Continuum Arm Research Articles

Design and performance analysis of continuum manipulator with twisting rotation

The continuum manipulator exhibits excellent flexibility, enabling it to navigate through unstructured and narrow spaces. However, the current motion capabilities of the continuum arm are limited to expansion, bending, or their combination, which restricts its application range and potential uses. Designing a continuum manipulator capable of twisting motion around its axis poses a significant challenge. In this study, we propose a torsion module for the continuum manipulator that enables twisting motions. This module comprises a central trunk made of a torsion spring and a driving mechanism consisting of two tendons arranged in cylindrical helix symmetry. By stretching these driving cables, the module can achieve twisting motion. We describe the principle behind torsional motion, establish a kinematic model for the continuum torsional module, and analyze how structural parameters affect its performance in terms of twisting motion. Furthermore, we construct a continuum arm incorporating this torsion module to enable both twisting and bending motions. We present examples showcasing the versatile capabilities of this continuum manipulator in various specialized scenarios. Experimental results demonstrate that the addition of torsional functionality enhances dexterity and expands design possibilities for continuum manipulators.

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

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

Image-to-Joint Inverse Kinematic of a Supportive Continuum Arm Using Deep Learning

Image-to-Joint Inverse Kinematic of a Supportive Continuum Arm Using Deep Learning

Origami‐Inspired Modular Electro‐Ribbon Actuator for Multi‐Degrees of Freedom Motion

Origami robots, inspired by an ancient form of paper folding art, are capable of achieving high displacement in a lightweight and compact design that conventional robots can hardly attain. It, however, remains a challenge to drive origami robots with in situ active materials that imply minimal added mass and complexity and can be easily controlled to achieve multiple actuation modalities. Herein, inspired by the Twisted Tower origami structure, dielectrophoretic liquid zipping actuation concept is employed to develop a modular architecture, capable of achieving complicated motions with multiple degrees of freedom (DoF). The experimental results show a maximum of 3.9 degrees tilting per layer toward any desired direction, a 56.1% contraction of the original length, and 5.4 degrees twisting per layer. Each layer can generate a maximum contractile force of 1.03 N with a maximum 64.7% power efficiency and 2.775 W kg−1 power‐to‐weight ratio. A modified heterochiral arrangement of this modular actuator is proposed to enhance controllability across various movement modes. Its use in robotic‐wrist‐like actuation has been demonstrated, highlighting its significant potential for integration into soft robotic multi‐DoF structures, such as continuum arms.

Dynamic Manipulation and Stiffness Modulation of Cooperative Continuum Robots: Theory and Experiment

Abstract Cooperative continuum robots (CCRs) are composed of multiple coupled continuum arms to cooperatively conduct manipulation tasks. They can highly enhance the performance of individual continuum arms by providing extra stiffness, leading to increased accuracy, payload capacity, and dynamic stability of the robot. This study aimed to investigate the stiffness analysis of tendon-driven supportive-type CCRs (S-CCRs). For this purpose, first, a generalized framework for the dynamic mathematical formulation and numerical solution of S-CCRs was proposed, their dynamic response to complex scenarios was obtained, and the accuracy of the model was experimentally evaluated. Then, the capability of stiffness modulation of S-CCRs was studied. Tendon-driven S-CCRs are potentially capable of changing the stiffness with structural configuration, providing active stiffness control at the design level. Hence, in this study, the effects of the connection point location/angle of the supportive arms to the operative arm, as well as the imposed tendon limitations of the supportive arm on the stiffness of the robot, and consequently on the dynamic payload manipulation, were studied and practical solutions were proposed to develop a simple but effective stiffness control mechanism. This study showed that a typical S-CCR can increase its stiffness, just by a modular connector design up to 84% during manipulation, bringing a novel opportunity for stiffness modulation of CCRs.

PDE-based control synthesis for a planar cable-driven continuum arm

Classical wave and beam models cannot describe the arc-like planar configuration of a continuum arm. Based on the Cosserat rod theory, a strain vector and the rotation matrix are introduced into this work. Two groups of nonlinear partial differential equations (PDEs) are obtained by analyzing kinetic and potential energy densities and virtual power done by the cable tension, which are explicit both in the spatial coordinates and in the physical parameters. Although involving algebraic–trigonometric descriptions, the obtained dynamical model is the most concise formulation but difficult to use for control design. We develop a feedforward plus proportional–derivative feedback boundary control scheme for the nonlinear PDE system to regulate the planar shape of the continuum arm and then linearize the dynamical equations about the equilibrium to assess the control performance. Some interesting results are the closed-loop stability and the well-posedness analysis of the linearized model, which have not previously been noted. Finally, numerical results validate the dynamical equations and the performance of the controller.

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 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, Modelling, and Control of Continuum Arms with Pneumatic Artificial Muscles: A Review

In this research paper, we present a comprehensive analysis of the current state of soft robots actuated with pneumatic artificial muscles and emphasise their distinct advantages over rigid robots, including exceptional flexibility, adaptability, and safety. Our study explores the design principles of soft robots, drawing inspiration from biological systems and human hands, and identifies promising avenues for further development. The emergence of hybrid robots is also recognised as a significant advancement, particularly in scenarios requiring high precision. The article explores mathematical models encompassing kinematics, dynamics, and statics, as well as alternative model-free approaches. These theoretical frameworks are instrumental in understanding and manipulating the behaviour of soft robots. However, despite substantial progress, soft robots’ practical application and simulation face limitations, primarily due to the demanding requirements and implementation challenges associated with their deployment. Consequently, this paper highlights the need for continued research and advancements to bridge the gap between the theoretical potential and practical utilisation of soft robots.

Control of soft robots with inertial dynamics.

Soft robots promise improved safety and capability over rigid robots when deployed near humans or in complex, delicate, and dynamic environments. However, infinite degrees of freedom and the potential for highly nonlinear dynamics severely complicate their modeling and control. Analytical and machine learning methodologies have been applied to model soft robots but with constraints: quasi-static motions, quasi-linear deflections, or both. Here, we advance the modeling and control of soft robots into the inertial, nonlinear regime. We controlled motions of a soft, continuum arm with velocities 10 times larger and accelerations 40 times larger than those of previous work and did so for high-deflection shapes with more than 110° of curvature. We leveraged a data-driven learning approach for modeling, based on Koopman operator theory, and we introduce the concept of the static Koopman operator as a pregain term in optimal control. Our approach is rapid, requiring less than 5 min of training; is computationally low cost, requiring as little as 0.5 s to build the model; and is design agnostic, learning and accurately controlling two morphologically different soft robots. This work advances rapid modeling and control for soft robots from the realm of quasi-static to inertial, laying the groundwork for the next generation of compliant and highly dynamic robots.

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.

Robust tracking control of unknown models for space in-cabin robots with a pneumatic continuum arm

The service robots of space station in-cabin have attracted more and more attention. The space in-cabin robot with a pneumatic continuum arm is studied in this paper. It could be safer, more efficient and more flexible than the space rigid robot. However, the coupling motion of the moving base and the pneumatic continuum continuous arm brings a new challenge for controlling the end-effector to track the desired path. In this paper, a new control method based on the zeroing neural network (ZNN) is developed to solve the high-precision kinematics trajectory tracking control problem of unknown models. The real-time Jacobian matrix of the in-cabin robots with a pneumatic continuum arm is estimated by the input–output information when the parameter and the structure of the kinematic model are unknown. Moreover, this paper also employs a modified activation function power-sigmoid activation function (PSAF) to improve the robustness. In addition, it is proved through the Lyapunov stability theory that the proposed control approach is convergent and stable. Finally, the simulation results are given to show the effectiveness and robustness of the proposed control method for space in-cabin robots with a pneumatic continuum arm.

A Novel Space Robot with Triple Cable-Driven Continuum Arms for Space Grasping

With the increasing demand of human beings for space exploration, space robots show great development potential. When grasping space objects with different sizes and shapes, cable-driven continuum arms have better performance than traditional robots. In this paper, a novel space robot with triple cable-driven continuum arms is proposed, which can achieve compliant grasping through multi-arm cooperation. The kinematic model of the robot is proposed and verified through simulations and experiments. Results show that the maximum repeat positioning error is no larger than 1 mm and the maximum tracking error is no larger than 2 mm, compared to the 300 mm long arm. In addition, the demonstration experiment of grasping a ball indicates the good performance of the robot in compliant grasping.

Soft manipulator inspired by octopi: object grasping in all anatomical planes using a tendon-driven continuum arm

Soft manipulator inspired by octopi: object grasping in all anatomical planes using a tendon-driven continuum arm

Collaborative Continuum Robots for Remote Engineering Operations

In situ repair and maintenance of high-value industrial equipment is critical if they are to maintain the ability to continue vital operations. Conventional single-arm continuum robots have been proven numerous times to be successful tools for use in repair operations. However, often more than one arm is needed to ensure successful operation within several scenarios; thus, the collaborative operation of multiple arms is required. Here, we present the design and operating principles of a dual-arm continuum robot system designed to perform critical tasks within industrial settings. Here, presented are the design principle of the robotic system, the optimization-based inverse kinematic calculation of the 6-DoF continuum arms, and the collaborative operation strategy. The collaborative principle and algorithms used have been evaluated by a set of experiments to demonstrate the ability of the system to perform in situ machining operations. With the developed prototype and controller, the average error between planned and real toolpaths can be within 2.5 mm.

Continuum-Body-Pose Estimation From Partial Sensor Information Using Recurrent Neural Networks

Soft continuum arms have significant potential for use in various applications due to their extremely high degrees of freedom. For example, these soft arms can be used for grasping and manipulating fragile materials in the deep sea or carrying a human to rescue in unstructured environments. However, in these situations, the environment is often dark and visual cues are not always usable. Therefore, these arms must estimate their pose from proprioceptive sensors to control their behavior and execute their tasks in dark places. Estimating the pose in a dynamic situation is still challenging because of the arms' high dimensionality and the complex structural changes in the body shape. Therefore, this study demonstrates a novel method for estimating the pose of proprioceptive bending sensors using recurrent neural networks (RNNs). In particular, an RNN framework known as deep reservoir computing was used for this purpose. Results from experiments using an octopus-inspired soft robotic arm clearly indicate that the proposed method significantly outperforms existing methods using long short-term memory models or linear models. We expect that our proposed method will enable behavioral control of these arms in dark places such as the deep sea, space, and inside the human body in future applications.

Hybrid Eye-in-Hand/Eye-to-Hand Image Based Visual Servoing for Soft Continuum Arms

Soft continuum arms (SCAs) that are controlled by visual servoing (VS) present trade-offs between the camera range and tracking accuracy. Cameras placed at a distance (eye-to-hand) can observe a larger workspace area and the SCA tip, while a camera at the end effector (eye-in-hand) can more accurately survey the target. In this letter, we present a hybrid eye-to-hand and eye-in-hand VS scheme to track a desired object in the SCA's worksapce. When the target is not in the field-of-view of the tip camera, hand-to-eye VS is implemented using a wide field-of-view camera on the soft robot's base, to servo the soft robot's tip to a feasible region where the target is expected to be seen by the tip camera. This region is estimated by solving an optimization problem that finds the best region to place the SCA assuming a constant curvature model for the SCA. When the target is seen by the tip camera, the system switches to a hand-in-eye controller that keeps the target in the desired image position of the tip camera. Experimental results on the popular <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$BR^{2}$</tex-math></inline-formula> SCA demonstrates the effectiveness of the hybrid VS scheme under practical settings that include external disturbances.

Integrated design of an aerial soft-continuum manipulator for predictive maintenance.

This article presents an integrated concept of an aerial robot used for predictive maintenance in the construction sector. The latter can be remotely controlled, allowing the localization of cracks on wall surfaces and the adaptive deposit of the material for in situ repairs. The use of an aerial robot is motivated by fast intervention, allowing time and cost minimizing of overhead repairs without the need for scaffolding. It is composed of a flying mobile platform positioned in stationary mode to guide a soft continuum arm that allows to reach the area of cracks with different access points. Indeed, some constructions have complex geometries that present problems for access using rigid mechanical arms. The aerial robot uses visual sensors to automatically identify and localize cracks in walls, based on deep learning convolutional neural networks. A centerline representing the structural feature of the crack is computed. The soft continuum manipulator is used to guide the continuous deposit of the putty material to fill the microscopic crack. For this purpose, an inverse kinematic model-based control of the soft arm is developed, allowing to estimate the length of the bending tubes. The latter are then used as inputs for a neural network to predict the desired input pressure to bend the actuated soft tubes. A set of experiments was carried out on cracks located on flat and oblique surfaces, to evaluate the actual performances of the predictive maintenance mechatronic robot.

Impedance Iterative Learning Backstepping Control for Output-Constrained Multisection Continuum Arms Based on PMA

Background: Pneumatic muscle actuator (PMA) actuated multisection continuum arms are widely applied in various fields with high flexibility and bionic properties. Nonetheless, their kinematic modeling and control strategy proves to be extremely challenging tasks. Methods: The relationship expression between the deformation parameters and the length of PMA with the geometric method is obtained under the assumption of piecewise constant curvature. Then, the kinematic model is established based on the improved D-H method. Considering the limitation of PMA telescopic length, an impedance iterative learning backstepping control strategy is investigated. For one thing, the impedance control is utilized to ensure that the ideal static balance force is maintained constant in the Cartesian space. For another, the iterative learning backstepping control is applied to guarantee that the desired trajectory of each PMA can be accurately tracked with the output-constrained requirement. Moreover, iterative learning control (ILC) is implemented to dynamically estimate the unknown model parameters and the precondition of zero initial error in ILC is released by the trajectory reconstruction. To further ensure the constraint requirement of the PMA tracking error, a log-type barrier Lyapunov function is employed in the backstepping control, whose convergence is demonstrated by the composite energy function. Results: The tracking error of PMA converges to 0.004 m and does not exceed the time-varying constraint function through cosimulation. Conclusion: From the cosimulation results, the superiority and validity of the proposed theory are verified.