Some applications, such as surgical interventions, require that potential soft robots have the capability to alter their shape and enhance their force output on demand. This paper presents an antagonistic stiffening mechanism combining pneumatic actuation with tendon locking to achieve configuration- and stiffness control. Elongation of a soft pneumatic section, resulting from air actuation, is opposed by constraining the length of integrated tendons. These tendons can be locked in length by pneumatically activated levers at the base of each segment. Hence, tendon locking will not affect the configuration of other segments of a multi-segment manipulator. Our concept achieves a stiffness increase of up to 201.7% and a larger, more uniform radial workspace compared to the widely used pneumatic actuation concept while maintaining the low technical effort required for actuation. We also demonstrate how our actuation concept enables independent control of stiffness levels for individual segments of a multi-segment manipulator and their MR compatibility.

Soft eversion robots have demonstrated significant advantages in navigating within confined spaces with minimal friction, making them promising candidates for various intraluminal applications in medical, industrial, and exploratory domains. While these type of growing robots enable frictionless movement within hollow structures, no existing soft robotic actuation mechanism can grow along the outer surface of an environment without generating friction. A robot with these capabilities could open new possibilities, such as endoscopic vein harvesting for coronary artery bypass graft surgery. This paper introduces a novel growing robotic manipulator based on an outside-in material feeding mechanism - the inversion robot. Unlike conventional eversion robots, which expand by feeding material from the inside out, the inversion robot draws material from the outside to the inside, encapsulating its external environment within an inner sleeve to achieve frictionless movement. We present the design, implementation, and experimental validation of this inversion robot, investigating its growth behavior under varying pressure values and with different diameters, its ability to navigate along defined trajectories, and with a tools mounted to its tip. This inversion robot could enable vein dissection while preserving the surrounding fat layer, making it a promising innovation for minimally invasive vascular surgery and beyond.

Soft shape-morphing technologies are explored in fields such as soft robotics, metamaterials and design, enabling systems to adapt dynamically through elastic deformations. Applied in mobile devices, actuators, interactive objects and environments, these systems can respond to functional needs and environmental stimuli, communicating information and enhancing human experiences. A primary design goal for these systems is achieving extensive and complex shape transformations. Traditionally, soft robotics employs a pneumatic active layer constrained by a passive layer, limiting the deformation range. However, using dual active layers can expand deformation potential. Expanding on these principles, this work introduces PNEUmorph: a pneumatic surface constrained by a network of variable-length tendons, allowing broader shape transformations than traditional single-layer systems. PNEUmorph’s dual-layer actuation overcomes fixed deformation limits, significantly enhancing shape-morphing capabilities. This paper presents PNEUmorph’s design and geometrical characterization, achieved through an interdisciplinary approach that merges design and soft robotics methods. This study details methods for simulation, fabrication, operation and evaluation, offering insights into experimental results and directions for advancing surface-based soft shape-morphing systems.

With the increasing development and implementation of Automated Driving Systems (ADS), understanding driver reactions during the transition from automated to manual driving has become a critical research area. The transition from automated to manual driving is a challenging task that requires the driver to take over control of the ADS-equipped vehicle in a safe and timely manner. Previous studies have identified various variables associated with driver reactions, but a comprehensive classification is lacking. This paper addresses this gap by systematically identifying and classifying the independent and dependent variables that affect drivers’ reactions in ADS-equipped vehicles during the transition from automated to manual driving. This is achieved through an extensive review of major findings in the designs of driving simulator experiments in this field. Additionally, an analysis of five hundred on-road collision reports from 2014 to 2023 involving ADS-equipped test vehicles is conducted. Finally, a comprehensive overview of the identified dependent and independent variables from both studies, along with their synergies and shortcomings, is presented. The variables are categorised and mapped, highlighting key research gaps. The main research gaps were identified by comparing the variables extracted from reviewed papers with the statistical analysis in the DMV reports. Some gaps include the need for incorporating real-world scenarios into experiments, driver-initiated take-over requests and applying physiological measures to assess driver-centric factors. This detailed identification and classification of variables assists in designing a range of future experimental scenarios to assess drivers’ reactions to the transition of control in ADS-equipped vehicles.

Soft continuum robots, fabricated from elastomeric materials, offer unparalleled flexibility and adaptability, making them ideal for applications such as minimally invasive surgery and inspections in constrained environments. With the miniaturization of imaging technologies and the development of novel control algorithms, these devices provide exceptional opportunities to visualize the internal structures of the human body. However, there are still challenges in accurately estimating external forces applied to these systems using current technologies. Adding additional sensors is challenging without compromising the softness of the device. This work presents a visual deformation-based force sensing framework for soft continuum robots. The core idea behind this work is that point loads lead to unique deformation profiles in an actuated soft-bodied robot. We introduce a Convolutional Neural Network-based tip force estimation method that utilizes arbitrarily placed camera images and actuation inputs to predict applied tip forces. Experimental validation was performed using the STIFF-FLOP robot, a pneumatically actuated soft robot developed for minimally invasive surgery. Our vision-based force estimation model demonstrated a sensing precision of 0.05 N in the XY plane during testing, with data collection and training taking only 70 minutes.

In this paper, we study the effectiveness of using a rigid movable palm for grasping varied objects, on a caging inspired gripper with three flexible fingers. This rigid palm extends to actively exert downwards force on objects, in contrast with existing methods, which combine movable palms with negative pressure to exert lifting forces on objects. We compare grasping with and without the palm, whilst also changing finger stiffness and fingertip angle, to analyse the effect on grasp success rate and stability over 24 design permutations. Reinforcement learning was used to train a unique grasping controller in every design case, aiming to achieve optimal grasping as the basis for comparison. Validation in both simulation and the real world was completed for every permutation. We demonstrated that the using palm improved success rates on average by 11% in simulation, 13% in the real world, and achieved a best real world success rate of 96% on 18 YCB benchmark food objects. Grasp stability against disturbances in three axes improved by 15% on average when using the palm. Our investigation determined fingertip angle had a large effect, whereas finger stiffness was less important.

Soft continuum robots, characterized by their inherent compliance and dexterity, are increasingly pivotal in applications requiring delicate interactions with the environment such as the medical field. Despite their advantages, challenges persist in accurately modeling and controlling their shape during interactions with surrounding objects. This is because of the difficulty in modeling the large degrees of freedom in soft-bodied objects that become more active during interactions. In this study, we present a deep visual model to predict the interaction shapes of a soft continuum robot in contact with surrounding objects. By formulating this task as a forward-statics problem, the model uses the initial state images containing the object configuration and future actuation values to predict interactive state images of the robot under this actuation condition. We developed and tested the model in both simulated and physical environments, explored the model’s predictive capabilities using monocular and binocular views, and tested the model’s generalization ability on different datasets. Our results show that deep learning methods are a promising tool for solving the complex problem of predicting the shape of a soft continuum robot interacting with the environment, requiring no prior knowledge about the system dynamics and explicit mapping of the environment. This study paves the way for future explorations in robot-environment interaction modeling and the development of more adaptable interaction shape control strategies.

The affordability and functionality of hand prosthetics in developing countries are still very limited. This work aims to present and evaluate the new version of the PrHand affordable robotic prosthesis (PrHand V3), built with soft robotics and compliant mechanisms. PrHand V3 implements a new frictionless tendon unification system, the degree of freedom of thumb opposition was removed, and the finger flexion was improved to the previous version, PrHand V2. The study contributes by evaluating these mechanical changes and conducting the first functional assessment of PrHand V3 with an amputee user. The Anthropomorphic Hand Assessment Protocol (AHAP) dexterity test was the first evaluation in this work; it evaluated how the prosthesis performs eight different grips. PrHand V3 was compared with a PrHand V2 and a commercial robotic prosthesis A3D from Prótesis Avanzadas SAS. PrHand V3’s score on the AHAP test was 80%. This result is higher than the 69% obtained by the PrHand V2 and the 79% obtained by A3D. The Activities Measure for Upper Limb Amputees (AM-ULA) test was the second evaluation in this work; An A3D amputee user performed 23 Activities of Daily Living with PrHand V3 and an A3D. PrHand V3 obtained an average of 2.86/4 and A3D obtained an average of 2.96/4 without significant differences between the two tests. The soft actuation of PrHand V3 as an affordable prosthesis performs similarly to a commercial robotic prosthesis with the advantage of being more flexible to assist a trans-radial hand amputee.

Stiffness sensing and palpation are essential for understanding object properties, including tissue health and fruit ripeness. Currently, there is limited research on using soft-tipped sensors for stiffness sensing and dynamic palpation. To address these challenges, we investigate how the pressure modulated optical tracking (PMOT) sensor can use compliance control to quantify tissue stiffness and detect margins in samples through dynamic palpation. Results show that the PMOT sensor modulus of elasticity sensing range is from 4.20 kPa up to 177.62 kPa. Across all untrained samples, elasticity was measured with a root-mean-square error (RMSE) of 7.72%. Further, it is shown that the sensor can locate margins between 13.4 kPa and embedded 29.3 kPa materials during palpation. When mounted on a linear rail, averaged for the direction of travel, the sensor's signal-to-noise ratio (SNR) was up to 39.5:1. Participants used the sensor to locate embedded margins in a teleoperation environment with visual feedback. This was achieved with an accuracy of 96.5%.

Soft robotic structures may play a major role in the 4th industrial revolution. Researchers have successfully demonstrated the advantages of soft robotics over traditional robots made of rigid links and joints in many application areas. Variable stiffness links (VSL) and joints (VSJ) have been investigated to achieve on-demand forces and, at the same time, be inherently safe in interactions with humans. However, a thorough characterisation of soft and rigid robotic components is still required. This paper investigates the influence of antagonistically actuated, stiffness-controllable joint-link units (JLUs) on the performance of collaborative robots (i.e. stiffness, load capacity, repetitive precision) and characterizes the difference compared with rigid units. A JLU is made of a combination of a VSL, a VSJ, and their rigid counterparts. Experimental results show that the VSL has minor differences in terms of stiffness (0.62 ∼ 0.95), output force (0.93 ∼ 0.94), and repetitive precision compared with the rigid link. For the VSJ, our results show a significant gap compared with the servo motor with regards to maximum stiffness (0.14 ∼ 0.21) and repetitive position precision (0.07 ∼ 0.25). However, similar performance on repetitive force precision and better performance on the maximum output force (1.54 ∼ 1.55 times) are demonstrated.

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.

Since the emergence of soft robotics around two decades ago, research interest in the field has escalated at a pace. It is fuelled by the industry’s appreciation of the wide range of soft materials available that can be used to create highly dexterous robots with adaptability characteristics far beyond that which can be achieved with rigid component devices. The ability, inherent in soft robots, to compliantly adapt to the environment, has significantly sparked interest from the surgical robotics community. This article provides an in-depth overview of recent progress and outlines the remaining challenges in the development of soft robotics for minimally invasive surgery.

Soft robotic manipulators have been created and investigated for a number of applications due to their advantages over rigid robots. In minimally invasive surgery, for instance, soft robots have successfully demonstrated a number of benefits due to the compliant and flexible nature of the material they are made of. However, these type of robots struggle with performing tasks that require on-demand stiffness i.e. exerting higher forces to the surrounding environment. A number of semi-active and active mechanisms have been investigated to change and control the stiffness of soft robotic manipulators. Embedding these mechanisms in soft manipulators for spacerestricted applications can be challenging though.To better understand the inherent passive stiffness properties of soft manipulators, we propose a screw theory-based stiffness analysis for fluidic-driven continuum soft robotic manipulators. First, we derive the forward kinematics based on a parameter-based piece-wise constant curvature model. It is worth noting, our stiffness analysis can be conducted based on any freespace forward kinematic model. Then our stiffness analysis and mapping methodology is conducted based on screw theory. Initial results of our approach demonstrate the feasibility comparing computational and experimental data.

This paper proposes the design of a robotic gripper motivated by the bin-picking problem, where a variety of objects need to be picked from cluttered bins. The presented gripper design focuses on an enveloping cage-like approach, which surrounds the object with three hooked fingers, and then presses into the object with a movable palm. The fingers are flexible and imbue grasps with some elasticity, helping to conform to objects and, crucially, adding friction to cases where an object cannot be caged. This approach proved effective on a set of basic shapes, such as cuboids and cylinders, in which every object could be grasped. In particular, flat bottom parts could be grasped in a very stable manner, as demonstrated by testing grasps with multiple 5N and 10N disturbances. A set of supermarket items were also tested, highlighting promising features such as effective grasping of fruits and vegetables, as well as some limitations in the current embodiment, which is not always able to slip the fingers underneath objects.

A dynamic model of a soft fibre-reinforced fluidic elastomer is presented and experimentally verified, which can be used for model-based controller design. Due to the inherent visco-(hyper)elastic characteristics and nonlinear time-dependent behaviour of soft fluidic elastomer robots, analytic dynamic modelling is challenging. The fibre reinforced noninflatable soft fluidic elastomer robot used in this paper can produce both planar and spatial movements. Dynamic equations are developed for both cases. Parameters, related to the viscoelastic behaviour of the robot during elongation and bending motion, are identified experimentally and incorporated into our model. The modified dynamic model is then validated in experiments comparing the time responses of the physical robot with the corresponding outputs of the simulation model. The results validate the accuracy of the proposed dynamic model.

An important aspect in safety–critical domains is Situational Awareness (SA) where operators consolidate data into an understanding of the situation that needs to be updated dynamically as the situation changes over time. Among existing measures of SA, only physiological measures can assess the cognitive processes associated with SA in real-time. Some studies showed promise in detecting cognitive states associated with SA in complex tasks using brain signals (e.g. electroencephalogram/EEG). In this paper, an analytical methodology is proposed to identify EEG signatures associated with SA on various regions of the brain. A new data set from 32 participants completing the SA test in the PEBL is collected using a 32-channel dry-EEG headset. The proposed method is tested on the new data set and a correlation is identified between the frequency bands of β (12-30Hz) and γ (30-45Hz) and SA. Also, activation of neurons in the left and right hemisphere of the parietal and temporal lobe is observed. These regions are responsible for the visuo-spatial ability and memory and reasoning tasks. Among the presented results, the highest achieved accuracy on test data is 67%.

Actuation means for soft robotic structures are manifold: despite actuation mechanisms such as tendon-driven manipulators or shape memory alloys, the majority of soft robotic actuators are fluidically actuated - either purely by positive or negative air pressure or by hydraulic actuation only. This paper presents the novel idea of employing hybrid fluidic - hydraulic and pneumatic - actuation for soft robotic systems. The concept and design of the hybrid actuation system as well as the fabrication of the soft actuator are presented: Polyvinyl Alcohol (PVA) foam is embedded inside a casted, reinforced silicone chamber. A hydraulic and pneumatic robotic syringe pump are connected to the base and top of the soft actuator. We found that a higher percentage of hydraulics resulted in a higher output force. Hydraulic actuation further is able to change displacements at a higher rate compared to pneumatic actuation. Changing between Hydraulic:Pneumatic (HP) ratios shows how stiffness properties of a soft actuator can be varied.

With the advent of service robots in public places (e.g., in airports and shopping malls), understanding socio-psychological interactions between humans and robots is of paramount importance. On the one hand, traditional robotic navigation systems consider humans and robots as moving obstacles and focus on the problem of real-time collision avoidance in Human-Robot Interaction (HRI) using mathematical models. On the other hand, the behavior of a robot has been determined with respect to a human. Parameters for human-human interaction have been assumed and applied to interactions involving robots. One major limitation is the lack of sufficient data for calibration and validation procedures. This paper models, calibrates and validates the socio-psychological interaction of the human in HRIs among crowds. The mathematical model is an extension of the Social Force Model for crowd modelling. The proposed model is calibrated and validated using open source datasets (including uninstructed human trajectories) from the Asia and Pacific Trade Center shopping mall in Osaka (Japan).In summary, the results of the calibration and validation on the multiple HRIs encountered in the datasets show that humans react to a service robot to a higher extend within a larger distance compared to the interaction range towards another human. This microscopic model, calibration and validation framework can be used to simulate HRI between service robots and humans, predict humans' behavior, conduct comparative studies, and gain insights into safe and comfortable human-robot relationships from the human's perspective.

Transcatheter aortic valve implantation (TAVI) is a minimally invasive surgical technique to treat aortic heart valve diseases. According to current clinical guidelines, the implanted prosthetic valve replacing the native one is selected based on pre-operative size assessment of the aortic annulus through different imaging techniques. That very often leads to suboptimal device selection resulting in major complications, such as prosthetic valve leakage or interruption of the cardiac electrical signal. In this paper, we propose a new, intra-operative approach to determine the diameter of theaortic annulus exploiting intra-balloon pressure and volume data, acquired from a robotised valvuloplasty balloon catheter. An inflation device, capable of collecting real-time intra-balloon pressure and volume data, was designed and interfaced with a commercially available valvuloplasty balloon catheter. A sizing algorithm allowing to precisely estimate the annular diameter was integrated. The algorithm relies on a characterised analytical model of the balloon free inflation and an iterative method based on linear regression. In vitro tests were performed on idealised aortic phantoms. Experimental results show that pressure-volume data can be used to determine annular diameters bigger than the unstretched diameter of the balloon catheter. For these cases, the proposed approach exhibited good precision (maximum average error 0.93%) and good repeatability (maximum standard deviation ±0.11 mm).

This paper describes a novel approach in human-robot interaction driven by ergonomics. With a clear focus on optimising ergonomics, the approach proposed here continuously observes a human user’s posture and by invoking appropriate cooperative robot movements, the user’s posture is, whenever required, brought back to an ergonomic optimum. Effectively, the new protocol optimises the human-robot relative position and orientation as a function of human ergonomics. An RGB-D camera is used to calculate and monitor human joint angles in real-time and to determine the current ergonomics state. A total of 6 main causes of low ergonomic states are identified, leading to 6 universal robot responses to allow the human to return to an optimal ergonomics state. The algorithmic framework identifies these 6 causes and controls the cooperating robot to always adapt the environment (e.g. change the pose of the workpiece) in a way that is ergonomically most comfortable for the interacting user. Hence, human-robot interaction is continuously re-evaluated optimizing ergonomics states. The approach is validated through an experimental study, based on established ergonomic methods and their adaptation for real-time application. The study confirms improved ergonomics using the new approach.

Soft material robots offer a number of advantages over traditional rigid robots in applications including human-robot interaction, rehabilitation and surgery. These robots can navigate around obstacles, elongate, squeeze through narrow openings or be squeezed - and they are considered to be inherently safe. The ability to stiffen compliant soft actuators has been achieved by embedding various mechanisms that are generally decoupled from the actuation principle. Miniaturisation becomes challenging due to space limitations which can in turn result in diminution of stiffening effects. Here, we propose to hydraulically actuate soft manipulators with low-melting-point material and, at the same time, be able to switch between a soft and stiff state. Instead of allocating an additional stiffening chamber within the soft robot, one chamber only is used for actuation and stiffening. Low Melting Point Alloy is integrated into the actuation chamber of a single-compartment soft robotic manipulator and the interfaced robotic syringe pump. Temperature change is enabled through embedded nichrome wires. Our experimental results show higher stiffness factors, from 9-12 opposing the motion of curvature, than those previously found for jamming mechanisms incorporated in separate additional chambers, in the range of 2-8 for the same motion.

Many modern roboticists take inspiration from biology to create novel robotic structures, including those that are modeled after the octopus. This paper advances this trend by creating soft robots modeling the complex motion patterns of octopus tentacles employing a bio-mimetic approach. The proposed octopus robot is entirely made from soft material and uses a novel fluidic actuation mechanism that allows the robot to advance forward, change directions and rotate around its primary axis. The paper presents the robot's design and fabrication process. An experimental study is conducted showing the feasibility of the proposed robot and actuation mechanism.

Endovascular aneurysm repair (EVAR) is a minimally invasive approach for abdominal aortic aneurysm (AAA) treatment. Compared to open surgery, the benefits of EVAR include faster recovery and shorter time in hospital as well as no general anesthesia (in most cases). Though EVAR has become a preferred way to treat AAA with an increasing number of procedures, there are persisting complications, e.g. stent graft migration. Suturing the stent graft to the aorta increases the displacement force necessary to move the implant. This paper describes the design of a suturing catheter for EVAR. The suturing device consist of two modules which can be inserted through the femoral arteries into the abdominal aorta where both join using an electro-magnetic connector. The positioning module provides an anchor inside the aorta for the suturing module and new sequential positions for each stitch. Our large-scale prototype is validated inside a phantom vessel made of silicone material. We are able to successfully prove the concept of this novel single-sided suturing catheter for EVAR.

Soft robotic structures might play a major role in the 4thindustrial revolution. Researchers have successfully demonstrated advantages of soft robotics over traditional robots made of rigid links and joints in several application areas including manufacturing, healthcare and surgical interventions. However, soft robots have limited ability to exert higher forces when it comes to interaction with the environment, hence, change their stiffness on demand over a wide range. One stiffness mechanism embodies tendon-driven and pneumatic air actuation in an antagonistic way achieving variable stiffness values. In this paper, we apply a beam-mechanics-based model to this type of soft stiffness controllable robot. This mathematical model takes into account the various stiffness levels of the soft robotic manipulator as well as interaction forces with the environment at the tip of the manipulator. The analytical model is implemented into a robotic actuation system made of motorised linear rails with load cells (obtaining applied forces to the tendons) and a pressure regulator. Here, we present and analyse the performance and limitations of our model.

Nowadays, the field of industrial robotics focuses particularly on collaborative robots that are able to work closely together with a human worker in an inherently safe way. To detect and prevent harmful collisions, a number of solutions both from the actuation and sensing sides have been suggested. However, due to the rigid body structures of the majority of systems, the risk of harmful collisions with human operators in a collaborative environment remains. In this paper, we propose a novel concept for a collaborative robot made of Variable Stiffness Links (VSLs). The idea is to use a combination of silicone based structures and fabric materials to create stiffness-controllable links that are pneumatically actuated. According to the application, it is possible to change the stiffness of the links by varying the value of pressure inside their structure. Moreover, the pressure readings from the pressure sensors inside the regulators can be utilised to detect collisions between the manipulator body and a human worker, for instance. A set of experiments are performed with the aim to assess the performance of the VSL when embedded in a robotic manipulator. The effects of different loads and pressures on the workspace of the manipulator are evaluated together with the efficiency of the collision detection control system and hardware.

The constantly increasing amount of machines operating in the vicinity of humans makes it necessary to rethink the design approach for such machines to ensure that they are safe when interacting with humans. Traditional mechanisms are rigid and heavy and as such considered unsuitable, even dangerous when a controlled physical contact with humans is desired. A huge improvement in terms of safe human-robot interaction has been achieved by a radically new approach to robotics - soft material robotics. These new robots are made of compliant materials that render them safe when compared to the conventional rigid-link robots. This undeniable advantage of compliance and softness is paired with a number of drawbacks. One of them is that a complex and sophisticated controller is required to move a soft robot into the desired positions or along a desired trajectory, especially with external forces being present. In this paper we propose an improved soft fluidic rotary actuator composed of silicone rubber and fiber-based reinforcement. The actuator is cheap and easily manufactured providing near linear actuation properties when compared to pneumatic actuators presented elsewhere. The paper presents the actuator design, manufacturing process and a mathematical model of the actuator behavior as well as an experimental validation of the model. Four different actuator types are compared including a square-shaped and three differently reinforced cylindrical actuators.

In this paper, we present a novel pose estimation and obstacle avoidance approach for tendon-driven multi-segment continuum manipulators moving in dynamic environments. A novel multi-stage implementation of an Extended Kalman Filter is used to estimate the pose of every point along the manipulator's body using only the position information of each segment tip. Combined with a potential field, the overall algorithm will guide the manipulator tip to a desired target location and, at the same time, keep the manipulator body safe from collisions with obstacles. The results show that the approach works well in a real-time simulation environment that contains moving obstacles in the vicinity of the manipulator.

This work investigates the applicability of stiffness-based modelling in soft robotics manipulation. The methodology is introduced and applied to model a soft robotics manipulator as single 3d Timoshenko beam element. The model is then utilized to solve the forward kinematics problem for the manipulator. The algorithm is validated comparing the simulated deflection with the deflection of the physical manipulator for two defined pressure sequences. It is shown that the model behaves in a highly similar fashion in comparison to the manipulator. For both trajectories the maximum position error is close to 6 mm while the error in orientation not more than 18°. The methodology as described in this work reveals great applicability to the field of soft robots being limited only by the stiffness matrix assembly for the given system. Implementations of inverse kinematics and the effects of external force applications are effectively integrable in the described theory.

This paper presents a methodology for the development of a multi-axis force/torque sensor based on optoelectronic technology. The advantages of using this sensing principle are the low manufacturing costs, the simple fabrication, and the immunity to electrical noise. The force/ torque sensor makes use of six optical sensors: each sensor measures the displacement of a reflective surface that moves integrally with a simply-supported beam. The proposed mechanical structure allows for a variety of shapes on the mechanical structure to be easily adaptable to many robot applications. In this paper, we present a five-axis force/torque sensor based on this optoelectronic principle. To measure force/torque components, two identical three-DoF force/torque sensor structures (comprised of three beams) are mounted on top of each other. Photo sensors and mirrors are fixed inside the structure to measure the six beam deflections. In this paper, we describe the sensor structure, design, fabrication, calibration, and verify our sensor development methodology.

This paper proposes a soft, inflatable manipulator that is antagonistically actuated by tendons and pneumatics. The combination of the two actuation mechanisms in this antagonistic robot structure is inspired by the octopus which uses its longitudinal and transversal muscles to steer, elongate, shrink and also stiffen its continuum arms. By “activating” its antagonistic muscle groups at the same time, the octopus can achieve multiple motion patterns as well as stiffen their arms. Being organized in a similar fashion, our robot manipulator uses, on the one hand, pneumatic actuation and, on the other hand, tendon-based actuation - one opposing the other, achieving an overall antagonistic actuation framework. Controlling the pressure inside the robot while at the same time controlling the tendons' displacements, the robot can be moved into a wide range of configurations while simultaneously controlling the arm's stiffness. This paper builds on earlier work by the authors: Here, we present a new conic-shaped manipulator structure and the control architecture. Using a constant curvature model, we have derived an approach suitable for controlling the robot manipulator. The manipulator's reachable workspace is analyzed and proof-of-concept experiments were conducted to show the robot's stiffness control and motion abilities.

This paper presents an innovative hand-held device able to compute stiffness when interacting with a soft object. The device is composed of four linear indenters and a USB camera. The stiffness is computed in real-time, tracking the movements of spherical features in the image of the camera. Those movements relate to the movements of the four indenters when interacting with a soft surface. Since the indenters are connected to springs with different spring constants, the displacement of the indenters varies when interacting with a soft object. The proposed multi-indenting device allows measuring the object's stiffness as well as the pan and tilt angles between the sensor and the surface of the soft object. Tests were performed to evaluate the accuracy of the proposed palpation mechanism against commercial springs of known stiffness. Results show that the accuracy and sensitivity of the proposed device increases with the softness of the examined object. Preliminary tests with silicon show the ability of the sensing mechanism to characterize phantom soft tissue for small indentation. It is noted that the results are not affected by the orientation of the device when probing the surface. The proposed sensing device can be used in different applications, such as external palpation for diagnosis or, if miniaturized, embedded on an endoscopic camera and used in Minimally Invasive Surgery (MIS).

This paper presents a highly compact fibre-optic system based on light intensity modulation for multi-segment flex sensing in pliable robot arms, e.g., articulated surgical instruments. This fibre-optic arrangement is 7.5 mm in diameter and is comprised of a two-segment flexible and stretchable Steiner chain arm section with twelve housings at the distal side which accommodates passive cables. The displacement of each cable will be used to determine the bending. This Steiner chain section is followed by a basal rigid fibre-optic sensing unit integrated with a low-friction retractable distance modulation array which couples the motion of the passive cables with light-emitting optical fibres. The low-friction retractable distance modulation array uses steel spring-needle double sliders to reduce the hysteresis and to recover reference sensor values when the arm returns to its original straight configuration. The U-shape loopback design of the optical fibres allows integration of all electronics away from the sensing site. The experimental results indicate a maximum bending angle error of 6° in one individual segment of the two-segment arm with respect to reference angle values calculated from camera images.

This paper presents an innovative approach of utilising visual feedback to determine physical interaction forces with soft tissue during Minimally Invasive Surgery (MIS). This novel force sensing device is composed of a linear retractable mechanism and a spherical visual feature. The sensor mechanism can be adapted to endoscopic cameras used in MIS. As the distance between the camera and feature varies due to the sliding joint, interaction forces with anatomical surfaces can be computed based on the visual appearance of the feature in the image. Hence, this device allows the measurement of forces without introducing new stand-alone sensors. A mathematical model was derived based on validation data tests and preliminary experiments were conducted to verify the model's accuracy. Experimental results confirm the effectiveness of our vision based approach.

This paper introduces an optical based three axis force sensor which can be integrated with the robot arm of the EU project STIFF-FLOP (STIFFness controllable Flexible and Learnable Manipulator for Surgical Operations) in order to measure applied external forces. The structure of the STIFF-FLOP arm is free of metal components and electric circuits and, hence, is inherently safe near patients during surgical operations. In addition, this feature makes the performance of this sensing system immune against strong magnetic fields inside magnetic resonance (MR) imaging scanners. The hollow structure of the sensor allows the implementation of distributed actuation and sensing along the body of the manipulator. In this paper, we describe the design and calibration procedure of the proposed three axis optics-based force sensor. The experimental results confirm the effectiveness of our optical sensing approach and its applicability to determine the force and momentum components during the physical interaction of the robot arm with its environment.

Robotic manipulators for Robot-assisted Minimally Invasive Surgery (RMIS) pass through small incisions into the patient's body and interact with soft internal organs. The performance of traditional robotic manipulators such as the da Vinci Robotic System is limited due to insufficient flexibility of the manipulator and lack of haptic feedback. Modern surgical manipulators have taken inspiration from biology e.g. snakes or the octopus. In order for such soft and flexible arms to reconfigure itself and to control its pose with respect to organs as well as to provide haptic feedback to the surgeon, tactile sensors can be integrated with the robot's flexible structure. The work presented here takes inspiration from another area of biology: cucumber tendrils have shown to be ideal tactile sensors for the plant that they are associated with providing useful environmental information during the plant's growth. Incorporating the sensing principles of cucumber tendrils, we have created miniature sensing elements that can be distributed across the surface of soft manipulators to form a sensor network capable of acquire tactile information. Each sensing element is a retractable hemispherical tactile measuring applied pressure. The actual sensing principle chosen for each tactile makes use of optic fibres that transfer light signals modulated by the applied pressure from the sensing element to the proximal end of the robot arm. In this paper, we describe the design and structure of the sensor system, the results of an analysis using Finite Element Modeling in ABAQUS as well as sensor calibration and experimental results. Due to the simple structure of the proposed tactile sensor element, it is miniaturisable and suitable for MIS. An important contribution of this work is that the developed sensor system can be ”loosely” integrated with a soft arm effectively operating independently of the arm and without affecting the arm's motion during bending or elongation.

Food processing provides a lot of possibilities to apply robotics and automation. In this paper, we identify disordered and ordered states of discrete food products. The concept of Degree of Disarray is introduced. Food ordering processes such as vibratory feeders, multi-head weighers, pick and place operations are common automation in food industry to transfer products from a higher to a lower Degree of Disarray. Parts entropy is introduced to describe a product's individual state based on the symmetry categorisation. A macroscopic transitional model is presented which determines a subspace of the disordered arrangement using the eigenvectors of the largest eigenvalues of the covariance matrix. A projection into this created subspace follows. As soon as the disorder state in only one dimension is achieved, the point of disorder can be derived which finally transfers the objects into order. From here, a transformation to any order arrangement in any dimension is possible. This methodology is applied to pick and place operations and experiments are conducted.

This paper explores a new hybrid actuation principle combining pneumatic and tendon-driven actuators for a soft robotic manipulator. The fusion of these two actuation principles leads to an overall antagonistic actuation mechanism whereby pneumatic actuation opposes tendon actuation - a mechanism commonly found in animals where muscles can oppose each other to vary joint stiffness. We are taking especially inspiration from the octopus who belongs to the class of Cephalopoda; the octopus uses its longitudinal and transversal muscles in its arms to achieve varied motion patterns; activating both sets of muscles, the octopus can control the arm stiffness over a wide range. Our approach mimics this behavior and achieves comparable motion patterns, including bending, elongation and stiffening. The proposed method combines the advantages of tendon-driven and pneumatic actuated systems and goes beyond what current soft, flexible robots can achieve: because the new robot structure is effectively an inflatable, sleeve, it can be pumped up to its fully inflated volume and, also, completely deflated and shrunk. Since, in the deflated state, it comprises just its outer “skin” and tendons, the robot can be compressed to a very small size, many times smaller when compared to its fully-inflated state. In this paper, we describe the mechanical structure of the soft manipulator. Proof-of-concept experiments focus on the robot's ability to bend, to morph from completely shrunk to entirely inflated as well as to vary its stiffness.

In the wind turbine manufacturing industry, the bolt-nut joint tightening process is one of the core processes in the full production chain and concerned with assembling the hub body, the pitch system and the bearing unit. This operation is currently executed manually with the aid of different tools and gauges; the main disadvantages are a relatively high degree of variability and the necessity to repeat this task several times during a production run to achieve a satisfactory, final tightening torque within a specified angle range. Moreover, the bolt tightening process includes various uncertainties such as the presence of friction forces and the use of different bolt sizes with different stiffness values which make it highly nonlinear and uncertain resulting in a challenging control problem. To facilitate the development of an effective control strategy, we study the bolt tightening process and propose 4 tightening stages, namely, bolt-nut alignment, partial and full engagement and final bolt tightening. Based on the characteristics of each stage, a fuzzy controller is designed for each stage to realize the respective control objectives. A fuzzy error detector incorporating the knowledge of each stage is proposed for early error detection, making use of the input from a torque and encoder (angular position) sensor. Errors can be detected in each stage to interrupt the process and prevent any damage to the system.