <22 ms response time, small sensing area (~5 mm2), and directional strain responses. To showcase potential applications, we demonstrate the use of distributed strain sensors for measuring multimodal deformations of a soft continuum arm.

Static Shape Control of Soft Continuum Robots Using Deep Visual Inverse Kinematic Models

Soft continuum robots are highly flexible and adaptable, making them ideal for unstructured environments such as the human body and agriculture. However, their high compliance and maneuverability make them difficult to model, sense, and control. Current control strategies focus on Cartesian space control of the end-effector, but few works have explored full-body control. This study presents a novel image-based deep learning approach for closed-loop kinematic shape control of soft continuum robots. The method combines a local inverse kinematics formulation in the image space with deep convolutional neural networks for accurate shape control that is robust to feedback noise and mechanical changes in the continuum arm. The shape controller is fast and straightforward to implement; it takes only a few hours to generate training data, train the network, and deploy, requiring only a web camera for feedback. This method offers an intuitive and user-friendly way to control the robot's 3-D shape and configuration through teleoperation using only 2-D hand-drawn images of the desired target state without the need for further user instruction or consideration of the robot's kinematics.

Independent and Hybrid Magnetic Manipulation for Full Body Controlled Soft Continuum Robots

Fully soft continuum magnetic (FSCMs) microrobots with highly deformable structures have emerged as a potential solution to robotically controlled endovascular interventions. The microrobot's structure is made of magneto-responsive material, which offers full body control under a magnetic field instead of limited tip deformation. The shape control for these microrobots enables steering in complex paths with limited contact with the environment. We studied full body control under fields generated using up to two robotically controlled permanent magnets and in combination with an electromagnetic system. The effect of different parameters, such as the number of permanent magnets, position of the permanent magnets and intensity and direction of the electromagnetic field, on the robot's shape has been experimentally investigated. A mathematical model to predict tip deformation angle (TDA) was introduced and verified experimentally (root mean square error (RMSE) 6.5). Ten different characteristic body shapes were identified based on the curvature of the soft robot. Based on the obtained data, a proof-of-concept demo is presented for the full body controlled soft robots. The accuracy of steering soft robots on a continuous path with all three magnetic control strategies was investigated. The results show that the proposed method effectively enables shape forming and minimizes contact with the surrounding environment (the average distance to the centerline was 1.24 mm). The proposed approach hold promises to elevate endovascular interventions towards the least invasive surgery.

Closed Loop Static Control of Multi-Magnet Soft Continuum Robots

This paper discusses a novel static control approach applied to magnetic soft continuum robots (MSCRs). Our aim is to demonstrate the control of a multi-magnet soft continuum robot (SCR) in 3D. The proposed controller, based on a simplified yet accurate model of the robot, has a high update rate and is capable of real-time shape control. For the actuation of the MSCR, we employ the dual external permanent magnet (dEPM) platform and we sense the shape via fiber Bragg grating (FBG). The employed actuation system and sensing technique makes the proposed approach directly applicable to the medical context. We demonstrate that the proposed controller, running at approximately 300 Hz, is capable of shape tracking with a mean error of 8.5% and maximum error of 35.2% . We experimentally show that the static controller is 25.9% more accurate than a standard PID controller in shape tracking and is able to reduce the maximum error by 59.2%.

A meshfree model of hard-magnetic soft materials

Hard-magnetic soft materials comprising a soft matrix embedded with hard-magnetic particles can exhibit large deformations under external magnetic fields, which have attracted much interest due to their non-contact activation, flexible programmability, and rapid response in various applications such as biomedical devices, soft robotics and flexible electronics. Precise predictions of large deformations of hard-magnetic soft materials would be a key for relevant applications. Given that the rational designs in the programmable shape morphing of ferromagnetic structures requires high precision, here we develop a meshfree model based on the radial point interpolation method to quantitatively predict large deformations of hard-magnetic soft materials. We apply the stabilized conforming nodal integration instead of direct nodal integration to eliminate the influence of zero-energy mode, and the central difference scheme is implemented for the resolution procedure. We explore the bending of a hard-magnetic beam and snap buckling of a bistable hard-magnetic beam under external magnetic stimuli. Uniform and non-uniform discretizations are compared to show the insensitivity of the radial point interpolation method to nodal mesh. To demonstrate the versatile programmability of our model in the design of magnetic robotics, we further simulate complex locomotion (crawling, walking and rolling) of hard-magnetic soft robots under external magnetic excitation. The results could be used to guide rational designs of ferromagnetic structures and soft continuum robots.

Design and performance of a slender soft continuum manipulator for tall structure inspection

PurposeThe current methods for inspecting tall or deep structures such as towers, chimneys, silos, and wells suffer from certain constraints. Manual and assisted inspection methods including humans, drones, wall climbing robots, and others are either costly, have a limited operation time, or affected by field conditions, such as temperature and radiation. This study aims to overcome the presented challenges through a teleoperated soft continuum manipulator capable of inspecting tall or deep structures with high resolution, an unlimited operation time and the ability to use different arms of the manipulator for different environments and structure sizes.Design/methodology/approachThe teleoperated manipulator uses one rotary and two tendon actuators to reach and inspect the interior of a tall (or deep) structure. A sliding part along the manipulator’s body (arm constrainer and tendon router) induces a variable-length bending segment, allowing an inspection camera to be placed at different distances from the desired location.FindingsThe experiments confirmed the manipulator’s ability to inspect different locations in the structure’s interior. The manipulator also demonstrated a submillimeter motion resolution vertically and a 2.5 mm per step horizontally. The inspection time of the full structure was 48.53 min in the step-by-step mode and was calculated to be 4.23 min in the continuous mode.Originality/valueThe presented manipulator offers several design novelties: the arm’s thin-wide cross-section, the variable-length bending segment in a fixed-length body, the external rolling tendon routing and the ability to easily replace the arm with another of different material or dimensions to suite different structures and environments.