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Stiffness adjusting ability is essential for soft robotic arms to perform complex tasks. A soft state enables dexterous operation and safe interaction, while a rigid state enables large force output or heavy weight carrying. However, making a compact integration of soft actuators with powerful stiffness adjusting mechanisms is challenging. This study aims to develop a piston-like particle jamming mechanism for enhanced stiffness adjustment of a soft robotic arm.

The arm has two pairs of differential tendons for spatial bending, and a jamming core consists of four jamming units with particles sealed inside braided tubes for stiffness adjustment. The jamming core is pushed and pulled smoothly along the tendons by a piston, which is then driven by a motor and a ball screw mechanism.

The tip displacement of the arm under 150 N jamming force and no more than 0.3 kg load is minimal. The maximum stiffening ratio measured in the experiment under 150 N jamming force is up to 6–25 depends on the bending direction and added load of the arm, which is superior to most of the vacuum powered jamming method.

The proposed robotic arm makes an innovative compact integration of tendon-driven robotic arm and motor-driven piston-like particle jamming mechanism. The jamming force is much larger compared to conventional vacuum-powered systems and results in a superior stiffening ability.

Supernumerary robotic limbs (SRLs) are a new type of wearable robot, which improve the user’s operating and perceive the user’s environment by extra robotic limbs. There are some literature reviews about the SRLs’ key technology and development trend, but the design of SRLs has not been fully discussed and summarized. This paper aims to focus on the design of SRLs and provides a comprehensive review of the ontological structure design of SRLs.

In this paper, the related literature of SRLs is summarized and analyzed by VOSviewer. The structural features of different types of SRLs are extracted, and then discuss the design approach and characteristics of SRLs which are different from typical wearable robots.

The design concept of SRLs is different from the conventional wearable robots. SRLs have various reconfiguration and installed positions, and it will influence the safety and cooperativeness performance of SRLs.

This paper focuses on discussing the structural design of SRLs by literature review, and this review will help researchers understand the structural features of SRLs and key points of the ontological design of SRLs, which can be used as a reference for designing SRLs.

Soft robots are known for their excellent safe interaction ability and promising in surgical applications for their lower risks of damaging the surrounding organs when operating than their rigid counterparts. To explore the potential of soft robots in cardiac surgery, this paper aims to propose an adaptive iterative learning controller for tracking the irregular motion of the beating heart.

In continuous beating heart surgery, providing a relatively stable operating environment for the operator is crucial. It is highly necessary to use position-tracking technology to keep the target and the surgical manipulator as static as possible. To address the position tracking and control challenges associated with dynamic targets, with a focus on tracking the motion of the heart, control design work has been carried out. Considering the lag error introduced by the material properties of the soft surgical robotic arm and system delays, a controller design incorporating iterative learning control with parameter estimation was used for position control. The stability of the controller was analyzed and proven through the construction of a Lyapunov function, taking into account the unique characteristics of the soft robotic system.

The tracking performance of both the proportional-derivative (PD) position controller and the adaptive iterative learning controller are conducted on the simulated heart platform. The results of these two methods are compared and analyzed. The designed adaptive iterative learning control algorithm for position control at the end effector of the soft robotic system has demonstrated improved control precision and stability compared with traditional PD controllers. It exhibits effective compensation for periodic lag caused by system delays and material characteristics.

Tracking the beating heart, which undergoes quasi-periodic and complex motion with varying accelerations, poses a significant challenge even for rigid mechanical arms that can be precisely controlled and makes tracking targets located at the surface of the heart with the soft robot fraught with considerable difficulties. This paper originally proposes an adaptive interactive learning control algorithm to cope with the dynamic object tracking problem. The algorithm has theoretically proved its convergence and experimentally validated its performance at the cable-driven soft robot test bed.

The 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.

The 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.

The 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.

The 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.

This paper aims to review the shape sensing techniques using large area flexible electronics (LAFE). Shape perception of humanoid robots using tactile data is mainly focused.

Research papers on different shape sensing methodologies of objects with large area, published in the past 15 years, are reviewed with emphasis on contact-based shape sensors. Fiber optics based shape sensing methodology is discussed for comparison purpose.

LAFE-based shape sensors of humanoid robots incorporating advanced computational data handling techniques such as neural networks and machine learning (ML) algorithms are observed to give results with best resolution in 3D shape reconstruction.

The literature review is limited to shape sensing application either two- or three-dimensional (3D) LAFE. Optical shape sensing is briefly discussed which is widely used for small area. Optical scanners provide the best 3D shape reconstruction in the noncontact-based shape sensing; here this paper focuses only on contact-based shape sensing.

Contact-based shape sensing using polymer nanocomposites is a very economical solution as compared to optical 3D scanners. Although optical 3D scanners can provide a high resolution and fast scan of the 3D shape of the object, they require line of sight and complex image reconstruction algorithms. Using LAFE larger objects can be scanned with ML and basic electronic circuitory, which reduces the price hugely.

LAFE can be used as a wearable sensor to monitor critical biological parameters. They can be used to detect shape of large body parts and aid in designing prosthetic devices. Tactile sensing in humanoid robots is accomplished by electronic skin of the robot which is a prime example of human–machine interface at workplace.

This paper reviews a unique feature of LAFE in shape sensing of large area objects. It provides insights from mechanical, electrical, hardware and software perspective in the sensor design. The most suitable approach for large object shape sensing using LAFE is also suggested.

Soft robotics, regarded as a new research branch of robotics, has generated increasing interests in this decade and has demonstrated its outperformance in addressing safety issues when cooperating with human beings. However, there is still lack of accurate close-loop control because of the difficulty in acquiring feedback information and accurately modeling the system, especially in interactive environments. To this end, this paper aims to improve the controllability of the soft robot working in specific underwater environment. The system dynamics, which takes complicated hydrodynamics into account, is solved using Kane’s method. The dynamics-based adaptive visual servoing controller is proposed to realize accurate sensorimotor control.

This paper presents an image-based visual servoing control scheme for a cable-driven soft robot with a fixed camera observing the motions. The intrinsic and extrinsic parameters of the camera can be adapted online so that tedious camera calibration work can be eliminated. It is acknowledged that kinematics-based control can be only applied into tasks in the free space and has limitation in accelerating the motion speed of robot arms. That is, one must consider the unneglectable interaction effects generated from the environment and objectives when operating soft robots in such interactive control tasks. To extend the application of soft robots into underwater environment, the study models system dynamics considering complicated hydrodynamic effects. With the pre-knowledge of the external effects, the performance of the robot can be further improved by adding the compensation term into the controller.

The proposed controller has theoretically proved its convergence of image error, adaptive estimation error and the stability of the dynamical system based on Lyapunov’s analysis. The authors also validate the performance of the controller in positioning control task in an underwater environment. The controller shows its capacity of rapid convergence to and accurate tracking performance of a static image target in a physical experiment.

To the best of the authors’ knowledge, there is no such research before that has developed dynamics-based visual servoing controller which takes into account the environment interactions. This work can thus improve the control accuracy and enhance the applicability of soft robotics when operating in complicated environments.

The purpose of this paper is to present a novel tactile sensor and a visual-tactile recognition framework to reduce the uncertainty of the visual recognition of transparent objects.

A multitask learning model is used to recognize intuitive appearance attributes except texture in the visual mode. Tactile mode adopts a novel vision-based tactile sensor via the level-regional feature extraction network (LRFE-Net) recognition framework to acquire high-resolution texture information and temperature information. Finally, the attribute results of the two modes are integrated based on integration rules.

The recognition accuracy of attributes, such as style, handle, transparency and temperature, is near 100%, and the texture recognition accuracy is 98.75%. The experimental results demonstrate that the proposed framework with a vision-based tactile sensor can improve attribute recognition.

Transparency and visual differences make the texture of transparent glass hard to recognize. Vision-based tactile sensors can improve the texture recognition effect and acquire additional attributes. Integrating visual and tactile information is beneficial to acquiring complete attribute features.

The story of Charles Babbage (1791–1871) is one that is well known to cyberneticians and systems scientists. His contribution to, and impact on, modern computing is recognised not only in the country of his birth but also worldwide. In particular, he played a leading role in the evolution of difference engines. Although it has often been said that the first difference engine was invented by Charles Babbage in 1821, it is also claimed that it was Johann Helfrich Müller (1746–1830), a German engineer and master builder, who presented the first idea of a difference engine. Others were also involved, and the text by Michael Lindgren Glory and Failure: The Difference Engines of Johann Muller, Charles Babbage and George and Edvard Scheutz (MIT Press, Cambridge, Mass., 1990) is well worth reading.

Variable stiffness structure can significantly improve the interactive capabilities of grippers. Shape memory alloys have become a popular option for materials with variable stiffness structures. However, its variable stiffness range is limited by its stiffness in two phases. The purpose of this paper is to enhance the manipulation capabilities of tendon-driven flexible grippers by designing a wide-range variable stiffness structure.

Constitutive models of shape memory alloy and mechanical models are used to analyze the performance of the variable stiffness structure. A separated solution was used to combine the tendon-driven gripper and the variable stiffness structure. The feed-forward control algorithm is used to enhance the control stability of the variable stiffness structure.

The stiffness variable capability of the proposed variable stiffness structure is verified by experiments. The stability of the feedback control algorithm was verified by sinusoidal tracking experiments. The variable stiffness range of 8.41 times of the flexible gripper was tested experimentally. The interaction capability of the variable stiffness flexible gripper is verified by the object grasping experiments.

A new wide-range variable stiffness structure is proposed and validated. The new variable stiffness structure has a larger range of stiffness variation and better control stability. The new flexible structure can be applied to conventional grippers to help them gain stiffness variable capability and improve their interaction ability.

This paper aims to devise a first-of-its-kind methodology to determine the design, operating conditions and actuation strategy of pneumatic artificial muscles (PAMs) for assistive robotic applications. This requires extensive characterization, data set generation and meaningful modelling between PAM characteristics and design variables. Such a characterization should cover a wide range of design and operation parameters. This is a stepping stone towards generating a design guide for this highly popular compliant actuator, just like any conventional element of a mechanism.

Characterization of a large pool of custom fabricated PAMs of varying designs is performed to determine their static and dynamic behaviours. Metaheuristic optimizer-based artificial neural network (ANN) structures are used to determine eight different models representing PAM behaviour. The assistance of knee flexion during level walking is targeted for evaluating the applicability of the developed actuator by attaching a PAM across the joint. Accordingly, the PAM design and the actuation strategy are optimized through a tabletop emulator.

The dependence of passive length, static contraction, dynamic step response for inflation and deflation of the PAMs on their design dimensions and operating parameters is successfully modelled by the ANNs. The efficacy of these models is investigated to successfully optimize the PAM design, operation parameters and actuation strategy for using a PAM in assisting knee flexion in human gait.

Characterization of static and the dynamic behaviour of a large pool of PAMs with varying designs over a wide range of operating conditions is the novel feature in this article. A lucid customizable fabrication technique is discussed to obtain a wide variety of PAM designs. Metaheuristic-based ANNs are used for tackling high non-linearity in data while modelling the PAM behaviour. An innovative tabletop emulator is used for investigating the utility of the models in the possible application of PAMs in assistive robotics.