This is an open access article under the CC BY license (http://creativecommons.org/licenses/BY/4.0/). Supplementary data for this article is available in the Loughborough Data Repository at doi: 10.17028/rd.lboro.5509528In 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

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.

Collaborative robots (Cobots) are essential in flexible automation solutions, enabling fast and easy reconfiguration to adapt to varying task requirements in dynamic environments. This requires the ability to safely handle different payloads with varying inertial parameters, which may not be known in advance. Hence, online identification of the payload's inertial parameters becomes essential for safe interactions, accurate path following, and stable grasping. Most existing methods require additional sensors, calibration procedures, or custom filtering, which increases the complexity and estimation time. In this letter, we propose a novel online identification method that employs a bagging ensemble machine learning approach to identify the payload inertial parameters without external sensors or additional filtering and calibration steps. The method uses available joint position, velocity, and torque measurements from the Cobot to train neural networks and decision trees as weak learners. The method is tested in simulation and validated using the Franka Emika Panda Cobot. The results showed that our method outperforms the state-of-the-art recursive least square methods reducing prediction errors by 75%–78% for mass and 49.5%–60% for the center of mass, while estimating accurate payload parameters within the first time step.

This paper was presented at MECHATRONICS 2016: 7th IFAC Symposium on Mechatronic Systems & 15th Mechatronics Forum International Conference \ud Loughborough University\ud 5th - 8th September 2016.In this paper, resistive flex sensors have been embedded at the strain limiting layer of soft\ud pneumatic actuators, in order to provide sensory feedback that can be utilised in predicting their bending\ud angle during actuation. An experimental setup was prepared to test the soft actuators under controllable\ud operating conditions, record the resulting sensory feedback, and synchronise this with the actual bending\ud angles measured using a developed image processing program. Regression analysis and neural networks\ud are two data-driven modelling techniques that were implemented and compared in this study, to evaluate\ud their ability in predicting the bending angle response of the tested soft actuators at different input\ud pressures and testing orientations. This serves as a step towards controlling this class of soft bending\ud actuators, using data-driven empirical models that lifts the need for complex analytical modelling and\ud material characterisation. The aim is to ultimately create a more controllable version of this class of soft\ud pneumatic actuators with embedded sensing capabilities, to act as compliant soft gripper fingers that can\ud be used in applications requiring both a ‘soft touch’ as well as more controllable object manipulation

Medical and agricultural robots that interact with living tissue or pick fruit require tactile and flexible sensors to minimise or eliminate damage. Until recently, research has focused on the development of robots made of rigid materials, such as metal or plastic. Due to their complex configuration, poor spatial adaptability and low flexibility, rigid robots are not fully applicable in some special environments such as limb rehabilitation, fragile objects gripping, human–machine interaction, and locomotion. All these should be done in an accurate and safe manner for them to be useful. However, the design and manufacture of soft robot parts that interact with living tissue or fragile objects is not as straightforward. Given that hyper-elasticity and conductivity are involved, conventional (subtractive) manufacturing can result in wasted materials (which are expensive), incompatible parts due to different physical properties, and high costs. In this work, additive manufacturing (3D printing) is used to produce a conductive, composite flexible sensor. Its electrical response was tested based on various physical conditions. Finite element analysis (FEA) was used to characterise its deformation and stress behaviour for optimisation to achieve functionality and durability. Also, a nonlinear regression model was developed for the sensor’s performance.