Electrical Impedance Tomography and Neural Networks for Shape Sensing in Soft Continuum Endoscopic Robots

Numerous soft and continuum robotic manipulators have demonstrated their potential for compliant operation in highly unstructured environments or near people. Despite their recent popularity, modeling of their smooth bending deformation remains a challenge. For soft continuum manipulators, the widespread, constant curvature approach to modeling is inadequate for modeling some deformations that occur in practice, such as combined bending and twisting deformations. In this paper, we extend the classical Cosserat rod approach to model a variable-length, pneumatic soft continuum arm. We model the deformation of a pneumatically driven soft continuum manipulator, and the model is then compared against experimental data collected from a three degree of freedom, pneumatically actuated, soft continuum manipulator. The model shows good agreement in capturing the overall behavior of the bending deformation, with mean Euclidean error at the tip of the robot of 2.48 cm for a 22 cm long robot. In addition, the model shows good numerical stability for simulating long duration computations.

The imitation of the functions of soft animals has always been the goal of soft robotic design. However, most of the current soft terrestrial robots just imitate the basic gaits and are difficult to perform in three dimensional space. In this work, we present an inchworm-inspired soft robot that integrates the locomotion and manipulation functions together. The soft robot is able to move in the vertical plane from one pole to another or work as a soft manipulator. The two functions can switch between each other naturally. Besides, the soft robot is unfettered by gravity, which greatly expands the applications of the soft robot. The deformation and load capacities of the soft components were tested, and to enhance the stiffness of the whole soft robot, the rigid-frame reinforcement was used. The performance of the soft robot shows great potential of actual applications, such as spatial maintenance and agricultural picking. Most importantly, the combination of the locomotion and manipulation functions provides a new idea for designing more applicable soft robots.

This paper introduces and validates a real-time dynamic predictive model based on a neural network approach for soft continuum manipulators. The presented model provides a real-time prediction framework using neural-network-based strategies and continuum mechanics principles. A time-space integration scheme is employed to discretize the continuous dynamics and decouple the dynamic equations for translation and rotation for each node of a soft continuum manipulator. Then the resulting architecture is used to develop distributed prediction algorithms using recurrent neural networks. The proposed RNN-based parallel predictive scheme does not rely on computationally intensive algorithms; therefore, it is useful in real-time applications. Furthermore, simulations are shown to illustrate the approach performance on soft continuum elastica, and the approach is also validated through an experiment on a magnetically-actuated soft continuum manipulator. The results demonstrate that the presented model can outperform classical modeling approaches such as the Cosserat rod model while also shows possibilities for being used in practice.

<p>Soft robots have become important members of the robot community with many potential applications owing to their unique flexibility and security embedded at the material level. An increasing number of researchers are interested in their designing, manufacturing, modeling, and control. However, the dynamic simulation of soft robots is difficult owing to their infinite degrees of freedom and nonlinear characteristics that are associated with soft materials and flexible geometric structures. In this study, a novel multi-flexible body dynamic modeling and simulation technique is introduced for soft robots. Various actuators for soft robots are modeled in a virtual environment, including soft cable-driven, spring actuation, and pneumatic driving. A pneumatic driving simulation was demonstrated by the bending modules with different materials. A cable-driven soft robot arm prototype and a cylindrical soft module actuated by shape memory alley springs inspired by an octopus were manufactured and used to validate the simulation model, and the experimental results demonstrated adequate accuracy. The proposed technique can be widely applied for the modeling and dynamic simulation of other soft robots, including hybrid actuated robots and rigid-flexible coupling robots. This study also provides a fundamental framework for simulating soft mobile robots and soft manipulators in contact with the environment.</p> <p> </p>

Planar soft space robotic manipulators: Dynamic modeling and control

Traditionally, any new development in soft robotics has to come first through many trial-and-error experiments. Many researchers tried to develop analytical models either to study certain phenomena in an already existed deigns or to use them to enhance soft robots performance. However, there is no general methodology that can be used to design any arbitrary soft robot from scratch. Such methodology should have the ability to test different operation scenarios for checking certain control approaches or certain types of sensors and actuators. This paper introduces a general tool for developing soft surgical manipulators, which is based on transient finite element analysis (FEA). To verify the feasibility of the proposed approach, experiments have been conducted on a fabricated soft manipulator. The test setup incorporates pressure sensors and vision tracking system to record the manipulator trajectory. Air pressure measurements are used as input to FEA for transient simulation. The experimental results demonstrate high level of agreement with FEA results and prove that the suggested approach is promising to be used in designing new soft surgical robots.

Soft robotic systems offer benefits over traditional rigid systems through reduced contact trauma with soft tissues and by enabling access through tortuous paths in minimally invasive surgery. However, the inherent deformability of soft robots places both a greater onus on accurate modelling of their shape, and greater challenges in realising intraoperative shape sensing. Herein we present a proprioceptive (self-sensing) soft actuator, with an electrically conductive working fluid. Electrical impedance measurements from up to six electrodes enabled tomographic reconstructions using Electrical Impedance Tomography (EIT). A new Frequency Division Multiplexed (FDM) EIT system was developed capable of measurements of 66 dB SNR with 20 ms temporal resolution. The concept was examined in two two-degree-of-freedom designs: a hydraulic hinged actuator and a pneumatic finger actuator with hydraulic beams. Both cases demonstrated that impedance measurements could be used to infer shape changes, and EIT images reconstructed during actuation showed distinct patterns with respect to each degree of freedom (DOF). Whilst there was some mechanical hysteresis observed, the repeatability of the measurements and resultant images was high. The results show the potential of FDM-EIT as a low-cost, low profile shape sensor in soft robots.

Soft robots have been attracting much research attention in the past decades owing to their unique properties that are inherently different from conventional rigid robots. The intrinsic characteristics of soft robots, such as compliance and flexibility, have made them a promising supplement for many existing robotic applications. For instance, soft robots provide safer human-machine interaction and better adaptability to unexplored environments than the conventional rigid ones. However, their “soft” feature would conversely cause complex material deformation due to their high-DoF nature, which has made their kinematic or dynamic models more difficult to deduce. Logically, the process of designing model-based controllers would therefore suffer from the lack of efficient and accurate modeling methods. In this letter, through adopting Finite Element Method (FEM), we demonstrate a FEM-based nonlinear control method for a soft trunk robot to achieve trajectory tracking objectives. The proposed controller is developed by utilizing the essential information from the dynamic FE model of the studied robot. The details of deriving this nonlinear control method are demonstrated, then followed by 4 practical experiments on the soft truck robot to verify the feasibility of the control method. The corresponding experimental results are presented accordingly.

Soft robots equipped with multifunctionalities have been increasingly needed for secure, adaptive, and autonomous functioning in unknown and unpredictable environments. Robotic stacking is a promising solution to increase the functional diversity of soft robots, which are required for safe human-machine interactions and adapting in unstructured environments. However, most existing multifunctional soft robots have a limited number of functions or have not fully shown the superiority of the robotic stacking method. In this study, we present a novel robotic stacking strategy, Netting-Rolling-Splicing (NRS) stacking, based on a dimensional raising method via 2D-to-3D rolling-and-splicing of netted stackable pneumatic artificial muscles to quickly and efficiently fabricate multifunctional soft robots based on the same, simple, and cost-effective elements. To demonstrate it, we developed a TriUnit robot that can crawl 0.46 ± 0.022 body length per second (BL/s) and climb 0.11 BL/s, and can carry a 3 kg payload while climbing. Also, the TriUnit can be used to achieve novel omnidirectional pipe climbing including rotating climbing, and conduct bionic swallowing-and-regurgitating, multi-degree-of-freedom manipulation based on their multimodal combinations. Apart from these, steady rolling, with a speed of 0.19 BL/s, can be achieved by using a pentagon unit. Furthermore, we applied the TriUnit pipe climbing robot in panoramic shooting and cargo transferring to demonstrate the robot's adaptability for different tasks. The NRS stacking-driven soft robot here has demonstrated the best overall performance among existing stackable soft robots, representing a new and effective way for building multifunctional and multimodal soft robots in a cost-effective and efficient way.

In this paper, the design and testing of a novel valve for the intuitive spatial control of soft or continuum manipulators are presented. The design of the valve is based on the style of a hydraulic flapper valve, but with simultaneous control of three pressure feed points, which can be used to drive three antagonistically arranged hydraulic actuators for positioning soft robots. The variable control orifices are arranged in a rotationally symmetric radial pattern to allow for an inline mounting configuration of the valve within the body of a manipulator. Positioning the valve ring at various 3D configurations results in different pressurizations of the actuators and corresponding spatial configurations of the manipulator. The design of the valve is suitable for miniaturization and use in applications with size constraints such as small soft manipulators and surgical robotics. Experimental validation showed that the performance of the valve can be reasonably modeled and can effectively drive an antagonistic arrangement of three actuators for soft manipulator control.

Soft robots are concerned by researchers due to several characteristics, such as high adaptability in complex unstructured environments, safe interaction with environments, and a high degree of dexterity. The deformability and dexterity of the octopus arm are remarkable and interesting for the development of bioinspiration soft robots. In this study, we proposed a biomimetic flexible manipulator actuated by shape memory alloy (SMA) wires and its PI controller, and evaluated its performances with focused experiments. This study aims to find out the advantages of the developed manipulator actuated by SMA wires. We designed the bionic structure to achieve flexible bending in 3D space based on the analysis of the octopus muscle structure. Then we built the mathematical models that described the relationship between the bending angle and the driving parameters. Surprisingly, the soft movement capacity of the bionic manipulator was investigated at different heating voltages and PWM duty cycles by the PI controller based on the self-sensing resistance feedback. The soft manipulator was able to bend flexibly with the maximum bending angle of 60°. Compared to the traditional soft arm, the soft manipulator has higher accuracy, with the error of the deflection angle less than 5° and the errors of bending angles less than 2°.

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.

Soft robots are increasingly being explored and developed in various settings that demand safe and adaptable interactions between robots and their environments. In addition, soft robots exhibit passive compliant behavior and generate continuous deformations when engaging with the environment. This imposes challenges on achieving active, on-demand interaction force control, especially when feedback force-sensing devices are not available. Consequently, there is a need to explore new model-based force control paradigms for soft robots. In this article, we propose a (quasi-)static force control approach for soft robots based on compliance modeling, avoiding the necessity for feedback control loops or extensive training data collection. The proposed approach can deliver contact force control along three Cartesian axes when the robot is actuated into various configurations. The compliance matrix is derived from the robot configuration, which allows the calculation of desired deflection displacements needed to generate on-demand forces. The resulting force control is achieved by solving inverse kinematics problems based on these deflection displacements. The efficacy of our proposed controller is validated through experiments with both one- and two-segment pneumatic-driven soft continuum robots. The results demonstrate effective static force control performance, with mean control errors below 5% of the desired peak forces.

This paper presents a robotic manipulation system capable of autonomously positioning a multi-segment soft fluidic elastomer robot in three dimensions. Specifically, we present an extremely soft robotic manipulator morphology that is composed entirely from low durometer elastomer, powered by pressurized air, and designed to be both modular and durable. To understand the deformation of a single arm segment, we develop and experimentally validate a static deformation model. Then, to kinematically model the multi-segment manipulator, we use a piece-wise constant curvature assumption consistent with more traditional continuum manipulators. In addition, we define a complete fabrication process for this new manipulator and use this process to make multiple functional prototypes. In order to power the robot’s spatial actuation, a high capacity fluidic drive cylinder array is implemented, providing continuously variable, closed-circuit gas delivery. Next, using real-time data from a vision system, we develop a processing and control algorithm that generates realizable kinematic curvature trajectories and controls the manipulator’s configuration along these trajectories. Lastly, we experimentally demonstrate new capabilities offered by this soft fluidic elastomer manipulation system such as entering and advancing through confined three-dimensional environments as well as conforming to goal shape-configurations within a sagittal plane under closed-loop control.

The goal of this work is to develop a soft-robotic manipulation system that is capable of autonomous, dynamic, and safe interactions with humans and its environment. First, we develop a dynamic model for a multi-body fluidic elastomer manipulator that is composed entirely from soft rubber and subject to the self-loading effects of gravity. Then, we present a strategy for independently identifying all of the unknown components of the system; these are the soft manipulator, its distributed fluidic elastomer actuators, as well as the drive cylinders that supply fluid energy. Next, using this model and trajectory-optimization techniques we find locally-optimal open-loop policies that allow the system to perform dynamic maneuvers we call grabs. In 37 experimental trials with a physical prototype, we successfully perform a grab 92% of the time. Last, we introduce the idea of static bracing for a soft elastomer arm and discuss how forming environmental braces might be an effective manipulation strategy for this class of robots. By studying such an extreme example of a soft robot, we can begin to solve hard problems inhibiting the mainstream use of soft machines.