A Multi-Curvature Soft Gripper Based on Segmented Variable Stiffness Structure Inspired by Snake Scales

In atypical industrial settings, soft grippers needed to adjust to different object shapes. Existing grabbers typically accommodated only single-curvature, fixed-stiffness objects, restricting their stability and usability. This study presents a design for a finger featuring multi-curvature, incorporating a wedge actuator alongside two variable stiffness units (VSUs) inspired by snake scales. By adjusting the high stiffness and low stiffness states of the variable stiffness element, the local structural stiffness of the finger was changed, thereby granting the gripper capabilities in bending shape control and variable stiffness. A finite element model of the wedge actuator was developed, and the influence of several parameters, including top wall thickness, side wall thickness, transition layer thickness, and sidewall height on bending angle and tip output force was analyzed through an orthogonal experiment. Furthermore, the relationship between the longitudinal length of the wedge actuator and both the bending angle and the tip output force was studied. Via explicit dynamic analysis, the stiffness variation of the VSU under operational vacuum pressure was predicted and subsequently validated against experimental data, confirming the reliability of the model. The effectiveness of finger shape control and stiffness adjustment was evaluated through experiments. Ultimately, a two-finger gripper was constructed to carry out the grasping experiments. The results showed that the gripper is capable of generating various clamping curvatures, enabling it to conform closely to the objects it grips and significantly broaden its clamping range.

Exploring Modeling Techniques for Soft Arms: A Survey on Numerical, Analytical, and Data-Driven Approaches

Soft arms, characterized by their compliance and adaptability, have gained significant attention in applications ranging from industrial automation to biomedical fields. Modeling these systems presents unique challenges due to their high degrees of freedom, nonlinear behavior, and complex material properties. This review provides a comprehensive overview of three primary modeling approaches: numerical methods, analytical techniques, and data-driven models. Numerical methods, including finite element analysis and multi-body dynamics, offer precise but computationally expensive solutions for simulating soft arm behaviors. Analytical models, rooted in continuum mechanics and simplified assumptions, provide insights into the fundamental principles while balancing computational efficiency. Data-driven approaches, leveraging machine learning and artificial intelligence, open new avenues for adaptive and real-time modeling by bypassing explicit physical formulations. The strengths, limitations, and application scenarios of each approach are systematically analyzed, and future directions for integrating these methodologies are discussed. This review aims to guide researchers in selecting and developing effective modeling strategies for advancing the field of soft robotic arm design and control.

Magnetic Soft Catheter Robot System for Minimally Invasive Treatments of Articular Cartilage Defects

Articular cartilage defects are among the most common orthopedic diseases, which seriously affect patients' health and daily activities, without prompt treatment. The repair biocarrier-based treatment has shown great promise. Total joint injection and open surgery are two main methods to deliver functional repair biocarriers into the knee joint. However, the exhibited drawbacks of these methods hinder their utility. The repair effect of total joint injection is unstable due to the low targeting rate of the repair biocarriers, whereas open surgery causes serious trauma to patients, thereby prolonging the postoperative healing time. In this study, we develop a magnetic soft catheter robot (MSCR) system to perform precise in situ repair of articular cartilage defects with minimal incision. The MSCR processes a size of millimeters, allowing it to enter the joint cavity through a tiny skin incision to reduce postoperative trauma. Meanwhile, a hybrid control strategy combining neural network and visual servo is applied to sequentially complete the coarse and fine positioning of the MSCR on the cartilage defect sites. After reaching the target, the photosensitive hydrogel is injected and anchored into the defect sites through the MSCR, ultimately completing the in situ cartilage repair. The in vitro and ex vivo experiments were conducted on a 3D printed human femur model and an isolated porcine femur, respectively, to demonstrate the potential of our system for the articular cartilage repair.

A Soft Collaborative Robot for Contact-based Intuitive Human Drag Teaching

Soft material-based robots, known for their safety and compliance, are expected to play an irreplaceable role in human-robot collaboration. However, this expectation is far from real industrial applications due to their complex programmability and poor motion precision, brought by the super elasticity and large hysteresis of soft materials. Here, a soft collaborative robot (Soft Co-bot) with intuitive and easy programming by contact-based drag teaching, and also with exceptional motion repeatability (< 0.30% of body length) and ultra-low hysteresis (< 2.0%) is reported. Such an unprecedented capability is achieved by a biomimetic antagonistic design within a pneumatic soft robot, in which cables are threaded to servo motors through tension sensors to form a self-sensing system, thus providing both precise actuation and dragging-aware collaboration. Hence, the Soft Co-bots can be first taught by human drag and then precisely repeat various tasks on their own, such as electronics assembling, machine tool installation, etc. The proposed Soft Co-bots exhibit a high potential for safe and intuitive human-robot collaboration in unstructured environments, promoting the immediate practical application of soft robots.

Adaptive Online Learning and Robust 3-D Shape Servoing of Continuum and Soft Robots in Unstructured Environments

In this article, we present a novel and generic data-driven method to servo-control the 3-D shape of continuum and soft robots based on proprioceptive sensing feedback. Developments of 3-D shape perception and control technologies are crucial for continuum and soft robots to perform tasks autonomously in surgical interventions. However, owing to the nonlinear properties of continuum robots, one main difficulty lies in the modeling of them, especially for soft robots with variable stiffness. To address this problem, we propose a versatile learning-based adaptive shape controller by leveraging proprioception of 3-D configuration from fiber Bragg grating (FBG) sensors, which can online estimate the unknown model of continuum robot against unexpected disturbances and exhibit an adaptive behavior to the unmodeled system without priori data exploration. Based on a new composite adaptation algorithm, the asymptotic convergences of the closed-loop system with learning parameters have been proven by Lyapunov theory. To validate the proposed method, we present a comprehensive experimental study using two continuum and soft robots both integrated with multicore FBGs, including a robotic-assisted colonoscope and multisection extensible soft manipulators. The results demonstrate the feasibility, adaptability, and superiority of our controller in various unstructured environments, as well as phantom experiments.

Soft robotics for farm to fork: applications in agriculture & farming

Agricultural tasks and environments range from harsh field conditions with semi-structured produce or animals, through to post-processing tasks in food-processing environments. From farm to fork, the development and application of soft robotics offers a plethora of potential uses. Robust yet compliant interactions between farm produce and machines will enable new capabilities and optimize existing processes. There is also an opportunity to explore how modeling tools used in soft robotics can be applied to improve our representation and understanding of the soft and compliant structures common in agriculture. In this review, we seek to highlight the potential for soft robotics technologies within the food system, and also the unique challenges that must be addressed when developing soft robotics systems for this problem domain. We conclude with an outlook on potential directions for meaningful and sustainable impact, and also how our outlook on both soft robotics and agriculture must evolve in order to achieve the required paradigm shift.

Hingeless arm for space robotics actuated through shape memory alloys

Operating outside the spacecraft via remotely controlled structures is an important opportunity in different space applications. The research in this area is focused on designing robots that are sufficiently flexible to allow inspection in locations where access is difficult or impossible for astronauts, while minimizing weight and bulk. The purpose of the research is to design a borescope for space applications with no hinges or other mechanisms, exploiting biomimetic design concepts. This is pursued by giving to the borescope a backbone exoskeleton provided by a continuous structure made of fibre reinforced composite material and using NiTi wires as tendons, taking advantage of their low weight and dimensions, which allow them to be embedded between the composite layers during the lamination process. After a study of the state of the art of flexible structures, concentrated in the medical and robotic fields, the research work unfolded in two phases. In the first design phase, several composite layup solutions were considered and analysed using finite element models, leading to the definition of the borescope geometrical parameters and to an initial estimate of the displacements that can be achieved. In the second experimental phase, seven prototypes were produced and tested, with one or more wires, to validate the design and to search for a configuration that can be actuated in different directions. The borescope prototypes resulted flexible enough to achieve an extended degree of bending and at the same time sufficiently rigid to allow complete rearm of the NiTi wires. The numerical and experimental study led to the definition of the design parameters, the number of wires, and the manufacturing technique to integrate NiTi actuators.

Octopus-inspired sensorized soft arm for environmental interaction

Octopuses can whip their soft arms with a characteristic "bend propagation" motion to capture prey with sensitive suckers. This relatively simple strategy provides models for robotic grasping, controllable with a small number of inputs, and a highly deformable arm with sensing capabilities. Here, we implemented an electronics-integrated soft octopus arm (E-SOAM) capable of reaching, sensing, grasping, and interacting in a large domain. On the basis of the biological bend propagation of octopuses, E-SOAM uses a bending-elongation propagation model to move, reach, and grasp in a simple but efficient way. E-SOAM's distal part plays the role of a gripper and can process bending, suction, and temperature sensory information under highly deformed working states by integrating a stretchable, liquid-metal-based electronic circuit that can withstand uniaxial stretching of 710% and biaxial stretching of 270% to autonomously perform tasks in a confined environment. By combining this sensorized distal part with a soft arm, the E-SOAM can perform a reaching-grasping-withdrawing motion across a range up to 1.5 times its original arm length, similar to the biological counterpart. Through a wearable finger glove that produces suction sensations, a human can use just one finger to remotely and interactively control the robot's in-plane and out-of-plane reaching and grasping both in air and underwater. E-SOAM's results not only contribute to our understanding of the function of the motion of an octopus arm but also provide design insights into creating stretchable electronics-integrated bioinspired autonomous systems that can interact with humans and their environments.

Adaptive control of a soft pneumatic actuator using experimental characterization data

Fiber reinforced soft pneumatic actuators are hard to control due to their non-linear behavior and non-uniformity introduced by the fabrication process. Model-based controllers generally have difficulty compensating non-uniform and non-linear material behaviors, whereas model-free approaches are harder to interpret and tune intuitively. In this study, we present the design, fabrication, characterization, and control of a fiber reinforced soft pneumatic module with an outer diameter size of 12 mm. Specifically, we utilized the characterization data to adaptively control the soft pneumatic actuator. From the measured characterization data, we fitted mapping functions between the actuator input pressures and the actuator space angles. These maps were used to construct the feedforward control signal and tune the feedback controller adaptively depending on the actuator bending configuration. The performance of the proposed control approach is experimentally validated by comparing the measured 2D tip orientation against the reference trajectory. The adaptive controller was able to successfully follow the prescribed trajectory with a mean absolute error of 0.68° for the magnitude of the bending angle and 3.5° for the bending phase around the axial direction. The data-driven control method introduced in this paper may offer a solution to intuitively tune and control soft pneumatic actuators, compensating for their non-uniform and non-linear behavior.

Characterization of continuum robot arms under reinforcement learning and derived improvements

In robotics, soft continuum robot arms are a promising prospect owing to their redundancy and passivity; however, no comprehensive study exists that examines their characteristics compared to rigid manipulators. In this study, we examined the advantages of a continuum robot arm as compared to a typical and rigid seven-degree-of-freedom (7-DoF) robot manipulator in terms of performing various tasks through reinforcement learning. We conducted simulations for tasks with different characteristics that require control over position and force. Common tasks in robot manipulators, such as reaching, crank rotation, object throwing, and peg-in-hole were considered. The initial conditions of the robot and environment were randomized, aiming for evaluations including robustness. The results indicate that the continuum robot arm excels in the crank-rotation task, which is characterized by uncertainty in environmental conditions and cumulative rewards. However, the rigid robot arm learned better motions for the peg-in-hole task than the other tasks, which requires fine motion control of the end-effector. In the throwing task, the continuum robot arm scored well owing to its good handling of anisotropy. Moreover, we developed a reinforcement-learning method based on the comprehensive experimental results. The proposed method successfully improved the motion learning of a continuum robot arm by adding a technique to regulate the initial state of the robot. To the best of our knowledge, ours is the first reinforcement-learning experiment with multiple tasks on a single continuum robot arm and is the first report of a comparison between a single continuum robot arm and rigid manipulator on a wide range of tasks. This simulation study can make a significant contribution to the design of continuum arms and specification of their applications, and development of control and reinforcement learning methods.

Design of a Lightweight and Deployable Soft Robotic Arm

Soft robotics represents a rising trend in recent years, due to the ability to work in unstructured environments or in strict contact with humans. Introducing soft parts, robots can adapt to various contexts overcoming limits relative to the rigid structure of traditional ones. Main issues of soft robotics systems concern the relatively low force exertion and control complexity. Moreover, several fields of application, as space industry, need to develop novel lightweight and deployable robotic systems, that can be stored into a relatively small volume and deployed when required. In this paper, POPUP robot is introduced: a soft manipulator having inflatable links and rigid joints. Its hybrid structure aims to match the advantages of rigid robots and the useful properties of having a lightweight and deployable parts, ensuring simple control, low energy consumption and low compressed gas requirement. The first robot prototype and the system architecture are described highlighting design criteria and effect of internal pressure on the performances. A pseudo-rigid body model is used to describe the behavior of inflatable links looking forward to control design. Finally, the model is extended to the whole robot: multi-body simulations are performed to highlight the importance of suitable sensor equipment for control development, proposing a visual servoing solution.

Review on generic methods for mechanical modeling, simulation and control of soft robots

In this review paper, we are interested in the models and algorithms that allow generic simulation and control of a soft robot. First, we start with a quick overview of modeling approaches for soft robots and available methods for calculating the mechanical compliance, and in particular numerical methods, like real-time Finite Element Method (FEM). We also show how these models can be updated based on sensor data. Then, we are interested in the problem of inverse kinematics, under constraints, with generic solutions without assumption on the robot shape, the type, the placement or the redundancy of the actuators, the material behavior… We are also interested by the use of these models and algorithms in case of contact with the environment. Moreover, we refer to dynamic control algorithms based on mechanical models, allowing for robust control of the positioning of the robot. For each of these aspects, this paper gives a quick overview of the existing methods and a focus on the use of FEM. Finally, we discuss the implementation and our contribution in the field for an open soft robotics research.

Underwater Soft Robotics: A Review of Bioinspiration in Design, Actuation, Modeling, and Control

Nature and biological creatures are some of the main sources of inspiration for humans. Engineers have aspired to emulate these natural systems. As rigid systems become increasingly limited in their capabilities to perform complex tasks and adapt to their environment like living creatures, the need for soft systems has become more prominent due to the similar complex, compliant, and flexible characteristics they share with intelligent natural systems. This review provides an overview of the recent developments in the soft robotics field, with a focus on the underwater application frontier.

A Survey for Machine Learning-Based Control of Continuum Robots

Soft continuum robots have been accepted as a promising category of biomedical robots, accredited to the robots’ inherent compliance that makes them safely interact with their surroundings. In its application of minimally invasive surgery, such a continuum concept shares the same view of robotization for conventional endoscopy/laparoscopy. Different from rigid-link robots with accurate analytical kinematics/dynamics, soft robots encounter modeling uncertainties due to intrinsic and extrinsic factors, which would deteriorate the model-based control performances. However, the trade-off between flexibility and controllability of soft manipulators may not be readily optimized but would be demanded for specific kinds of modeling approaches. To this end, data-driven modeling strategies making use of machine learning algorithms would be an encouraging way out for the control of soft continuum robots. In this article, we attempt to overview the current state of kinematic/dynamic model-free control schemes for continuum manipulators, particularly by learning-based means, and discuss their similarities and differences. Perspectives and trends in the development of new control methods are also investigated through the review of existing limitations and challenges.

Visual Servoing Pushing Control of the Soft Robot with Active Pushing Force Regulation

Soft robots characterize operational safety due to inherent compliance from their soft mechanism, whereas their mechanism enhances the difficulty in accurate closed-loop control. To explore their applicability in manipulation tasks, in this article, we propose a visual servoing pushing controller considering the effect of contact. The controller is designed to simultaneously complete the positioning and manipulation tasks. To further improve the manipulation performance, an active pushing force regulation method is proposed and integrated into the controller. The proposed control algorithm is validated experimentally. The results show that the controller guarantees the convergence to the image error and meanwhile, it improves the pushing manipulation performance.

Bio-Inspired Soft Grippers Based on Impactive Gripping

Grasping and manipulation are fundamental ways for many creatures to interact with their environments. Different morphologies and grasping methods of "grippers" are highly evolved to adapt to harsh survival conditions. For example, human hands and bird feet are composed of rigid frames and soft joints. Compared with human hands, some plants like Drosera do not have rigid frames, so they can bend at arbitrary points of the body to capture their prey. Furthermore, many muscular hydrostat animals and plant tendrils can implement more complex twisting motions in 3D space. Recently, inspired by the flexible grasping methods present in nature, increasingly more bio-inspired soft grippers have been fabricated with compliant and soft materials. Based on this, the present review focuses on the recent research progress of bio-inspired soft grippers based on impactive gripping. According to their types of movement and a classification model inspired by biological "grippers", soft grippers are classified into three types, namely, non-continuum bending-type grippers, continuum bending-type grippers, and continuum twisting-type grippers. An exhaustive and updated analysis of each type of gripper is provided. Moreover, this review offers an overview of the different stiffness-controllable strategies developed in recent years.

Characterizing Continuous Manipulation Families for Dexterous Soft Robot Hands

There has been an explosion of ideas in soft robotics over the past decade, resulting in unprecedented opportunities for end effector design. Soft robot hands offer benefits of low-cost, compliance, and customized design, with the promise of dexterity and robustness. The space of opportunities is vast and exciting. However, new tools are needed to understand the capabilities of such manipulators and to facilitate manipulation planning with soft manipulators that exhibit free-form deformations. To address this challenge, we introduce a sampling based approach to discover and model continuous families of manipulations for soft robot hands. We give an overview of the soft foam robots in production in our lab and describe novel algorithms developed to characterize manipulation families for such robots. Our approach consists of sampling a space of manipulation actions, constructing Gaussian Mixture Model representations covering successful regions, and refining the results to create continuous successful regions representing the manipulation family. The space of manipulation actions is very high dimensional; we consider models with and without dimensionality reduction and provide a rigorous approach to compare models across different dimensions by comparing coverage of an unbiased test dataset in the full dimensional parameter space. Results show that some dimensionality reduction is typically useful in populating the models, but without our technique, the amount of dimensionality reduction to use is difficult to predict ahead of time and can depend on the hand and task. The models we produce can be used to plan and carry out successful, robust manipulation actions and to compare competing robot hand designs.

3D Printing Materials for Soft Robotics

Soft robotics is a growing field of research, focusing on constructing motor-less robots from highly compliant materials, some are similar to those found in living organisms. Soft robotics has a high potential for applications in various fields such as soft grippers, actuators, and biomedical devices. 3D printing of soft robotics presents a novel and promising approach to form objects with complex structures, directly from a digital design. Here, recent developments in the field of materials for 3D printing of soft robotics are summarized, including high-performance flexible and stretchable materials, hydrogels, self-healing materials, and shape memory polymers, as well as fabrication of all-printed robots (multi-material printing, embedded electronics, untethered and autonomous robotics). The current challenges in the fabrication of 3D printed soft robotics, including the materials available and printing abilities, are presented and the recent activities addressing these challenges are also surveyed.

Fault-Tolerant Six-DoF Pose Estimation for Tendon-Driven Continuum Mechanisms

We propose a fault-tolerant estimation technique for the six-DoF pose of a tendon-driven continuum mechanisms using machine learning. In contrast to previous estimation techniques, no deformation model is required, and the pose prediction is rather performed with polynomial regression. As only a few datapoints are required for the regression, several estimators are trained with structured occlusions of the available sensor information, and clustered into ensembles based on the available sensors. By computing the variance of one ensemble, the uncertainty in the prediction is monitored and, if the variance is above a threshold, sensor loss is detected and handled. Experiments on the humanoid neck of the DLR robot DAVID, demonstrate that the accuracy of the predicted pose is significantly improved, and a reliable prediction can still be performed using only 3 out of 8 sensors.

Research and analysis of stiffness-enhanced soft gripper

In view of the problems of low stiffness, small driving force and large balloon effect existing in the current soft actuator, this paper proposes an optimization method to enhance the overall stiffness of the soft gripper by using rigid components based on the multi-cavity soft pneumatic actuator. This paper introduces the main components of the actuator: the soft part poured by liquid silica gel, and the open rectangular rigid structures by 3D printed. The kinematics model of the finger is established based on the Piecewise Constant Curvature model(PCC). The bending performance of the enhanced stiffness gripper is verified by finite element analysis(FEA): the tip force of actuator increased with the increase of the number of rigid structures when the bending angle is constant. According to the and experimental data, the overall stiffness of soft gripper is increased by the rigid structure without affecting the flexibility of operation. And the maximum weight which can grasp is 3.4 times that of the traditional soft gripper, improved the grasping range of the soft gripper effectively.

Review of machine learning methods in soft robotics

Soft robots have been extensively researched due to their flexible, deformable, and adaptive characteristics. However, compared to rigid robots, soft robots have issues in modeling, calibration, and control in that the innate characteristics of the soft materials can cause complex behaviors due to non-linearity and hysteresis. To overcome these limitations, recent studies have applied various approaches based on machine learning. This paper presents existing machine learning techniques in the soft robotic fields and categorizes the implementation of machine learning approaches in different soft robotic applications, which include soft sensors, soft actuators, and applications such as soft wearable robots. An analysis of the trends of different machine learning approaches with respect to different types of soft robot applications is presented; in addition to the current limitations in the research field, followed by a summary of the existing machine learning methods for soft robots.

How to Model Tendon-Driven Continuum Robots and Benchmark Modelling Performance

Tendon actuation is one of the most prominent actuation principles for continuum robots. To date, a wide variety of modelling approaches has been derived to describe the deformations of tendon-driven continuum robots. Motivated by the need for a comprehensive overview of existing methodologies, this work summarizes and outlines state-of-the-art modelling approaches. In particular, the most relevant models are classified based on backbone representations and kinematic as well as static assumptions. Numerical case studies are conducted to compare the performance of representative modelling approaches from the current state-of-the-art, considering varying robot parameters and scenarios. The approaches show different performances in terms of accuracy and computation time. Guidelines for the selection of the most suitable approach for given designs of tendon-driven continuum robots and applications are deduced from these results.

Reality-Assisted Evolution of Soft Robots through Large-Scale Physical Experimentation: A Review

We introduce the framework of reality-assisted evolution to summarize a growing trend towards combining model-based and model-free approaches to improve the design of physically embodied soft robots. In silico, data-driven models build, adapt, and improve representations of the target system using real-world experimental data. By simulating huge numbers of virtual robots using these data-driven models, optimization algorithms can illuminate multiple design candidates for transference to the real world. In reality, large-scale physical experimentation facilitates the fabrication, testing, and analysis of multiple candidate designs. Automated assembly and reconfigurable modular systems enable significantly higher numbers of real-world design evaluations than previously possible. Large volumes of ground-truth data gathered via physical experimentation can be returned to the virtual environment to improve data-driven models and guide optimization. Grounding the design process in physical experimentation ensures that the complexity of virtual robot designs does not outpace the model limitations or available fabrication technologies. We outline key developments in the design of physically embodied soft robots in the framework of reality-assisted evolution.

Hierarchical control of soft manipulators towards unstructured interactions

Performing daily interaction tasks such as opening doors and pulling drawers in unstructured environments is a challenging problem for robots. The emergence of soft-bodied robots brings a new perspective to solving this problem. In this paper, inspired by humans performing interaction tasks through simple behaviors, we propose a hierarchical control system for soft arms, in which the low-level controller achieves motion control of the arm tip, the high-level controller controls the behaviors of the arm based on the low-level controller, and the top-level planner chooses what behaviors should be taken according to tasks. To realize the motion control of the soft arm in interacting with environments, we propose two control methods. The first is a feedback control method based on a simplified Jacobian model utilizing the motion laws of the soft arm that are not affected by environments during interaction. The second is a control method based on [Formula: see text]-learning, in which we present a novel method to increase training data by setting virtual goals. We implement the hierarchical control system on a platform with the Honeycomb Pneumatic Networks Arm (HPN Arm) and validate the effectiveness of this system on a series of typical daily interaction tasks, which demonstrates this proposed hierarchical control system could render the soft arms to perform interaction tasks as simply as humans, without force sensors or accurate models of the environments. This work provides a new direction for the application of soft-bodied arms and offers a new perspective for the physical interactions between robots and environments.

Recent Advances in Design and Actuation of Continuum Robots for Medical Applications

Traditional rigid robot application in the medical field is limited due to the limited degrees of freedom caused by their material and structure. Inspired by trunk, tentacles, and snakes, continuum robot (CR) could traverse confined space, manipulate objects in complex environment, and conform to curvilinear paths in space. The continuum robot has broad prospect in surgery due to its high dexterity, which can reach circuitous areas of the body and perform precision surgery. Recently, many efforts have been done by researchers to improve the design and actuation methods of continuum robots. Several continuum robots have been applied in clinic surgical interventions and demonstrated superiorities to conventional rigid-link robots. In this paper, we provide an overview of the current development of continuum robots, including the design principles, actuation methods, application prospect, limitations, and challenge. And we also provide perspective for the future development. We hope that with the development of material science, Engineering ethics, and manufacture technology, new methods can be applied to manufacture continuum robots for specific surgical procedures.

Model-free motion control of continuum robots based on a zeroing neurodynamic approach

As a result of inherent flexibility and structural compliance, continuum robots have great potential in practical applications and are attracting more and more attentions. However, these characteristics make it difficult to acquire the accurate kinematics of continuum robots due to uncertainties, deformation and external loads. This paper introduces a method based on a zeroing neurodynamic approach to solve the trajectory tracking problem of continuum robots. The proposed method can achieve the control of a bellows-driven continuum robot just relying on the actuator input and sensory output information, without knowing any information of the kinematic model. This approach reduces the computational load and can guarantee the real time control. The convergence, stability, and robustness of the proposed approach are proved by theoretical analyses. The effectiveness of the proposed method is verified by simulation studies including tracking performance, comparisons with other three methods, and robustness tests.

A learning-based harmonic mapping: Framework, assessment, and case study of human-to-robot hand pose mapping

Harmonic mapping provides a natural way of mapping two manifolds by minimizing distortion induced by the mapping. However, most applications are limited to mapping between 2D and/or 3D spaces owing to the high computational cost. We propose a novel approach, the harmonic autoencoder (HAE), by approximating a harmonic mapping in a data-driven way. The HAE learns a mapping from an input domain to a target domain that minimizes distortion and requires only a small number of input–target reference pairs. The HAE can be applied to high-dimensional applications, such as human-to-robot hand pose mapping. Our method can map from the input to the target domain while minimizing distortion over the input samples, covering the target domain, and satisfying the reference pairs. This is achieved by extending an existing neural network method called the contractive autoencoder. Starting from a contractive autoencoder, the HAE takes into account a distance function between point clouds within the input and target domains, in addition to a penalty for estimation error on reference points. For efficiently selecting a set of input–target reference pairs during the training process, we introduce an adaptive optimization criterion. We demonstrate that pairs selected in this way yield a higher-performance mapping than pairs selected randomly, and the mapping is comparable to that from pairs selected heuristically by the experimenter. Our experimental results with synthetic data and human-to-robot hand pose data demonstrate that our method can learn an effective mapping between the input and target domains.

Controlling of Pneumatic Muscle Actuator Systems by Parallel Structure of Neural Network and Proportional Controllers (PNNP)

This article proposed a novel controller structure to track the non-linear behavior of the pneumatic muscle actuator (PMA), such as the elongation for the extensor actuator and bending for the bending PMA. The proposed controller consists of a neural network (NN) controller laid in parallel with the proportional controller (P). The parallel neural network proportional (PNNP) controllers provide a high level of precision and fast-tracking control system. The PNNP has been applied to control the length of the single extensor PMA and the bending angle of the single self-bending contraction actuator (SBCA) at different load values. For further validation, the PNNP has been applied to control a human-robot shared control system. The results show the efficiency of the proposed controller structure.

Biomedical soft robots: current status and perspective

This paper reviews the current status of soft robots in biomedical field. Soft robots are made of materials that have comparable modulus of elasticity to that of biological systems. Several advantages of soft robots over rigid robots are safe human interaction, ease of adaptation with wearable electronics and simpler gripping. We review design factors of soft robots including modeling, controls, actuation, fabrication and application, as well as their limitations and future work. For modeling, we survey kinematic, multibody and numerical finite element methods. Finite element methods are better suited for the analysis of soft robots, since they can accurately model nonlinearities in geometry and materials. However, their real-time integration with controls is challenging. We categorize the controls of soft robots as model-based and model-free. Model-free controllers do not rely on an explicit analytical or numerical model of the soft robot to perform actuation. Actuation is the ability to exert a force using actuators such as shape memory alloys, fluid gels, elastomers and piezoelectrics. Nonlinear geometry and materials of soft robots restrict using conventional rigid body controls. The fabrication techniques used for soft robots differ significantly from that of rigid robots. We survey a wide range of techniques used for fabrication of soft robots from simple molding to more advanced additive manufacturing methods such as 3D printing. We discuss the applications and limitations of biomedical soft robots covering aspects such as functionality, ease of use and cost. The paper concludes with the future discoveries in the emerging field of soft robots.

Continuum Robots for Manipulation Applications: A Survey

This paper presents a literature survey documenting the evolution of continuum robots over the past two decades (1999–present). Attention is paid to bioinspired soft robots with respect to the following three design parameters: structure, materials, and actuation. Using this three-faced prism, we identify the uniqueness and novelty of robots that have hitherto not been publicly disclosed. The motivation for this study comes from the fact that continuum soft robots can make inroads in industrial manufacturing, and their adoption will be accelerated if their key advantages over counterparts with rigid links are clear. Four different taxonomies of continuum robots are included in this study, enabling researchers to quickly identify robots of relevance to their studies. The kinematics and dynamics of these robots are not covered, nor is their application in surgical manipulation.

Toward Task Autonomy in Robotic Cardiac Ablation: Learning-Based Kinematic Control of Soft Tendon-Driven Catheters

The goal of this study was to propose and validate a control framework with level-2 autonomy (task autonomy) for the control of flexible ablation catheters. To this end, a kinematic model for the flexible portion of typical ablation catheters was developed and a 40-mm-long spring-loaded flexible catheter was fabricated. The feasible space of the catheter was obtained experimentally. Furthermore, a robotic catheter intervention system was prototyped for controlling the length of the catheter tendons. The proposed control framework used a support vector machine classifier to determine the tendons to be driven, and a fully connected neural network regressor to determine the length of the tendons. The classifier and regressors were trained with the data from the feasible space. The control system was implemented in parallel at user-interface and firmware and exhibited a 0.4-s lag in following the input. The validation studies were four trajectory tracking and four target reaching experiments. The system was capable of tracking trajectories with an error of 0.49 ± 0.32 and 0.62 ± 0.36 mm in slow and fast trajectories, respectively. Also, it exhibited submillimeter accuracy in reaching three preplanned targets and ruling out one nonfeasible target autonomously. The results showed improved accuracy and repeatability of the position control compared with the recent literature. The proposed learning-based approach could be used in enabling task autonomy for catheter-based ablation therapies.

A soft manipulator for efficient delicate grasping in shallow water: Modeling, control, and real-world experiments

Collecting in shallow water (water depth: ~30 m) is an emerging field that requires robotics for replacing human divers. Soft robots have several promising features (e.g., safe interaction with the environments, lightweight, etc.) for performing such tasks. In this article, we developed an underwater robotic system with a three-degree-of-freedom (3-DoF) soft manipulator for spatial delicate grasping in shallow water. First, we present the design and fabrication of the soft manipulator with an opposite-bending-and-stretching structure (OBSS). Then, we proposed a simple and efficient kinematics method for controlling the spatial location and trajectory of the soft manipulator’s end effector. The inverse kinematics of the OBSS manipulator can be solved efficiently (computation time: 8.2 ms). According to this inverse kinematics method, we demonstrated that the OBSS soft manipulator could track complex two-dimensional and three-dimensional trajectories, including star, helix, etc. Further, we performed real-time closed-loop pick-and-place experiments of the manipulator with binocular and on-hand cameras in a lab aquarium. Hydrodynamic experiments showed that the OBSS soft manipulator produced little force (less than 0.459 N) and torque (less than 0.228 N·m), which suggested its low-inertia feature during the underwater operation. Finally, we demonstrated that the underwater robotic system with the OBSS soft manipulator successfully collected seafood animals at the bottom of the natural oceanic environment. The robot successfully collected eight sea echini and one sea cucumber within 20 minutes at a water depth of around 10 m.

Robust control of a silicone soft robot using neural networks

This paper deals with the robust controller design problem to regulate the position of a soft robot with elastic behavior, driven by 4 cable actuators. In this work, we first used an artificial neural network to approximate the relation between these actuators and the controlled position of the soft robot, based on which two types of robust controllers (type of integral and sliding mode) are proposed. The effectiveness and the robustness of the proposed controllers have been analyzed both for the constant and the time-varying disturbances. The performances (precision, convergence speed and robustness) of the proposed method have been validated via different experimental tests.

Learning the Complete Shape of Concentric Tube Robots

Concentric tube robots, composed of nested pre-curved tubes, have the potential to perform minimally invasive surgery at difficult-to-reach sites in the human body. In order to plan motions that safely perform surgeries in constrained spaces that require avoiding sensitive structures, the ability to accurately estimate the entire shape of the robot is needed. Many state-of-the-art physics-based shape models are unable to account for complex physical phenomena and subsequently are less accurate than is required for safe surgery. In this work, we present a learned model that can estimate the entire shape of a concentric tube robot. The learned model is based on a deep neural network that is trained using a mixture of simulated and physical data. We evaluate multiple network architectures and demonstrate the model's ability to compute the full shape of a concentric tube robot with high accuracy.

Design, implementation, and control of a deformable manipulator robot based on a compliant spine

This article presents the conception, the numerical modeling, and the control of a dexterous, deformable manipulator bio-inspired by the skeletal spine found in vertebrate animals. Through the implementation of this new manipulator, we show a methodology based on numerical models and simulations, that goes from design to control of continuum and soft robots. The manipulator is modeled using a finite element method (FEM), using a set of beam elements that reproduce the lattice structure of the robot. The model is computed and inverted in real-time using optimization methods. A closed-loop control strategy is implemented to account for the disparities between the model and the robot. This control strategy allows for accurate positioning, not only of the tip of the manipulator, but also the positioning of selected middle points along its backbone. In a scenario where the robot is piloted by a human operator, the command of the robot is enhanced by a haptic loop that renders the boundaries of its task space as well as the contact with its environment. The experimental validation of the model and control strategies is also presented in the form of an inspection task use case.

Design and Control of Foam Hands for Dexterous Manipulation

There has been great progress in soft robot design, manufacture, and control in recent years, and soft robots are a tool of choice for safe and robust handling of objects in conditions of uncertainty. Still, dexterous in-hand manipulation using soft robots remains a challenge. This paper introduces foam robot hands actuated by tendons sewn through a fabric glove. The flexibility of tendon actuation allows for high competence in utilizing deformation for robust in-hand manipulation. We discuss manufacturing, control, and design optimization for foam robots and demonstrate robust grasping and in-hand manipulation on a variety of different physical hand prototypes.

Comparison and experimental validation of predictive models for soft, fiber-reinforced actuators

Successful soft robot modeling approaches appearing in the recent literature have been based on a variety of distinct theories, including traditional robotic theory, continuum mechanics, and machine learning. Though specific modeling techniques have been developed for and validated against already realized systems, their strengths and weaknesses have not been explicitly compared against each other. In this article, we show how three distinct model structures, a lumped-parameter model, a continuum mechanical model, and a neural network, compare in capturing the gross trends and specific features of the force generation of soft robotic actuators. In particular, we study models for fiber-reinforced elastomeric enclosures (FREEs), which are a popular choice of soft actuator and that are used in several soft articulated systems, including soft manipulators, exoskeletons, grippers, and locomoting soft robots. We generated benchmark data by testing eight FREE samples that spanned broad design and kinematic spaces and compared the models on their ability to predict the loading–deformation relationships of these samples. This comparison shows the predictive capabilities of each model on individual actuators and each model’s generalizability across the design space. While the neural net achieved the highest peak performance, the first principles-based models generalized best across all actuator design parameters tested. The results highlight the essential roles of mathematical structure and experimental parameter determination in building high-performing, generalizable soft actuator models with varying effort invested in system identification.

An Opposite-Bending-and-Extension Soft Robotic Manipulator for Delicate Grasping in Shallow Water

Collecting seafood animals (such as sea cucumbers, sea echini, scallops, etc.) cultivated in shallow water (water depth: ~30 m) is a profitable and an emerging field that requires robotics for replacing human divers. Soft robotics have several promising features (e.g., safe contact with the objects, lightweight, etc.) for performing such a task. In this paper, we implement a soft manipulator with an opposite-bending-and-extension structure. A simple and rapid inverse kinematics method is proposed to control the spatial location and trajectory of the underwater soft manipulator's end effector. We introduce the actuation hardware of the prototype, and then characterize the trajectory and workspace. We find that the prototype can well track fundamental trajectories such as a line and an arc. Finally, we construct a small underwater robot and demonstrate that the underwater soft manipulator successfully collects multiple irregular shaped seafood animals of different sizes and stiffness at the bottom of the natural oceanic environment (water depth: ~10 m).