Structural Optimization of Adaptive Soft Fin Ray Fingers with Variable Stiffening Capability

Soft and adaptable grippers are desired for their ability to operate effectively in unstructured or dynamically changing environments, especially when interacting with delicate or deformable targets. However, utilizing soft bodies often comes at the expense of reduced carrying payload and limited performance in high-force applications. Hence, methods for achieving variable stiffness soft actuators are being investigated to broaden the applications of soft grippers. This paper investigates the structural optimization of adaptive soft fingers based on the Fin Ray® effect (Soft Fin Ray), featuring a passive stiffening mechanism that is enabled via layer jamming between deforming flexible ribs. A finite element model of the proposed Soft Fin Ray structure is developed and experimentally validated, with the aim of enhancing the layer jamming behavior for better grasping performance. The results showed that through structural optimization, initial contact forces before jamming can be minimized and final contact forces after jamming can be significantly enhanced, without downgrading the desired passive adaptation to objects. Thus, applications for Soft Fin Ray fingers can range from adaptive delicate grasping to high-force manipulation tasks

Directly Printable Flexible Strain Sensors for Bending and Contact Feedback of Soft Actuators

This paper presents a fully printable sensorized bending actuator that can be calibrated to provide reliable bending feedback and simple contact detection. A soft bending actuator following a pleated morphology, as well as a flexible resistive strain sensor, were directly 3D printed using easily accessible FDM printer hardware with a dual-extrusion tool head. The flexible sensor was directly welded to the bending actuator’s body and systematically tested to characterize and evaluate its response under variable input pressure. A signal conditioning circuit was developed to enhance the quality of the sensory feedback, and flexible conductive threads were used for wiring. The sensorized actuator’s response was then calibrated using a vision system to convert the sensory readings to real bending angle values. The empirical relationship was derived using linear regression and validated at untrained input conditions to evaluate its accuracy. Furthermore, the sensorized actuator was tested in a constrained setup that prevents bending, to evaluate the potential of using the same sensor for simple contact detection by comparing the constrained and free-bending responses at the same input pressures. The results of this work demonstrated how a dual-extrusion FDM printing process can be tuned to directly print highly customizable flexible strain sensors that were able to provide reliable bending feedback and basic contact detection. The addition of such sensing capability to bending actuators enhances their functionality and reliability for applications such as controlled soft grasping, flexible wearables, and haptic devices

Data-driven bending angle prediction of soft pneumatic actuators with embedded flex sensors

In this paper, resistive flex sensors have been embedded at the strain limiting layer of soft pneumatic actuators, in order to provide sensory feedback that can be utilised in predicting their bending angle during actuation. An experimental setup was prepared to test the soft actuators under controllable operating conditions, record the resulting sensory feedback, and synchronise this with the actual bending angles measured using a developed image processing program. Regression analysis and neural networks are two data-driven modelling techniques that were implemented and compared in this study, to evaluate their ability in predicting the bending angle response of the tested soft actuators at different input pressures and testing orientations. This serves as a step towards controlling this class of soft bending actuators, using data-driven empirical models that lifts the need for complex analytical modelling and material characterisation. The aim is to ultimately create a more controllable version of this class of soft pneumatic actuators with embedded sensing capabilities, to act as compliant soft gripper fingers that can be used in applications requiring both a ‘soft touch’ as well as more controllable object manipulation

Bending angle prediction and control of soft pneumatic actuators with embedded flex sensors - a data-driven approach

In this paper, a purely data-driven modelling approach is presented for predicting and controlling the free bending angle response of a typical soft pneumatic actuator (SPA), embedded with a resistive flex sensor. An experimental setup was constructed to test the SPA at different input pressure values and orientations, while recording the resulting feedback from the embedded flex sensor and on-board pressure sensor. A calibrated high speed camera captures image frames during the actuation, which are then analysed using an image processing program to calculate the actual bending angle and synchronise it with the recorded sensory feedback. Empirical models were derived based on the generated experimental data using two common data-driven modelling techniques; regression analysis and artificial neural networks. Both techniques were validated using a new dataset at untrained operating conditions to evaluate their prediction accuracy. Furthermore, the derived empirical model was used as part of a closed-loop PID controller to estimate and control the bending angle of the tested SPA based on the real-time sensory feedback generated. The tuned PID controller allowed the bending SPA to accurately follow stepped and sinusoidal reference signals, even in the presence of pressure leaks in the pneumatic supply. This work demonstrates how purely data-driven models can be effectively used in controlling the bending of SPAs under different operating conditions, avoiding the need for complex analytical modelling and material characterisation. Ultimately, the aim is to create more controllable soft grippers based on such SPAs with embedded sensing capabilities, to be used in applications requiring both a ‘soft touch’ as well as a more controllable object manipulation

Bending angle prediction and control of soft pneumatic actuators with embedded flex sensors: a data-driven approach

In this paper, a purely data-driven modelling approach is presented for predicting and controlling the free bending angle response of a typical soft pneumatic actuator (SPA), embedded with a resistive flex sensor. An experimental setup was constructed to test the SPA at different input pressure values and orientations, while recording the resulting feedback from the embedded flex sensor and on-board pressure sensor. A calibrated high speed camera captures image frames during the actuation, which are then analysed using an image processing program to calculate the actual bending angle and synchronise it with the recorded sensory feedback. Empirical models were derived based on the generated experimental data using two common data-driven modelling techniques; regression analysis and artificial neural networks. Both techniques were validated using a new dataset at untrained operating conditions to evaluate their prediction accuracy. Furthermore, the derived empirical model was used as part of a closed-loop PID controller to estimate and control the bending angle of the tested SPA based on the real-time sensory feedback generated. The tuned PID controller allowed the bending SPA to accurately follow stepped and sinusoidal reference signals, even in the presence of pressure leaks in the pneumatic supply. This work demonstrates how purely data-driven models can be effectively used in controlling the bending of SPAs under different operating conditions, avoiding the need for complex analytical modelling and material characterisation. Ultimately, the aim is to create more controllable soft grippers based on such SPAs with embedded sensing capabilities, to be used in applications requiring both a ‘soft touch’ as well as a more controllable object manipulation

Towards an automated masking process: A model-based approach

The masking of aircraft engine parts, such as turbine blades, is a major bottleneck for the aerospace industry. The process is often carried out manually in multiple stages of coating and curing, which requires extensive time and introduces variations in the masking quality. This article investigates the automation of the masking process utilising the well-established time–pressure dispensing process for controlled maskant dispensing and a robotic manipulator for accurate part handling. A mathematical model for the time–pressure dispensing process was derived, extending previous models from the literature by incorporating the robot velocity for controlled masking line width. An experiment was designed, based on the theoretical analysis of the dispensing process, to derive an empirical model from the generated data that incorporate the losses that are otherwise difficult to model mathematically. The model was validated under new input conditions to demonstrate the feasibility of the proposed approach and the masking accuracy using the derived model

Towards a more controllable sensorised soft gripper: a data-driven approach

Towards a more controllable sensorised soft gripper: a data-driven approach</p

Experimental data - modelling the bending response of soft pneumatic actuators

File contains the experimental data generated from testing the soft bending actuators with embedded flex sensor at varying input conditions. The data was split and used for training and testing an empirical model describing the bending response of the soft actuators, for prediction and control purposes.<div><br></div><div>The related article is available in Loughborough University's Institutional Repository.</div

Experimental data - characterising a printable flexible strain sensor

This file contains the experimental data generated from testing a flexible strain sensor, when integrated to a pneumatic bending actuator, at different input pressures. Both the flexible sensor and bending actuator were directly printed using an FDM printing process.<div><br><div>The data includes:</div><div>- Sheet 1 (Repeatability analysis): testing the sensorized actuator repeatedly at variable input pressures of 12, 14, 16, and 18 psi.</div><div><div>- Sheet 2 (Bending calibration): 501 samples from testing the sensorised actuator at an input pressure of 18 psi for training, and another 550 samples at input pressures of 16 and 20 psi for validation. Each sample is an array containing the readings from the printed sensor and onboard pressure sensor, with the corresponding bending angle from the vision system.</div><div><br><div>The related article is available in Loughborough University's Institutional Repository.</div></div></div></div

Design and Characterisation of a Variable-Stiffness Soft Actuator Based on Tendon Twisting

The field of soft robotics aims to address the challenges faced by traditional rigid robots in less structured and dynamic environments that require more adaptive interactions. Taking inspiration from biological organisms’ such as octopus tentacles and elephant trunks, soft robots commonly use elastic materials and novel actuation methods to mimic the continuous deformation of their mostly soft bodies. While current robotic manipulators, such as those used in the DaVinci surgical robot, have seen use in precise minimally invasive surgeries applications, the capability of soft robotics to provide a greater degree of flexibility and inherently safe interactions shows great promise that motivates further study. Nevertheless, introducing softness consequently opens new challenges in achieving accurate positional control and sufficient force generation often required for manipulation tasks. In this paper, the feasibility of a stiffening mechanism based on tendon-twisting is investigated, as an alternative stiffening mechanism for soft actuators that can be easily scaled as needed based on tendon size, material properties, and arrangements, while offering simple means of controlling a gradual increase in stiffening during operation